# Feasibility of using Sentinel-2 images to detect decline in kiwifruit orchards

Marianne Avignon, Maxime Garnault, Claire Marsden, Adeline Gachein, Lionel Alletto, Yvan Capowiez, Claire Marais-Sicre

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Marianne Avignon, Maxime Garnault, Claire Marsden, Adeline Gachein, Lionel Alletto, et al. Feasibility of using Sentinel-2 images to detect decline in kiwifruit orchards. International Journal of Applied Earth Observation and Geoinformation, 2025, 144, pp.104846. 10.1016/j.jag.2025.104846. hal-05269532

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# Feasibility of using Sentinel-2 images to detect decline in kiwifruit orchards

Marianne Avignon<sup>a,b,*</sup>, Maxime Garnault<sup>c</sup>, Claire Marsden<sup>d ID</sup>, Adeline Gachein<sup>b</sup>,
Lionel Alletto<sup>c</sup>, Yvan Capowiez<sup>a</sup>, Claire Marais-Sicre<sup>e</sup>

## ARTICLE INFO

**Keywords:**

Orchard mapping

Vegetation indices

Random forest

Model comparison

## ABSTRACT

Tree decline affects many perennial orchards with potentially heavy impacts on production. In five years 3 % of national production of kiwifruit orchards were lost in France, leading to economic issues and jeopardizing the value chain. Some specific management practices could help to mitigate kiwifruit decline, but rapid and simple tools are needed to assess the development of the decline in response to these practices. As kiwifruit decline is characterized by a low-vigor canopy along with changes in canopy color and density, Sentinel-2 images were used to detect vine decline over large areas. We first selected 28 orchards, with varying characteristics (e.g. row grass cover, hail protection nets, fertilization practices) and characterized each vine (14000 in total) according to its agronomic status (i.e. vigor and presence of decline symptoms). Sentinel-2 images are made up of 10 x 10 m pixels. To classify them we fitted two models using a random forest procedure. The spectral model (SM) used only spectral inputs, while the agronomic and spectral model (ASM) used both spectral inputs from satellite images and agronomic inputs obtained from orchard characteristics. Spectral inputs included raw spectral bands (e.g., red, near-infrared, green) and vegetation indices (e.g., NDVI, GNDVI). Results show that vigorous and dead areas were well detected (more than 80 % of correct predictions). Declining areas were correctly detected when patches of decline were larger than 500 m². Mixed pixels containing vines with different agronomic status were poorly predicted. Accuracy improves when agronomic information, such as soil texture, is incorporated into the model. Both models (SM and ASM) could enable growers to adjust their practices in real time according to the health status of their vines.

## 1. Introduction

Tree decline is a widespread phenomenon that affects tissues, foliage, flowers and even shoots resulting in a permanently altered, scorched appearance (Delatour, 1990; Office québécois de la langue française, 1985). While forest decline is almost always associated with periods of water deficit (Allen et al., 2010; Delatour, 1990), in kiwifruit orchards, decline is mainly linked with poor soil physical conditions, often accompanied by or following waterlogging events (Reid et al., 1992; Sofo et al., 2022). Such waterlogging can be caused by poor irrigation management, intense rainfall, or flooding events, and is aggravated by soil compaction (Donati et al., 2020; Hughes and Wilde, 1989; Sofo et al., 2024). The first appearance of kiwifruit vine decline was observed in New Zealand in the late 1980s after heavy rains

associated to the Cyclone Bola. On poorly drained soils, only unhealthy vines were found. Their root length was halved compared to situations with good water infiltration below 25 cm deep (Hughes and Wilde, 1989). Similar observations were made 25 years later in Italy where orchards irrigated with flood irrigation showed a decline five times greater than those irrigated only with microjets (Tosi et al., 2015). Today few orchards are declining in New Zealand, but in Italy more than 15 % of the country's orchards are showing decline symptoms, representing more than 4500 ha (Donati et al., 2020; Sofo et al., 2024). In France, kiwifruit decline was first reported after heavy rains in November 2019, which were accompanied by flooding events (Agusti, 2020). For example, in one month, 800 mm of rain fell in the Adour valley (a large kiwifruit production zone, in the southwest of France), while the average annual precipitation for the region is 1220 mm.

<footnote><sup>a</sup> UMR 1114 EMMAH, INRAE, Université d'Avignon, Avignon, France</footnote>

<footnote><sup>b</sup> Bureau national Interprofessionnel du Kiwi (BIK), Toulouse, France</footnote>

<footnote><sup>c</sup> Université de Toulouse, INRAE, UMR AGIR, INPT, El PURPAN, Castanet-Tolosan, France</footnote>

<footnote><sup>d</sup> Eco&Sols, Univ Montpellier, CIRAD, INRAE, IRD, Institut Agro Montpellier, Montpellier, France</footnote>

<footnote><sup>e</sup> Centre d'Etudes Spatiales de la BIOsphère (CESBIO), Université de Toulouse, CNES/CNRS/INRAE/IRD/UPS, Toulouse, France</footnote>

<footnote>* Corresponding author at: UMR 1114 EMMAH, INRAE, Université d'Avignon, Avignon, France.
E-mail address: marianne.avignon@hotmail.fr (M. Avignon).</footnote>
Climate projections for the coming decades suggest an increase in intense climatic events, including heavy autumn and winter precipitations with a 10 % increase in winter in the Adour valley by 2050 (Météo France, 2024). There may be limited options to directly mitigate effects of climate change, but if kiwifruit decline stems from prolonged exposure to extreme climatic conditions accompanied by inappropriate cultivation practices, proactive measures adopted by growers such as precision irrigation or cane pruning could prevent orchard decline before it becomes irreversible (Sofo et al., 2024a).

The health status of the canopy reflects the damage suffered by the root system, albeit to a lesser extent (Buwalda and Hutton, 1988; Savian et al., 2020a). Root growth depends on resource availability and competes with shoot and fruit growth. In anoxic conditions, kiwifruit root systems deteriorate quickly (Smith et al., 1989) and even if they are not damaged, the remaining roots are only located in the first 5 to 10 cm of the soil (Donati et al., 2020; Tacconi et al., 2015). In summer, kiwifruit vine decline symptoms generally appear after heat peaks even if they are the consequences of impaired root development that occurred in winter and spring. These first aerial symptoms are the progressive desiccation of shoots and leaves from the margin to their center (Tosi et al., 2015). They are often accompanied by the sudden and irreversible wilting and defoliation of the plant (Savian et al., 2020a). Given the rapid progression of kiwifruit decline (Reid et al., 1992), fast identification of orchards at risk or of declining areas within orchards is crucial. However, carrying out a tree-by-tree or plot-by-plot diagnosis is time consuming requiring approximately 1.5 h of labor per hectare (Avignon et al., 2024; Wagner et al., 2018). There is a need to develop a tool capable of rapidly and accurately identifying healthy, declining, and dead areas on a large scale and based on canopy health.

