Supplementary materials

<table><caption>**Table 6.8** – Number of features after hyperparameter optimization (FV) and number of hyperparameters (HP) per feature extraction methods</caption><thead><tr><th>Type</th><th>Name</th><th>FV</th><th>HP</th></tr></thead><tbody><tr><td>Spatial</td><td>Borner</td><td>1</td><td>4</td></tr><tr><td>Spatial</td><td>Row</td><td>1</td><td>6</td></tr><tr><td>Spatial</td><td>Blob</td><td>1</td><td>4</td></tr><tr><th>type</th><th>name</th><th>FV</th><th>HP</th></tr><tr><td>Shape</td><td>Angles</td><td>9</td><td>5</td></tr><tr><td>Shape</td><td>Ellipse</td><td>11</td><td>-</td></tr><tr><td>Shape</td><td>Fragstat</td><td>6</td><td>-</td></tr><tr><td>Shape</td><td>metrics</td><td>8</td><td>-</td></tr><tr><td>Shape</td><td>Solidity</td><td>1</td><td>-</td></tr><tr><td>Shape</td><td>Particle</td><td>18</td><td>-</td></tr><tr><td>Shape</td><td>Hu Moment</td><td>7</td><td>-</td></tr><tr><td>Shape</td><td>Skeletonize</td><td>4</td><td>-</td></tr></tbody></table><table><thead><tr><th>Type</th><th>Name</th><th>FV</th><th>HP</th></tr></thead><tbody><tr><td>Spectral</td><td>Sig</td><td>16</td><td>-</td></tr><tr><td>Spectral</td><td>Stats</td><td>72</td><td>-</td></tr><tr><td>Spectral</td><td>polyfit</td><td>58</td><td>1</td></tr><tr><td>Spectral</td><td>DoG</td><td>72</td><td>2</td></tr><tr><td>Spectral</td><td>HOG</td><td>72</td><td>1</td></tr><tr><td>Spectral</td><td>Gabor</td><td>288</td><td>4</td></tr><tr><td>Spectral</td><td>Haralick</td><td>416</td><td>1</td></tr><tr><td>Spectral</td><td>GLCM</td><td>80</td><td>2</td></tr><tr><td>Spectral</td><td>Hu Moment</td><td>56</td><td>-</td></tr><tr><td>Spectral</td><td>Zernike</td><td>32</td><td>2</td></tr><tr><td>Spectral</td><td>LBP</td><td>2056</td><td>1</td></tr><tr><td>Spectral</td><td>CSLBP</td><td>144</td><td>1</td></tr><tr><td>Spectral</td><td>OCLBP</td><td>34</td><td>-</td></tr><tr><td>Spectral</td><td>Shi-Tomasi</td><td>8</td><td>1</td></tr></tbody></table>

