# Algorithms for Plant Monitoring Applications: A Comprehensive Review
## 3 Literature Analysis
### Agronomic Aims and Data Sources  
Agronomic practices are primarily based on visual observations. For example, diseases are traditionally identified with a visual diagnosis of symptoms, followed by laboratory analyses when observations are not sufficient. This traditional approach is time-consuming, it involves skilled personnel, sometimes it lacks objectivity, and it is not efficient for early disease detection [121]. Therefore, it represents a limiting factor in sustainable agriculture [276]. Visual observation is still relevant, but it is progressively substituted by image acquisition. Pictures can be used for a large variety of agronomic aims, with specific differences in terms of methods and algorithms. An example is provided by yield prediction and disease detection; the former leverages hyper-spectral images, eventually fused together with meteorological and phenological data [277; 278],to compute vegetation indexes, and consequently it employs classic machine learning predictors. On the contrary, disease detection typically uses images taken in the visible spectrum; the region of interest on the leaf, where symptoms usually appear, is extracted using clustering or segmentation methods; in this way, noisy background is removed and classifiers can work only on leaf pixels. In general, different combinations of data and algorithms are found, even if the agronomic aims are common. Hyper-spectral cameras are frequently used, mainly remotely but also proximally. Proximal data collection is reported in [279; 280; 271; 272; 273; 274; 275; 276; 277; 278; 279; 37; 37; 37; 37; 37; 37; 37; 37; 37; 38; 39; 40; 41; 42; 43; 44; 45; 46; 47; 48; 49; 50; 51; 52; 53; 54; 55; 56; 57; 58; 59; 60; 61; 62; 63; 64; 65; 66; 67; 68; 69; 70; 71; 72; 73; 74; 75; 76; 77; 78; 79; 80; 81], and it is applied to _Colletotrichum gloesoprioides_ in citrus fruits [279], tomato bacterial spot disease caused by _Xanthomonas perforans_[121], strawberry gray mold and anthracose [280], and wheat stripe rust [281]. For the early detection of root rot in chili pepper, spectral variations should be analyzed several times by a spectrophotometer [282]. A similar approach allows the acquisition of images in situ for the non-destructive diagnosis of tomato bacterial spec caused by _Pseudomonas syringae_ pv. _tomato_, and bacterial spot, caused by _Xanthomonas euvesicatoria_, on leaves [283], and for the early detection of wheat stripe rust. This study analyzes the minimum detection limit, a characteristic that determines the scope and the applicability of a measurement technique [134]. The near-infrared (nir) spectroscopy technique is used, instead, to quantify aflatoxin B1 (AFB1) in peanuts [284]. Visible/nir spectroscopy, supported by different algorithms, is successfully used in sugarcane for early disease detection [285] and in banana [286], to identify fruits infected with _Colletotrichum musae_; one-day resolution is obtained with an accuracy of around 95 percent. Remote collection is primarily carried out with satellites and UAVs. Satellite data are useful for pre-harvest crop mapping, applied to soybean, sorghum, cotton, and sugarcane crops, to predict production [287] and to map invasive species [288], but also for coffee rust detection [132]. Nevertheless, cloud cover is still challenging and the most used approach is based on UAVs with multi-spectral or ultra-high-resolution cameras [287]. Multi-spectral resolution is usefully applied to quantify peanut defoliation due to fungal diseases [289], rubber tree powdery mildew [290], _Fusarium_ wilt and banana blood disease, two significant diseases that infect banana trees, caused by _Fusarium oxysporum_, wheat resistance to _Fusarium_ head blight [162], rise diseases [291], _Ralstonia syzygi_[100], and Basal Stem Rot disease caused by _Ganoderma boninense_[159]. Multi-spectral analysis is useful to monitor leaf rust in wheat and to screen disease resistance among different wheat varieties, as well as for virus resistance in sweet potato [292].