Remote sensing techniques, already employed in viticulture, forestry, and orchard groves, are able to distinguish canopy changes, tree water status or tree health (Chéret et al., 2019; González-Gómez et al., 2022; Lemaire, 2018; Merot et al., 2023) and deserve attention as a potential solution for the monitoring of kiwifruit vine decline. Remote sensing can combine and merge thermal, hyperspectral and multispectral data. In kiwifruit, a strong relationship was established between the bacterial canker caused by Pseudomonas syringae pv. Actinidiae (PSA) and thermal data at the orchard scale (Taylor et al., 2014). PSA is a bacterium capable of systemic invasion of kiwifruit vines, leading to symptoms in different organs, significant harvest losses and rapid plant death in many cases (Donati et al., 2020). Another study in kiwifruit orchards showed a strong correlation between vine vigor and multispectral data (Savian et al., 2020b). Most of these images were caught by unmanned aerial vehicles (UAV). UAVs provide high-resolution images with a spatial resolution of 10 cm or less but are limited for regional monitoring and require extensive image processing. Compared to UAVs, the Sentinel-2 satellite constellation (Sentinel-2A and Sentinel-2B) provides free medium resolution data (10 m) every 5 days, with 13 spectral bands ranging from 400 nm to 2300 nm. To our knowledge, such data have never been used for the monitoring of kiwifruit decline, despite their multiple advantages. While red edge spectral bands are the most sensitive to plant activity (Chéret et al., 2019), declining trees have a higher reflectance for blue, green and red compared to healthy vines (Mouret et al., 2024). More generally, by combining different spectral bands, vegetation indices (VI) can be computed to assess plant health (Lamb et al., 2001). In viticulture the four most widely used indicators are the Normalized Difference Vegetation Index (NDVI), green NDVI (GNDVI), simple ratio (SR) and modified simple ratio (MSR) (Giovos et al., 2021). The photosynthetic vigor ratio (PVR) has been used to monitor and map PSA infections in kiwifruit orchards (Taylor et al., 2014). However, the reflectance perceived by satellites is not only influenced by vine health but can also be influenced by row and inter-row grass cover that can vary seasonally (Almeida-Ñauñay et al., 2023; Liu et al., 2023). A Sentinel-2 image pixel integrates the information from the canopy of up to 14 different kiwifruit vines and also from grass cover, it is therefore always a so-called "mixed pixel". It is up

to now unknown if such mixed pixels provide sufficient information to allow the detection of vine decline symptoms. Moreover, as shown by Jiang et al. (2022), the addition of some agronomic information to spectral data could increase the quality of the decline detection because difference in shading due to the presence of hail nets, grass development (in relation to row and inter-row grass management) and fertilization could influence the reflectance perceived by Sentinel-2 satellites. Furthermore, the spatial structure of decline symptoms may interfere with decline detection when using 10 m resolution images: a diffuse decline, with only a small number of affected vines per pixel, may be more difficult to detect than a patchy structure of vine decline, with a larger number of contiguous diseased vines. However, no study has evaluated the effect of the spatial configuration of declining vines on detection efficiency.

This study aims to assess the feasibility of using Sentinel-2 images to detect kiwifruit vine decline, either by using satellite images only or by adding knowledge about orchard characteristics. We first studied kiwifruit decline, at the vine level, in 28 commercial orchards. We then assessed the efficiency of models built with a random forest (RF) procedure. These models were evaluated in terms of accuracy, inter-reliability and predictive performances. The first model is purely spectral (SM) and based exclusively on Sentinel-2 data. This model is based on open access images and requires no additional information other than kiwifruit orchard location. The second model requires additional qualitative and quantitative data, on soil texture, soil organic matter, presence or absence of hail protection nets, grass management and type of fertilization (organic, mineral or both). These data are easily available to most kiwifruit producers. Based on these considerations, the specific objectives of this study are (i) to compare the performance of both models in estimating the health status of vines in the orchards; (ii) to assess the importance of additional variables in the ASM model; (iii) to determine whether diffuse vine decline can be detected using Sentinel-2 images with a 10 m spatial resolution, or if a minimum patch size (consecutive pixels with the same class) is required. We hypothesized that it would be possible to detect patches affected by kiwifruit vine decline with a purely spectral model, but that the ASM would show higher accuracy by accounting for confounding factors such as the presence of hail nets. We also hypothesized that a minimum patch size would be required for the detection of decline with Sentinel-2 data.

## 2. Material and methods

### 2.1. Studied area and selected orchards

The studied area is located in Southwest France, with 20 orchards in the Adour valley and 8 in the Garonne valley (Fig. 1), i.e. the two main French kiwifruit production zones, accounting for a total of about 3200 ha in 2022 which represents 81 % of the national production area (Colas and Boudour, 2023). The 28 kiwifruit orchards, ranging from 0.35 to 1.80 ha, covered 40 ha and are characterized by diverse pedoclimatic conditions and agronomic practices. These orchards were chosen thanks to snowball sampling (Goodman, 1961) to include a spectrum of cultural practices commonly observed in kiwifruit cultivation, including variations in row grass cover, presence or absence of hail protection nets, fertilization practices and planting density. They also encompassed varying levels of decline (Table 1). Twenty-three orchards were located on river alluvium, and 19 on more silty soils. Nineteen orchards had permanent row grass cover, and 8 had hail protection nets. Only 4 orchards had both row grass cover and hail protection nets. All orchards had permanent inter row grass cover.

The Adour valley is characterized by a temperate climate. A 30-year time series showed that the Atlantic Ocean brings mildness and humidity, but also a high degree of weather variability. The average rainfall is 1250 mm per year and average annual temperature is 11.8 °C. Warm days (> 30 °C) are quickly interrupted by thunderstorms, and in winter, days with frost below -5°C are rare. In the Garonne valley, the
<figure><img src="image_3.png" /><figcaption>**Fig. 1.** Location of the 28 studied orchards. 20 of them are in the Adour valley, characterized by an oceanic climate with more than 1250 mm of annual precipitation on average and 8 orchards are in the Garonne valley with an annual precipitation of 750 to 950 mm (Fick and Hijmans, 2017).</figcaption></figure>

average annual temperature is above 13 °C. There is a high number of warm days, and days with frost below -5°C are also rare. Rainfall, which is not very abundant on an annual basis (less than 800 mm), is more frequent in winter than in summer (Joly et al., 2010).