<table><caption>**Table 6.9** – Top features for each shape types</caption><tr><td>Shape Angles</td><td>bins_8, bins_12, bins_13, bins_14, bins_15, bins_16, bins_17, bins_18, bins_19, bins_20</td></tr><tr><td>Shape Ellipse</td><td>center-distance, minor-axis, angle, eccentricity_1, eccentricity_2, eccentricity_3, eccentricity_angle, focal-distance, directrice, det</td></tr><tr><td>Shape Fragstats</td><td>perimeter-area-ratio, shape-index, fractal-dimension_index, linearity-index, related-circumscribing-circle, perimeter-area-fractal-dimension</td></tr><tr><td>Shape Hu-Moment</td><td>0, 1, 2, 3, 4, 5, 6</td></tr><tr><td>Shape Metrics</td><td>proximity, spin, area-exchange, perim-index, depth, dispersion, girth, detour</td></tr><tr><td>Shape Particle</td><td>inv-area, inv-perimeter, m2, m3, m4, m6, m8, aspect-ratio, roundness, convexity</td></tr><tr><td>Shape Skeletonize</td><td>pixel-count, end-point, intersection, branch-count</td></tr></table>
<table><caption>Table 6.10 – Top 10 for each spectral types</caption><tr><td>Spectral CSLBP</td><td>bins_0_21, bins_1_10, bins_1_18, bins_1_19, bins_1_20, bins_1_21, bins_2_10, bins_2_18, bins_2_20, bins_4_21</td></tr><tr><td>Spectral DoG</td><td>0_median, 2_min, 2_median, 3_max, 3_median, 3_entropy, 4_max, 5_max, 5_mean, std_max</td></tr><tr><td>Spectral Gabor</td><td>0_0_median, 1_0_mean, 2_0_max, 2_0_mean, 3_0_min, 3_158_entropy, 4_0_min, 5_0_min, std_158_mean, ndvi_0_min</td></tr><tr><td>Spectral HOG</td><td>0_bins_70, 1_bins_70, 1_bins_130, 2_bins_70, 3_bins_30, 5_bins_110, std_bins_30, std_bins_70, std_bins_150, ndvi_bins_150</td></tr><tr><td>Spectral GLCM</td><td>0_ASM_00, 1contrast_90, 3_correlation_90, 3_ASM_90, 4contrast_90, 4_homogeneity_00, 5contrast_90, stdcontrast_90, std_correlation_90, ndvi_homogeneity_90</td></tr><tr><td>Spectral Haralick</td><td>1_17, 1_31, 2_24, 2_31, 3_32, 4_32, 5_3, 5_51, std_29, std_44</td></tr><tr><td>Spectral Hu-Moment</td><td>0_0, 1_1, 2_1, 3_0, 3_2, 4_0, 5_0, 5_4, std_3, ndvi_3</td></tr><tr><td>Spectral LBP</td><td>bins_0_64, bins_0_209, bins_1_129, bins_2_129, bins_2_209, bins_3_129, bins_4_209, bins_4_250, bins_5_129, bins_std_129</td></tr><tr><td>Spectral OCLBP</td><td>bins_0, bins_1, bins_2, bins_3, bins_4, bins_9, bins_20, bins_26, bins_32, bins_33</td></tr><tr><td>Spectral Polyfit</td><td>1_2, 1_6, 2_6, 3_1, 3_6, 4_4, 5_3, 5_4, std_5, ndvi_0</td></tr><tr><td>Spectral Shi-tomasi</td><td>bins_0, bins_1, bins_2, bins_3, bins_4, bins_6, bins_7, bins_9, bins_11, bins_13</td></tr><tr><td>Spectral Signature</td><td>shape_0, shape_1, shape_2, shape_3, shape_4, shape_5, shape_ndvi, window_0, window_std, window_ndvi</td></tr><tr><td>Spectral Stats</td><td>0_min, 1_std, 2_min, 2_median, 3_max, 3_median, 4_std, 4_entropy, 5_max, ndvi_max</td></tr><tr><td>Spectral Zernike</td><td>0_A, 1_Zr, 1_A, 2_Zr, 3_A, 4_A, 5_A, std_A, std_Phi, ndvi_A</td></tr></table>

## Funds & Conflicts of Interest

Funding was received from the European Union's Horizon 2020 research and innovation program under grant agreement No 727321 (project acronym, IWMPRAISE). It was also funded by the French National Research Agency Challenge RoSE [grant agreement ID : ANR-17-ROSE-0002] (project acronym : ROSEAU). In addition, a collaboration with SITIA company is existing. 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
### 6.7 Conclusion de chapitre

L'un des buts de cette thèse était de définir les meilleures propriétés pour la discrimination culture/adventices. Comme il n'existe pas d'études les définissant clairement (souvent, un petit ensemble de propriétés, dans des conditions spécifiques, est utilisé.), nous avons mené nos propres recherches pour identifier les méthodes les plus prometteuses. De plus, une échelle de segmentation plus fine que celle de la plante a été définie, celle de la feuille. Ce faisant, nous espérons fournir une compréhension plus détaillée de la manière dont ces propriétés peuvent être utilisées à des fins de classification cultures/adventices.

Pour combler cette lacune, nous avons recensé les méthodes existantes et identifiées les propriétés les plus prometteuses pour la discrimination cultures/adventices à l'échelle de la feuille. On y distingue les propriétés spatiales, reposant sur des notions de distances à l'échelle de l'image, et les propriétés de forme permettant de caractériser les traits morphologiques des individus. Des propriétés de texture, de couleur et de spectre ont également été proposées pour extraire des informations liées à la composition et aux structures internes des feuilles (veines, épiderme, ...).

À partir de ce recensement, nous avons vu que certaines méthodes permettant d'extraire des propriétés comportaient des paramètres. Ces paramètres influencent le potentiel de discrimination des propriétés extraites. C'est pourquoi un algorithme a été proposé pour optimiser ces paramètres avant l'extraction et l'exploitation des propriétés concernées. Il a été montré que l'optimisation de ces hyperparamètres jouait un rôle crucial. Par exemple, les propriétés du type ShapeAngles passent de 50 % à 62.81 % de potentiel de discrimination après optimisation, de même que les propriétés SpectralDoG passent de 46.75 % à 72.39 %, etc.

Après cette phase d'optimisation, toutes les propriétés ont été extraites à partir du jeu de données pour évaluation. Au cours de cette évaluation, il a été montré l'importance de la sélection d'un ensemble plus petit de propriétés. Les 10 meilleures ont été sélectionnées pour chaque type de propriété, puis les 20 meilleures parmi toutes les propriétés combinées. Les résultats montrent une performance de discrimination culture/adventices à hauteur de 91 % avec 20 propriétés, tandis qu'une performance de 79.97 % est observée en utilisant l'ensemble des 3545 propriétés. En effet, les techniques de classification usuelles ne semblent pas être capables de gérer de grands ensembles de propriétés. Par conséquent, il est important de définir un ensemble minimal de critères qui permette de diminuer le temps de calcul tout en maximisant les performances de classification.

En conclusion, une étude a été réalisée pour déterminer quelles propriétés sont les plus importantes pour la tâche de discrimination cultures/adventices, puis elles ont été optimisées pour obtenir les meilleurs résultats. Un algorithme a été utilisé pour trouver la meilleure façon d'utiliser ces propriétés pour la classification. Au final, il a été constaté que l'utilisation d'un ensemble plus restreint de propriétés, de différents types, donne les meilleurs résultats.
# 7 Conclusion Générale

Les travaux de recherche de cette thèse ont été entrepris dans le cadre du projet européen *H2020 IWMPRAISE*, dans le but de proposer des outils pour une meilleure gestion des adventices avec pour principal objectif de diminuer l'utilisation des produits phytosanitaires et leurs impacts écologiques (présentés dans l'introduction). Cette thèse, s'est aussi insérée dans l'*ANR Challenge RoSE*, dont l'objectif était de mobiliser et d'évaluer les performances de différentes équipes de recherche, pour le désherbage de l'intra-rang en grandes cultures et cultures légumières de plein champ.

Les recherches qui ont été effectuées et présentées dans ce manuscrit, concernent la tâche de détection des adventices, qui constitue encore aujourd'hui un verrou scientifique. Pour répondre aux autres tâches évaluées dans le cadre de l'*ANR Challenge RoSE* (telles que l'interprétation, la décision et l'action), un partenariat avec la société *SITIA*, les chambres d'Agriculture de Bretagne et Pays de Loire, et l'*IRSEEM-ESIGELEC* a été établi en amont de cette thèse. L'ensemble constitue le consortium *ROSEAU*. Ainsi, les résultats de ces recherches ont voca-
tion à être implémentés sur le robot « TREKTOR », de la société *SITIA*, pour une gestion automatisée des adventices.

L'objectif principal de cette thèse était de définir les "meilleurs" critères extractibles à par-
tir d'images multispectrales, afin de discriminer les cultures des adventices. Toutefois, il est
rapidement apparu que l'extraction et l'évaluation de critères ne présentaient pas de verrou
scientifique majeur, mais que la performance et la stabilité des critères de discrimination dé-
pendait de la segmentation des images. Ainsi, le travail s'est orienté vers la définition d'indices
de végétation optimaux et d'une méthode de segmentation sémantique des instances à l'échelle
de la feuille. La performance des méthodes proposées a été évaluée sur des images de feuillage
dense, acquises en lumière naturelle. Au final, l'approche proposée mixe classification usuelle
et apprentissage profond.
### 7.1 Contributions

Comme évoqué dans le chapitre 2, il existe deux grandes voies pour détecter les adventices : une approche standard d'analyse d'image ou l'utilisation du "tout" deep-learning. Notre méthode repose sur une architecture "standard" dont chaque partie a été améliorée par optimisation des hyperparamètres ou par apprentissage profond. La figure 7.1 ci-dessous, montre l'architecture finale qui a été développée ici.