## 2.2. Vine characterization

In late September and beginning of October 2023, the 14,000 vines of the 28 orchards were classified during a field survey. Each vine was classified according to its visual vigor and the presence or absence of decline symptoms (Fig. 2): wilting, browning, or defoliation (Nageleisen, 2009). Five agronomic statuses were defined: (i) vigorous vines with a well-developed, dense canopy and indeterminate growth canes, with an absence of symptoms; (ii) low vigor vines with a poorly developed canopy, and determinate growth canes, for which it is possible to see the sky through the canopy; (iii) diseased vines, with wilting leaves, necrosis, and advanced defoliation; (iv) almost dead, dead, or missing vines; and (v) recently planted vines (trunk diameter under 6 cm) which have a very little canopy. Each vine was then spatially positioned on aerial photographs captured by the French National Geographic Institute (IGN) in July 2021, using Qgis 3.16 software.

## 2.3. Sentinel-2 images

Sentinel-2 is a constellation of two satellites, Sentinel-2A and Sentinel-2B, on the same orbit and phased at 180° from each other. It aims at monitoring land surface variability with a high revisiting frequency (10 days at the equator with one satellite, and 5 days with two satellites if cloud condition allows it). Sentinel-2A was launched in 2015 and Sentinel-2B in 2017. Since then, they have provided images with 13 spectral bands spanning from blue to near infrared (NIR). Three level 2 Sentinel-2 images were downloaded from THEIA which is a French

open-source catalog (https://www.theia-land.fr/) where images are ortho-rectified to remove the influence of the atmosphere and are delivered as reflectance. The first image was for the Adour valley, and the two others for the Garonne valley which extended over a wider geographical area. One image is 110 x 110 km², and Garonne valley orchards covered 150 x 60 km². They were captured by satellites in the same weeks as the vine characterization within the orchards (i.e. on September 20, for the Adour valley and October 2, for the Garonne valley.) These images are accompanied by a cloud mask. The cloud coverage was 10 % in the Adour image, and 1 % and 5 % in the Garonne images. We visually verified the absence of clouds over the orchards. Green (λ = 560 nm), red (λ = 665 nm) and NIR (λ = 842 nm) spectral bands obtained from Sentinel-2 data were used to calculate various vegetation indices (VI, Table 2).

## 2.4. Joining field and Sentinel-2 satellite data

To be able to compare data acquired in orchards (vine agronomic status) with satellite data (spectral bands), we first needed to assign each vine to a 10 x 10 m pixel of the Sentinel-2 images. A vine was assigned to a pixel if its distance from the pixel centroid in the Sentinel-2 image was less than 7 m, i.e. within the circumcircle of the pixel. In total, vines were distributed across 3977 pixels. All resulting pixels did not contain the same number of vines. Some pixels contained up to 14 vines, while others contained only a few, depending on planting density, the fact that some pixels are not entirely within the orchard, and the orientation of the orchard relative to the image grid orientation. To ensure that the pixel information was mainly related to vine canopies, we excluded the pixels containing an insufficient number of vines, as recommended by Sozzi et al. (2020). To do this we assessed the distribution of number of vines per pixel in each orchard, and selected only the pixels strictly above the first decile. This 1st decile varied between 2 and 10 vines per
<caption>**Table 1**</caption>

Description of the 28 orchards with their size, the number of conserved pixels, the different agronomic characteristics (row grass cover, hail nets, fertilization, soil texture, soil organic matter) and the agronomic status of vines present in the plot.

<table><thead><tr><th>Valley</th><th>Surface (ha)</th><th>Number of pixels</th><th>Row grass cover</th><th>Hail nets</th><th>Fertilization<sup>1</sup></th><th>Soil texture<sup>2</sup></th><th>Soil organic matter (%)</th><th>Vigorous vines</th><th>Low vigor vines</th><th>Diseased vines</th><th>Dead or absent vines</th><th>Young vines</th></tr></thead><tbody><tr><td>Adour</td><td>1.05</td><td>98</td><td>No</td><td>No</td><td>both</td><td>Scs</td><td>3.1</td><td>445</td><td>11</td><td>25</td><td>70</td><td>20</td></tr><tr><td>Adour</td><td>1.00</td><td>89</td><td>No</td><td>Yes</td><td>both</td><td>Scs</td><td>2.5</td><td>448</td><td>2</td><td>15</td><td>34</td><td>17</td></tr><tr><td>Adour</td><td>1.05</td><td>100</td><td>Yes</td><td>No</td><td>both</td><td>Sc</td><td>2.4</td><td>97</td><td>197</td><td>65</td><td>196</td><td>11</td></tr><tr><td>Adour</td><td>0.55</td><td>51</td><td>No</td><td>No</td><td>both</td><td>Sc</td><td>3.5</td><td>51</td><td>84</td><td>39</td><td>97</td><td>17</td></tr><tr><td>Adour</td><td>1.00</td><td>95</td><td>Yes</td><td>No</td><td>both</td><td>Ssc</td><td>2.2</td><td>126</td><td>8</td><td>2</td><td>15</td><td>5</td></tr><tr><td>Adour</td><td>0.35</td><td>35</td><td>Yes</td><td>No</td><td>both</td><td>Ssc</td><td>2.2</td><td>145</td><td>85</td><td>13</td><td>132</td><td>125</td></tr><tr><td>Adour</td><td>0.50</td><td>43</td><td>Yes</td><td>No</td><td>both</td><td>Css</td><td>2.2</td><td>150</td><td>27</td><td>25</td><td>60</td><td>2</td></tr><tr><td>Adour</td><td>0.50</td><td>47</td><td>Yes</td><td>No</td><td>both</td><td>Sc</td><td>2.3</td><td>94</td><td>43</td><td>40</td><td>64</td><td>1</td></tr><tr><td>Adour</td><td>0.55</td><td>52</td><td>Yes</td><td>Yes</td><td>both</td><td>Sc</td><td>2.3</td><td>248</td><td>13</td><td>2</td><td>8</td><td>0</td></tr><tr><td>Adour</td><td>0.60</td><td>59</td><td>No</td><td>No</td><td>both</td><td>Ssc</td><td>2.2</td><td>180</td><td>45</td><td>13</td><td>6</td><td>33</td></tr><tr><td>Adour</td><td>0.60</td><td>53</td><td>Yes</td><td>Yes</td><td>organic</td><td>Sc</td><td>1.5</td><td>236</td><td>4</td><td>1</td><td>18</td><td>28</td></tr><tr><td>Adour</td><td>0.80</td><td>74</td><td>Yes</td><td>No</td><td>both</td><td>S</td><td>2.0</td><td>237</td><td>48</td><td>50</td><td>5</td><td>12</td></tr><tr><td>Adour</td><td>0.65</td><td>57</td><td>Yes</td><td>No</td><td>both</td><td>Sac</td><td>2.0</td><td>311</td><td>9</td><td>0</td><td>2</td><td>7</td></tr><tr><td>Adour</td><td>1.40</td><td>127</td><td>Yes</td><td>No</td><td>organic</td><td>S</td><td>1.9</td><td>347</td><td>102</td><td>82</td><td>80</td><td>27</td></tr><tr><td>Adour</td><td>1.80</td><td>162</td><td>Yes</td><td>No</td><td>organic</td><td>S</td><td>1.9</td><td>538</td><td>143</td><td>75</td><td>61</td><td>30</td></tr><tr><td>Adour</td><td>0.90</td><td>88</td><td>No</td><td>No</td><td>organic</td><td>Ssa</td><td>2.9</td><td>441</td><td>0</td><td>0</td><td>1</td><td>2</td></tr><tr><td>Adour</td><td>1.05</td><td>97</td><td>No</td><td>Yes</td><td>mineral</td><td>Css</td><td>2.2</td><td>8</td><td>8</td><td>0</td><td>488</td><td>13</td></tr><tr><td>Adour</td><td>1.20</td><td>112</td><td>Yes</td><td>No</td><td>both</td><td>Sc</td><td>2.3</td><td>211</td><td>91</td><td>66</td><td>81</td><td>23</td></tr><tr><td>Adour</td><td>1.40</td><td>128</td><td>No</td><td>No</td><td>both</td><td>S</td><td>2.3</td><td>20</td><td>48</td><td>98</td><td>445</td><td>21</td></tr><tr><td>Adour</td><td>1.00</td><td>91</td><td>Yes</td><td>No</td><td>both</td><td>Ssc</td><td>1.8</td><td>237</td><td>76</td><td>10</td><td>111</td><td>69</td></tr><tr><td>Garonne</td><td>1.50</td><td>137</td><td>Yes</td><td>No</td><td>both</td><td>Sac</td><td>1.2</td><td>60</td><td>149</td><td>114</td><td>264</td><td>45</td></tr><tr><td>Garonne</td><td>0.95</td><td>87</td><td>Yes</td><td>No</td><td>both</td><td>Ssa</td><td>1.4</td><td>448</td><td>11</td><td>92</td><td>53</td><td>20</td></tr><tr><td>Garonne</td><td>0.95</td><td>88</td><td>No</td><td>Yes</td><td>both</td><td>Sa</td><td>1.5</td><td>487</td><td>25</td><td>93</td><td>21</td><td>32</td></tr><tr><td>Garonne</td><td>1.10</td><td>98</td><td>Yes</td><td>No</td><td>organic</td><td>Ssc</td><td>1.8</td><td>547</td><td>50</td><td>181</td><td>58</td><td>19</td></tr><tr><td>Garonne</td><td>1.05</td><td>91</td><td>Yes</td><td>No</td><td>organic</td><td>S</td><td>2.7</td><td>413</td><td>48</td><td>67</td><td>12</td><td>39</td></tr><tr><td>Garonne</td><td>0.85</td><td>76</td><td>No</td><td>Yes</td><td>both</td><td>Sc</td><td>1.8</td><td>202</td><td>69</td><td>37</td><td>92</td><td>20</td></tr><tr><td>Garonne</td><td>1.65</td><td>149</td><td>Yes</td><td>Yes</td><td>organic</td><td>Ss</td><td>1.3</td><td>151</td><td>153</td><td>46</td><td>440</td><td>8</td></tr><tr><td>Garonne</td><td>1.55</td><td>141</td><td>Yes</td><td>Yes</td><td>organic</td><td>Css</td><td>1.7</td><td>146</td><td>157</td><td>0</td><td>451</td><td>11</td></tr></tbody></table>

<figure><img src="image_4.png" /><figcaption>**Fig. 2.** Classification of the agronomic statuses of vines according to their vigor and presence of symptoms.</figcaption></figure>

pixel according to the orchard. In this way, approximately 35 % of the pixels were excluded and 2525 pixels remained.

Assigning a unique agronomic status to a pixel was not straightforward, as pixels often contained vines with different agronomic statuses. To summarize the information within each pixel we used a pixel

classification. Each pixel was assigned to a class using the k-means method (“factoextra” package, Kassambara and Mundt, 2020) to gather similar pixels in the same class. The k-means procedure was based on both (i) the total number of vines and (ii) the number of vines from each agronomic status, within each pixel. The optimal number of classes was

<caption>**Table 2**</caption>

Vegetation indices used in random forest models, calculation formulas and references.

<table><thead><tr><th>Indicators</th><th>Abbreviation</th><th>Formula</th><th>Reference</th></tr></thead><tbody><tr><td>Normalized difference vegetative index</td><td>NDVI</td><td>(NIR - Red) / (NIR + Red)</td><td>Rouse et al. (1974)</td></tr><tr><td>Green NDVI</td><td>GNDVI</td><td>(NIR - Green) / (NIR + Green)</td><td>Gitelson et al. (1996)</td></tr><tr><td>Simple ratio</td><td>SR</td><td>NIR / Red</td><td>Rouse et al. (1974)</td></tr><tr><td>Modified simple ratio</td><td>MSR</td><td>((NIR / Red) - 1) / √((NIR / Red) + 1)</td><td>Chen (1996)</td></tr><tr><td>Photosynthetic vigor ratio</td><td>PVR</td><td>Green/Red</td><td>Taylor et al. (2014), Metternicht (2003)</td></tr></tbody></table>

<footnote n="1">¹Both mean organic and mineral fertilization</footnote>

<footnote n="2">²Soil textures are described as follows: clayey silty sandy soil (Scs), clayey silt (Sc), sandy clayey silt (Ssc), silty sandy clay (CSS), silty (S), sandy (Sa), sandy silt (Ssa), clayey sand (Sac), silty sand (Ss).</footnote>
<figure><img src="image_5.png" /><figcaption>Fig. 3. Illustration of the data transformation from vine agronomic statuses (left) to their combination into pixel classes (right).</figcaption></figure>

determined using the elbow method (Thorndike, 1953), by plotting the sum of squared distances within classes against the number of classes. Pixels were split across four ordered classes (Fig. 3). In the next part of our analysis, models were designed to predict pixel class from Sentinel-2 data, and not the agronomic status of individual vines, which was not possible because of the resolution of images.

Two kinds of inputs were used as explanatory variables for the different pixel classes. Spectral inputs were based exclusively on satellite data. They included both spectral bands (green, red and NIR) and the calculated vegetation indices (NDVI, GNDVI, SR, MSR, PVR). Agronomic inputs included row vegetation cover (presence or absence), presence or absence of hail protection nets, fertilization (organic, mineral or both) and soil organic matter content (Table 1). It also included soil texture class from the French GEPPA classification (for Study Group of Applied Pedological Issues, in French) which has become the national standard classification as it is recognized as the only one resulting from a collective effort. It was elaborated by a group of pedologists based on the tactile sensation felt on samples from all over France. It is made up of 17 classes. These agronomic characteristics were assessed during the field survey and soil data was retrieved from the producers.