<figure><img src="image_152.png" /><figcaption>**Figure 7.1** – Pipeline final pour la détection et la discrimination des adventices</figcaption></figure>

La première étape du pipeline est le "Pre-Processing" des données, pour l'exploitation des images multispectrales acquises durant nos expérimentations. Elle consiste en la calibration de la caméra et l'utilisation d'un algorithme développé, permettant la superposition des bandes spectrales avec une erreur inférieure au pixel.

La seconde étape, "Deep-Indices", vise à discriminer la végétation du sol. Cela consiste en l'utilisation de nouveaux indices déduis par apprentissage supervisé sur des approximateurs de fonction. Ces derniers, contrairement aux indices standards, ont l'avantage de ne pas dépendre d'une correction radiométrique. Ces Deep-Indices ont montré une nette amélioration de la qualité de la segmentation (82.19 mIoU) par rapport aux indices standards (63.98–73.71 mIoU).

La troisième étape, "Deep-Leaves", permet d'identifier les feuilles. Un algorithme a été développé et utilise les Deep-Indices ainsi qu'un CNN qui détecte le contour des feuilles. Cette approche a montré de bonnes performances sur des jeux de données externes et sur nos données acquises en condition de terrain. Cette méthode permet de travailler avec n'importe quelle densité de feuillage et a la capacité de détecter les plus petites adventices même lors de leur émergence.

Enfin, on réalise l'extraction des meilleures caractéristiques pour la discrimination des cultures et des adventices. Pour ce faire, on a recensé plusieurs méthodes permettant l'extraction de différents types de caractéristiques. On utilise ensuite un algorithme d'optimisation des hyperparamètres pour chacune d'elles afin de maximiser leurs performances dans notre cas d'utilisation. On termine en utilisant un algorithme de sélection de propriétés qui permet de déterminer les meilleures combinaisons des propriétés extraites.

Une performance de classification de 91 % a été démontrée. Nous rappelons que notre étude est à l'échelle de la feuille, ce qui est une première dans ce domaine, ainsi qu'en condition terrain. Il n'existe donc pas de points de comparaison dans la littérature. On peut cependant noter qu'à l'échelle de la plante dans différentes conditions d'acquisition, l'état de l'art [Ahmed et al., 2016] donne des performances entre 77 % et 97 % de discrimination culture/adventices par de tels critères. Ce qui place favorablement nos résultats et offre ainsi des perspectives.
### 7.2 Perspectives

**Acquisition** Concernant la caméra, de par sa conception multi-capteurs, une méthode de recapture des bandes spectrales est nécessaire. Même si un algorithme a été développé durant cette thèse et même s'il montre d'excellentes performances pour corriger ce problème, la méthode est coûteuse en temps de calcul et il n'existe actuellement pas de solution plus performante pour cet appareil. Ainsi, bien que cette caméra offre une résolution spatiale de très bonne qualité, son utilisation ne permet pas un traitement des données en temps réel. D'autres caméras multispectrales et hyperspectrales ont vu le jour durant cette thèse, et pourraient améliorer les performances (temps de calcul ou précision). De plus, aucune correction radiométrique n'a été utilisée durant cette thèse, principalement du fait des contraintes expérimentales. Les résultats de cette thèse montrent d'excellentes performances sans cette correction radiométrique, mais l'on peut supposer qu'un tel pré-traitement les amélioreraient encore. Des approches par ap-prentissage profond pourraient être développées, pour proposer une correction ne nécessitant pas de dispositif d'étalonnage [Afifi, 2018, Tian et al., 2020].