## 2.5. Data analysis

In this paper two approaches were considered. First, the “learning approach” analyzes the full dataset to assess class detection performance and identify key explanatory variables. Then, the “cross-validation approach” splits the dataset into 80 % for calibration and 20 % for validation and aims to assess the model’s predictive performance on new orchards.

### Learning approach.

The first approach aimed to classify pixels according to spectral inputs or spectral and agronomic inputs. The spectral model (SM) used only spectral inputs, while the agronomic and spectral model (ASM) used both. Both models were fitted using Random Forests (RF) (Breiman, 2001; Liaw and Wiener, 2002) with vine classification results as response variables. The RF ensemble classifier gives good results on agricultural surfaces. In addition, it has some of the advantages described in Belgiu and Drăguț (2016), such as low sensitivity to feature selection, easy parameterization and low computational time. RF is obtained by training multiple decision trees on mixed subsets of the initial dataset. This classification algorithm uses two parameters: the number of variables to randomly sample as candidates for each split (mtry) and the number of decision trees (ntree). Optimized values for both parameters were chosen with “caret” package (Kuhn, 2008); in particular mtry was set to 8 for SM and 2 for ASM, ntree was set at 500 for both SM and ASM.

For each model, Gini’s impurity index (Breiman, 1998) was used to determine variable importance, which indicates which explanatory variables contributed the most to class prediction. Variable importance was then expressed as a percentage of the maximum observed importance value. The robustness of the generated forests was also tested. The robustness indicators were the accuracy (i.e. the fraction of correct predictions), and the F1-score (Eq.1) (Powers, 2008; Sasaki, 2007), which varies between 0 and 1. The F1-score is the harmonic mean of two metrics: precision (Eq.2) and recall (Eq.3). It was calculated for each class.

$$F1 - score (class = a) = \frac{2 \times Precision (class = a) \times Recall (class = a)}{Precision (class = a) + Recall (class = a)} \quad (1)$$
Precision (class = a)

$$= \frac{\text{True positive (class = a)}}{\text{True positive (class = a)} + \text{False positive (class = a)}} \quad (2)$$

Recall (class = a)

$$= \frac{\text{True positive (class = a)}}{\text{True positive (class = a)} + \text{False negative (class = a)}} \quad (3)$$

The weighted kappa (Kw) was the last indicator (Eq. (4), Fleiss and Cohen, 1973). It is used for ordinal categories, because mispredictions by more than one class are considered worse than by closer classes (Agresti, 2008). We used quadratic weighting, where weights are given by Eq. (5). The weighted kappa is interpreted with Landis and Koch (1977) criteria.

$$K_w = 1 - \frac{\sum w_{ij} \text{foij}}{\sum w_{ij} \text{feij}} \quad \begin{array}{l} \text{with } f_{ij} \text{ observed frequencies} \\ \text{and } f_{ij} \text{ expected frequencies} \end{array} \quad (4)$$

$$W_{ij} = (i-j)^2(k-1)^2 \quad \begin{array}{l} \text{where } (i-j) \text{ is the distance between} \\ \text{pixel class, and } k \text{ is the number of classes} \end{array} \quad (5)$$

To quantify the impact of the neighborhood of a pixel on model accuracy, we also defined a homogeneity score. This homogeneity score counts the number of pixels sharing an edge with a given pixel and belonging to the same class as the given pixel. This analysis was performed only for pixels with four adjacent pixels (n = 1264 pixels, over the 2525 observations used to fit complete models).

### Cross validation approach.

The second approach tested how the two models (SM and ASM) would perform for new orchard data. The accuracy of each complete model was compared with accuracies obtained with RFs fitted on partial datasets. These partial datasets were constituted by randomly removing five orchards, from the 28 present in the initial dataset. At each draw, data from the five removed orchards was used to compute the accuracy and the F1-score of the fitted RF. Removing five orchards removed on average 20 % of the initial dataset (i.e. 450 pixels), as the average number of pixels per orchard was 90. We performed a thousand random draws (without replacement) of 23 orchards among 28, to compute 1000 simulated accuracies.

All computations were done using R software (R Core Team, 2023, version 4.3.1).

## 3. Results

### 3.1. Clustering vines into pixel classes

According to the elbow criteria, we determined that 4 was the most sparing number of clusters, to reduce the complexity of the information initially present within pixels while maintaining the agronomic interpretability of the classes.

On total, 1130 pixels, i.e. 45 % of observations, were attributed to class 1 (C1). Table 3 shows that this class gathered pixels containing mostly vigorous vines, as they were composed of on average 7 vigorous vines out of 8.49 vines in total.

Class 4 (C4) grouped 798 pixels, i.e. 31 % of observations. This class gathered pixels containing mostly dead or missing vines, as they were composed of on average 6.55 dead or almost dead vines out of 7.71.

Class 2 (C2) grouped 167 pixels, i.e. 7 % of the observations. This class included most of the recently replanted vines with an average of 2.79 young vines per pixel, which is much higher than all other classes (0.2, 0.23 and 0.13 vines for C1, C3 and C4, respectively), but it also included vigorous vines (2.18), low and diseased vines (1.19 and 0.47) and dead or almost dead vines (1.40) out of 8.03.

Class 3 (C3) grouped 430 pixels, i.e. 17 % of the observations. It gathered heterogeneous pixels in which more than half of the vines were declining (low vigor, diseased, dead, or almost dead vines). On average they were composed of 2.21 vines with low vigor, 1.18 diseased vines and 1.59 dead vines out of 7.23.

Classes can be ordered according to increasing gravity of decline as follows: C1 < C2 < C3 < C4.

### 3.2. The situation of French kiwifruit orchards

This study provided spatial information on kiwifruit vine agronomic status over 40 ha in September-October 2023. To only study the orchards, we removed all border pixels, as recommended by Sozzi et al. (2020), meaning that only 25.25 ha remained (2525 pixels of 100 m²). Out of this area, only 45 % was in good health and vigorous i.e. 11.30 ha (Fig. 4). More than a third (38 %) of the vines have died in recent years, of which 18 % (i.e. 7 % of the total) have been replanted, not always successfully, and 17 % were showing decline symptoms i.e. about 4.3 ha.

### 3.3. Visual inspection of pixel classes and predicted classes (whole dataset)

The weighted kappa was 0.66 for the SM and 0.74 for the ASM. Such values are considered substantial according to the Landis and Koch classification (1977). Global accuracy was 0.66 for the spectral model (SM) and 0.73 for the agronomic and spectral model (ASM), with varied accuracy among classes. Table 4 and Table 5 showed that the class with best prediction was C1 (vigorous vines) with respectively 81 % and 86 % of correct classification in SM and ASM (Table 4), and a F1-score of respectively 0.78 and 0.82 (Table 5). With both models, C1 pixels were the most often misclassified as C3 pixels (low vigor and diseased vines) with 17 % and 12 % of misclassification respectively for SM and ASM.

C4 pixels (almost dead or dead vines) were the second-best predicted class with respectively 67 % and 72 % of correct classification in SM and ASM, and a F1-score of 0.71 and 0.76. In both models, C4 pixels were the most often misclassified as C3 pixels (low vigorous and diseased vines) with 27 % and 24 % of misclassification respectively in SM and ASM.

C3 pixels (low vigor and diseased vines) were the third best predicted class with respectively 58 % and 68 % of correct classification in SM and ASM, and a F1-score of 0.56 and 0.66. In both models, C3 pixels were the

<table><caption>**Table 3**

Total and mean number of vines per agronomic class for each pixel class after clustering.</caption><thead><tr><th></th><th>Vigorous<br>vines</th><th>Low vigor<br>vines</th><th>Diseased<br>vines</th><th>Dead, almost dead or absent<br>vines</th><th>Recently<br>replanted</th><th>Mean number of vines per<br>pixel</th><th>Number of pixels per<br>class</th></tr></thead><tbody><tr><td>Class 1</td><td>7.00</td><td>0.29</td><td>0.59</td><td>0.42</td><td>0.20</td><td>8.49</td><td>1130</td></tr><tr><td>Class 2</td><td>2.18</td><td>1.19</td><td>0.47</td><td>1.40</td><td>2.79</td><td>8.03</td><td>167</td></tr><tr><td>Class 3</td><td>2.01</td><td>2.21</td><td>1.18</td><td>1.59</td><td>0.23</td><td>7.23</td><td>430</td></tr><tr><td>Class 4</td><td>0.36</td><td>0.39</td><td>0.29</td><td>6.55</td><td>0.13</td><td>7.71</td><td>798</td></tr></tbody></table>
<figure><img src="image_6.png" /><figcaption>Fig. 4. Proportion of area for each class observed in the 28 commercial orchards.</figcaption></figure>

<caption>**Table 4**</caption>

Confusion matrix for both models. For each class, the number of pixels predicted to belong to each class, and the corresponding percentage, as well as the percentage of misclassified pixels are indicated.

<table><caption>**Table 5**</caption><thead><tr><th rowspan="2">Spectral Model (SM)</th><th rowspan="2"></th><th colspan="4">Number of pixels predicted</th><th colspan="4">Predicted class</th><th rowspan="2">Percentage of misclassified pixels</th></tr><tr><th>C1</th><th>C2</th><th>C3</th><th>C4</th><th>C1</th><th>C2</th><th>C3</th><th>C4</th></tr></thead><tbody><tr><td rowspan="4">Observed pixel class</td><td>C1</td><td>920</td><td>13</td><td>193</td><td>4</td><td>81 %</td><td>1 %</td><td>17 %</td><td>0 %</td><td>19 %</td></tr><tr><td>C2</td><td>60</td><td>14</td><td>75</td><td>18</td><td>36 %</td><td>8 %</td><td>45 %</td><td>11 %</td><td>92 %</td></tr><tr><td>C3</td><td>229</td><td>30</td><td>462</td><td>77</td><td>29 %</td><td>4 %</td><td>58 %</td><td>10 %</td><td>42 %</td></tr><tr><td>C4</td><td>20</td><td>7</td><td>115</td><td>288</td><td>5 %</td><td>2 %</td><td>27 %</td><td>67 %</td><td>33 %</td></tr></tbody></table><table><thead><tr><th rowspan="2">Agronomic and Spectral Model (ASM)</th><th rowspan="2"></th><th colspan="4">Number of pixels predicted</th><th colspan="4">Predicted class</th><th rowspan="2">Percentage of misclassified pixels</th></tr><tr><th>C1</th><th>C2</th><th>C3</th><th>C4</th><th>C1</th><th>C2</th><th>C3</th><th>C4</th></tr></thead><tbody><tr><td rowspan="4">Observed pixel class</td><td>C1</td><td>976</td><td>18</td><td>133</td><td>3</td><td>86 %</td><td>2 %</td><td>12 %</td><td>0 %</td><td>13 %</td></tr><tr><td>C2</td><td>67</td><td>26</td><td>58</td><td>16</td><td>40 %</td><td>16 %</td><td>35 %</td><td>10 %</td><td>84 %</td></tr><tr><td>C3</td><td>185</td><td>14</td><td>542</td><td>57</td><td>23 %</td><td>2 %</td><td>68 %</td><td>7 %</td><td>31 %</td></tr><tr><td>C4</td><td>14</td><td>5</td><td>102</td><td>309</td><td>3 %</td><td>1 %</td><td>24 %</td><td>72 %</td><td>28 %</td></tr></tbody></table>

Precision (observed accuracy), recall (sensitivity) and F1-score computed for each class and for both models. The F1-score is the harmonic mean of precision and recall.

<table><thead><tr><th rowspan="2"></th><th colspan="3">SM</th><th colspan="3">ASM</th></tr><tr><th>Precision</th><th>Recall</th><th>F1-score</th><th>Precision</th><th>Recall</th><th>F1-score</th></tr></thead><tbody><tr><td>C1</td><td>0.81</td><td>0.75</td><td>0.78</td><td>0.86</td><td>0.79</td><td>0.82</td></tr><tr><td>C2</td><td>0.08</td><td>0.22</td><td>0.12</td><td>0.16</td><td>0.41</td><td>0.23</td></tr><tr><td>C3</td><td>0.58</td><td>0.55</td><td>0.56</td><td>0.68</td><td>0.65</td><td>0.66</td></tr><tr><td>C4</td><td>0.67</td><td>0.74</td><td>0.71</td><td>0.72</td><td>0.80</td><td>0.76</td></tr></tbody></table>

most often misclassified as C1 pixels (vigorous vines) with 29 % and 23 % of misclassification respectively in SM and ASM.

Finally, very low precisions were obtained for C2 pixels (recently replanted vines) for which precision only reached 8 % and 16 % for respectively the SM and ASM, and a F1-score of 0.12 and 0.23. The C2 pixels were mostly misclassified as C1 or C3 pixels for both SM and ASM.