**Des données synthétiques** L'annotation de larges bases de données est longue, coûteuse et souvent sujettes à des erreurs d'annotations. Dans le cas de la détection de végétation, comment définir si un pixel contient de la végétation en bordure de feuille ? Par exemple, lorsqu'il présente un mélange spectral ? Annoter précisément des images dans ce cas de figure est difficile. Certaines techniques d'augmentation de données, permettent d'ajouter de la variabilité pour ce cas de figure. Dans cette thèse, nous avons proposé d'ajouter un bruit de perlin permettant de simuler de l'ombrage. Toutefois, superposer des images ne contenant que de la végétation à des images ne contenant que du sol, en proportion variable, permettrait peut-être d'apprendre des indices encore plus résistants, mais permettrait également d'obtenir la quantité de ce mélange. Concernant les données pour la détection des feuilles, [Ward et al., 2018] a montré qu'il était possible de construire des annotations RGB synthétiques pour la détection de ces individus. Il serait fort intéressant d'étendre cette méthode à des données multispectrales en lien avec notre caméra. Pour réaliser ces données synthétiques, je propose d'utiliser soit un moteur de rendu temps-réel telque l'UnrealEngine 5 [Qiu and Yuille, 2016], soit un moteur de rendu style path-tracing via Mitsuba 2 [Nimier-David et al., 2019], tous deux basés sur des rendus physiquement réalistes. En utilisant les modèles de BRDF à microfacet et les distributions GGX, différentes conditions d'acquisition et de rugosité de surface peuvent être aisément simulées. Notons de plus, que des méthodes d'informatiques graphiques permettent d'apprendre ces matériaux à partir d'un ensemble d'acquisitions [Dupuy et al., 2015]. L'utilisation de ces technologies permettraient d'obtenir des données synthétiques d'une qualité pour l'instant inexistante dans la littérature scientifique, en agriculture de précision.
**Détection des individus** La détection des objets est un sujet difficile en particulier en scène dense, tel qu'il a été vu dans cette thèse. L'approche qui a été proposée ici semble prometteuse avec la conception d'une nouvelle architecture CNN, qui s'appuie sur plusieurs recherches anciennes. Bien que les résultats obtenus soient bons, des recherches complémentaires semblent nécessaires. L'échelle de la feuille semble adéquate, mais celle de la foliole serait peut-être plus facile d'un point de vue détection des objets. De plus, d'autres méthodes pourraient être ajoutées à l'architecture CNN proposée. Par exemple, l'algorithme des basins versants [Bai and Urta-sun, 2017] qui est actuellement utilisé en post-traitement (après la sortie du CNN). L'ajout d'un espace latent semble aussi prometteur d'après [Kulikov et al., 2018] pour la segmentation des feuilles. Enfin, comme nous l'avons vu, des améliorations sur la fonction de perte, et des techniques plus avancées d'augmentation des données doivent être explorée. En réalité, la construction de l'architecture d'un CNN est souvent empirique. Or, cette construction pourrait elle aussi être automatisée et améliorée à l'aide d'une optimisation des hyperparamètres. Mais cela nécessite des ressources de calcul qui n'étaient pas disponibles durant cette thèse.

**Extraction de propriétés** L'un des objectifs initiaux de la thèse était l'extraction et l'évaluation d'un ensemble de propriétés "standards" afin de définir leurs potentiels de discrimination pour la discrimination cultures/adventices. Il s'agissait de déterminer quels étaient les meilleurs d'entre eux. En réalité, ces caractéristiques sont fortement limitées par de nombreux facteurs. Ils ne permettent donc pas une classification parfaite. Aujourd'hui, l'apprentissage profond est la seule alternative compétitive qui pourrait permettre d'améliorer les résultats. En suivant la démarche qui a été entreprise dans cette thèse, une alternative basée sur les approximateurs de fonction est possible. On pourrait ainsi, définir les meilleures propriétés de forme à partir d'une représentation fixe du contour. De même pour les propriétés de texture et les propriétés spatiales. En réalité, un modèle type "UNet" synthétise déjà l'ensemble des caractéristiques de texture, de couleur et de spectre utilisables dans ce cadre de discrimination culture/adventices. De plus, les performances de l'extraction de ces caractéristiques ne permettent pas une utilisation temps réel, ce qui renforce la nécessite d'étudier l'apprentissage profond pour cette dernière étape.