Pixels were most of the time better classified when they were surrounded by pixels belonging to the same class (Fig. 5). Indeed, for both SM and ASM, if a C1 pixel (vigorous vines) was surrounded by three C1 pixels or more, it was correctly classified in more than 75 % of cases. The same result was found for C4 pixels (dead or missing vines) and C3 pixels (diseased vines) with the ASM. The 75 % accuracy threshold was only achieved with 4 similar neighboring pixels for C3 pixels with the SM. The precision for C2 pixels (young vines) was always below the 50 % threshold, independently of the number of similar neighbors. The best accuracy for C2 pixels was achieved in the ASM with 3 similar neighbors

(0.44; Fig. 5).

According to the Gini impurity index, variables which contributed most to the SM were the NIR band and the PVR (Fig. 6A). In the ASM, soil texture played a fundamental role as it reached third place after the NIR band and GNDVI in Gini's performance class. Grass cover and hail protection nets were not important to predict pixel class at this time of year. Their Gini impurity indexes were below 20 % of the NIR's (Fig. 6B).

### 3.4. Performance of prediction for new orchards

To assess prediction quality for potentially new data, we randomly selected 23 orchards to fit RFs (training set), and we quantified their accuracy for the five remaining orchards (validation set). This was run 1000 times. The average accuracy obtained after these 1000 model fits was 0.55 for SM and 0.53 for ASM (Fig. 7). They were better than a random model which should obtain an accuracy of 25 % for each class (for a 4-category model). Models were better predictors for C1 than for the other classes, as the F1-score reached 0.69 for SM and 0.67 for ASM (Table 6).

For both ASM and SM, the accuracy calculated on the complete training dataset (with the 28 orchards) was higher than the accuracies calculated on data that were not considered for model training. ASM accuracy was beyond the 95 % confidence interval of the distribution of accuracies obtained on the validation datasets. For the SM, the global accuracy was within the 95 % confidence interval (Fig. 7).
<figure><img src="image_7.png" /><figcaption>Fig. 5. Comparison of accuracies for the spectral model (SM) and the agronomic model (ASM) depending on the homogeneity score of neighboring pixels (i.e. the number of similar surrounding neighbors). Numbers above lines represent the number of pixels correctly classified.</figcaption></figure>

<figure><img src="image_8.png" /><figcaption>Fig. 6. Importance of variables on the left for the spectral model (SM) (A), and on the right for the agronomic model (ASM) (B).</figcaption></figure>

# 4. Discussion

## 4.1. Sentinel-2 images are useful to detect kiwifruit decline

The main finding of this study is that Sentinel-2 images can be used to detect decline in kiwifruit orchards and thus over large regions, despite their relatively low spatial resolution. While Savian et al. (2020b) showed better prediction and detection results on kiwifruit decline, they relied on high resolution data (<1m pixel size) acquired for one 1 ha orchard. Our approach shows that 10 m pixels can also bear useful information, as both our ASM and SM were useful to separate declining areas from healthy ones, with better prediction quality than a purely random model with 4 classes. This is the first generic model able to separate declining areas from healthy areas in kiwifruit orchards. The strength of our approach lies in our ability to cover and analyze several orchards at the same time using multispectral data from Sentinel-2 images, which would not be possible with a UAV approach.

In agreement with Savian et al. (2020b), multispectral data showed a

high correlation with plant vigor. But contrary to them, we did not use UAV thermal imagery, although they showed that it bore high potential for the early detection of kiwifruit vine health. With high resolution thermal images they were able to predict the subsequent decline one year later of seemingly vigorous vines, with an accuracy of 71 %. Indeed, it is known that when kiwifruit vines are under anoxic conditions for more than 48 h, they are not able to evapotranspirate normally, leading to an increase in canopy temperature. In such conditions root activity is reduced, and stomatal conduction drops, meaning that the flow of water between the canopy and the root system is interrupted (Matese and Di Gennaro, 2018; Smith et al., 1989). Thermal data is currently accessible for free thanks to the Landsat-8 satellite. However, its resolution of 100 m is not ideal for precise orchard monitoring, as one pixel covers an entire orchard area of 1 ha. This broad coverage can mask variations in vine vigor, leading to a loss of critical information. To achieve earlier and more accurate assessments, higher-resolution thermal data would be beneficial. Both of our model types could be improved if such data becomes available at a better resolution.
<figure><img src="image_9.png" /><figcaption>**Fig. 7.** Histogram of validation dataset accuracies for the 1000 simulations, for both models. Validation datasets consisted of data from 5 orchards randomly removed from the training dataset (i.e. around 20% of the dataset). The green dashed line indicates the accuracy of the model fitted with complete observations. The purple plain line indicates the average accuracy for the 1000 simulations. Purple dotted lines represent the 2.5% and 97.5% quantiles. (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)</figcaption></figure>

<table><caption>**Table 6**
Mean of 1000 random draws of F1-score computed for each class and for both models. The F1-score is the harmonic mean of precision and recall.</caption><thead><tr><th>F1-score</th><th>C1</th><th>C2</th><th>C3</th><th>C4</th></tr></thead><tbody><tr><th>SM</th><td>0.69</td><td>0.05</td><td>0.41</td><td>0.35</td></tr><tr><th>ASM</th><td>0.67</td><td>0.10</td><td>0.31</td><td>0.36</td></tr></tbody></table>

Our study showed that NIR was the most important variable for discriminating between the different classes. In agreement with the literature, healthy plants were characterized by higher reflectance at these wavelengths (Lum et al., 2016). Healthy vines also showed a lower reflectance for green wavelengths as they poorly absorbed green light. PVR and GNDVI are very sensitive to variation in chlorophyll content (Gitelson et al., 1996) and absorption (Metternicht, 2003). Both were higher for green canopies, so for C1 pixels. The GNDVI is more sensitive to variation in chlorophyll content than the NDVI. C3 pixels which were characterized by vines with low vigor but also with yellowing, desiccating and wilting leaves, contained vines with less chlorophyll in their leaves, and therefore a lower GNDVI than C1 pixels.

In the ASM, soil texture also plays an important role in the detection of decline. In our study, decline was more severe in orchards with clayey soils. Heavier soils naturally have poorer drainage and tend to store more water, creating unfavorable conditions for kiwifruit root systems (Bardi, 2020). This effect was apparently not sufficiently captured by the spectral data we used and may be better captured by thermal data that respond to changes in leaf temperature (Savian et al. 2020b). Today, little information on soil texture is available online (Gis Sol, 2011), or only at a spatial resolution that is not very suitable (a few kilometers), especially for small orchards, but this information is often known by growers. It is noteworthy that a recent study showed that soil texture could also be estimated even for vegetated zones using Sentinel-2 images with a resolution of 30 m, albeit with many limitations (Swain et al., 2021). This data could thus be added to the model for large-scale analysis when soil analyses are not provided or for unknown growers. Nonetheless, at the farm-scale, SM classification can be improved if the producers add their soil texture information.

As pixels size was 10 x 10 m, each pixel contains information coming from both grass and tree vegetation (Almeida-Naunay et al., 2023). All kiwifruit crops in this area have grass cover on the inter-rows, but rows are either kept bare or with a grass cover. Information on the presence of

row grass cover was not useful to discriminate classes of decline. At this time of year, after the intense summer heat, the canopy was either well developed or vines were declining. If vines were dense and vigorous, the canopy completely masks the row and the grass influence was greatly reduced. If they were declining, the row (vegetation cover or bare soil) was visible by satellites. The addition of this information in September only slightly modified pixel classification. However, it is likely that in May, at the beginning of the season, grass cover and thus weed control would have a greater impact on the ASM as shown by Zhu et al. (2024). Finally, contrary to our expectations, hail protection nets had very little impact on pixel classification. We expected that the nets would dominate orchard reflectance, decreasing the impact of canopy health status on pixel reflectance and thereby reducing the prediction accuracy of pixel classes, but this was not the case. Contrasts in canopy reflectance were sufficiently perceptible for effective classification of pixels despite the nets, possibly due to their large mesh size (2 x 8 mm).

The low F1 scores observed for classes C2 (0.23) and C3 (0.56) can be attributed to the heterogeneity within these classes. Although C2 pixels mainly comprise recently replanted vines (35 % of vines on average), and C3 pixels contain the highest number of low vigor and diseased vines (47 %), both classes also encompass vines with other agronomic statuses, including healthy and dead individuals. As a result, pixels assigned to C2 and C3 are not homogeneous and do not correspond to a single agronomic condition, which likely complicates their classification from satellite imagery. This internal variability may hinder the model's ability to distinguish these classes effectively. Moreover, as the number of C2 and C3 pixels is relatively low compared to other classes, the Random Forest model may not learn to generalize well on such underrepresented observations. This highlights the need to explore approaches specifically designed for imbalanced datasets. In this context, it may be relevant to assess model performance using complementary metrics such as macro-averaged, weighted, or micro-averaged F1 scores, as well as balanced accuracy, in order to gain a more precise and reliable understanding of classification quality.

## 4.2. Higher model accuracy when similar health status pixels are spatially grouped

One of the current limits of Sentinel-2 images is their spatial resolution. In this study we demonstrated that kiwifruit decline detection was easier when the decline affects spatial patches. ASM and SM correctly estimated vines' health status when pixels were grouped in
homogeneous patches with the same agronomic status, but the quality of the detection greatly decreased when pixels with high prevalence of decline were scattered throughout the orchard, and thus different health statuses were contiguous (less than 30 % accuracy when pixels were isolated with no similar neighbors). This is likely due to a mixed pixel effect. It means that early detection may be difficult with Sentinel-2 images.

Particularly, in our study both models had poor predictive performance for pixels containing a higher proportion of young (replanted) vines. This is in agreement with the results of Majikumna et al. (2024) where young olive trees were hardly picked up by Sentinel-2 images. Even if C2 pixels contained more young vines than the other classes, they were also a kind of melting pot, containing a mixture of all categories of vines. As their canopy was possibly poorly developed, they could be masked by vigorous vines surrounding them. Their spectral signature was thus not as specific as for pixels made up of a majority of healthy vines. It is possible that the use of temporal data could improve the classification results, because young replanted vines could be expected to show stronger interannual changes than older vines, but this remains to be tested. In addition, using multiyear data might make the method less responsive to fast changes, which is an important issue when working on swift phenomena such as kiwifruit decline. At the orchard scale, growers, who have knowledge of the orchard history, could decide to exclude pixels mainly composed of replanted vines. By removing these pixels, an accuracy of 80 % was achieved with the ASM. However, it is important to note that orchards where trees have been replanted often correspond to areas of previous severe decline, thus warranting close monitoring directly in the field.

### 4.3. From static observations to the monitoring of changing practices

Our study area represented around 1 % of French national kiwifruit surfaces i.e. 40 ha out of 3800 ha. Although our plots were chosen to be representative of varying levels of decline, only half of the surfaces were covered with vigorous and healthy vines. As we have shown, 25 % of the orchards were already dead, and 30 % were in decline. With climate change and inappropriate farming practices, this phenomenon could apply to ever larger portions of the orchard and jeopardize the French kiwifruit industry. As kiwifruit decline can be very fast (11 weeks, (Reid et al., 1992)), growers need a rapid tool to detect it and adapt their practices as soon as possible. Sentinel-2 images could be used to look at the evolution of the phenomenon over time, during a season or at a fixed date from one year to the next in selected orchards. Although the 10 x 10 m pixels contain mixed information, our study shows that Sentinel-2 images can be used to focus on management at the orchard scale, which corroborates the findings of Deng et al. (2023) and Sozzi et al. (2020). Moreover Sentinel-2 provides free access to data every 5 days. This tool could enable growers to monitor their orchard almost in real time and help them to decide precisely which part of the orchard should be tilled, drained, or where they should adapt or reduce irrigation.

## 5. Conclusions

This study has shown that Sentinel-2 images can be used to monitor kiwifruit decline over multiple and large orchards with a useful level of accuracy: roughly 61 % of declining pixels were correctly classified with a model using only multispectral data, and 69 % with a model using multispectral data and simple data on agronomic characteristics. The spectral model can be generated automatically, thanks to the spectral bands that can be easily downloaded from open databases. Prediction accuracy was mainly limited by the spatial resolution of the data: decline patches smaller than one pixel in size were not well detected, meaning that decline could not be monitored at the tree level with this technique. Pixels containing young replanted vines were also misclassified due to this limitation. Nevertheless, the mapping technology we propose could allow grower organizations to monitor the state of health of their

orchards quite simply and could be used as an alert system. This may allow growers to gain time in adjusting their practices in order to slow down the current rapid spread of kiwifruit decline.

## CRediT authorship contribution statement

**Marianne Avignon:** Writing – original draft, Methodology, Investigation, Formal analysis, Data curation, Conceptualization. **Maxime Garnault:** Writing – review & editing, Methodology, Formal analysis, Data curation. **Claire Marsden:** Writing – review & editing, Validation, Methodology. **Adeline Gachein:** Resources, Funding acquisition. **Lionel Alletto:** Writing – review & editing, Methodology. **Yvan Capowiez:** Writing – review & editing, Methodology, Data curation. **Claire Marais-Sicre:** Writing – review & editing, Software, Methodology.

## Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

## Acknowledgments

The authors would like to thank kiwifruit growers for providing their orchards, National French Kiwifruit Institute (BIK) and the ANRT for funding the PhD of Marianne Avignon (Cifre 2021/1208).

## Data availability

Data will be made available on request.

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