**Le numérique pour l'agroécologie - gestion adventices** Ces travaux sont d'une grande utilité dans le contexte de transition agroécologique où des systèmes de cultures innovants, sans recours aux herbicides, sont étudiés. En effet, ces systèmes contribueront au maintien de la production agricole et permettront de répondre aux besoins d'une population humaine croissante (9,5 millards en 2050), tout en préservant les ressources naturelles et en assurant la provision de services associés à la biodiversité des adventices (services de pollinisation, maintien de la biodiversité et des chaînes trophiques basées sur les adventices). Toutefois, ils seront moins robustes face aux aléas climatiques, aux attaques des ravageurs ou parasites, et nécessiteront une grande réactivité. Face à ce problème, les outils non-destructifs issus de la proxidétection permettront de caractériser finement la biodiversité dans les parcelles cultivées. Un suivi spatio-temporel d'une parcelle avec des outils de proxidétection combinés aux outils de télédétection et à la modélisation permettra de prédire ses évolutions et d'anticiper les interventions culturales. Ainsi, l'innovation peut être un levier pour accompagner les agriculteurs dans la transition agroécologique.
Table des figures

<table><tr><td>1.1</td><td>L'Anthropocène</td><td>1</td></tr><tr><td>1.2</td><td>Dynamique des systèmes : les différents imaginaires possibles</td><td>2</td></tr><tr><td>1.3</td><td>Remembrement des terres agricoles</td><td>3</td></tr><tr><td>1.4</td><td>Excédent d'azote dans la majorités des cours d'eau en 2013</td><td>4</td></tr><tr><td>1.5</td><td>Prolifération d'algues et asphyxie des milieux aquatiques</td><td>4</td></tr><tr><td>1.6</td><td>Presence de pesticide dans la majorités des cours d'eau en 2013</td><td>5</td></tr><tr><td>1.7</td><td>Cycle de l'agriculture de précision composé de 4 étapes</td><td>8</td></tr><tr><td>1.8</td><td>Évolution de la résolution spatiale</td><td>9</td></tr><tr><td>1.9</td><td>Simulation de l'agencement spatial de paysages</td><td>10</td></tr><tr><td>1.10</td><td>Concept de projet en agriculture numérique</td><td>11</td></tr><tr><td>1.11</td><td>Plantes bio-indicatrices, fixatrices et symbiotiques</td><td>13</td></tr><tr><td>1.12</td><td>Adventices comestibles</td><td>13</td></tr><tr><td>1.13</td><td>Adventices parasite, psychotrope et médicinal</td><td>13</td></tr><tr><td>1.14</td><td>Profondeur de germination en fonction de la masse des graines</td><td>14</td></tr><tr><td>1.15</td><td>Période préférentielle de germination de certaines adventices</td><td>14</td></tr><tr><td>1.16</td><td>Taux annuel de décroissance du stock semencier des adventices</td><td>15</td></tr><tr><td>1.17</td><td>Illustration de cinq fenêtres d'opportunité pour la gestion des adventices</td><td>16</td></tr><tr><td>1.18</td><td>Exemple de pulvérisateurs</td><td>17</td></tr><tr><td>1.19</td><td>Différentes formes de buses associées</td><td>17</td></tr><tr><td>1.20</td><td>Présentation du WeedSeeker en viticulture</td><td>18</td></tr><tr><td>1.21</td><td>Exemples de méthodes alternatives de désherbage</td><td>18</td></tr><tr><td>1.22</td><td>Les différents consortiums de l'ANR Challenge ROSE</td><td>19</td></tr><tr><td>1.23</td><td>Module de désherbage BIP-BIP</td><td>19</td></tr><tr><td>1.24</td><td>Les trois modules CarbonBee (caméra hyperspectrale, contrôleur, afficheur)</td><td>20</td></tr><tr><td>1.25</td><td>Électrode téléguidée par le bras robotique</td><td>20</td></tr><tr><td>1.26</td><td>Outil de binage ROSEAU</td><td>20</td></tr><tr><td>1.27</td><td>Schéma de l'interaction entre le système climatique physique, l'exposition et la vulnérabilité produisant des risques dans la chaîne de production.</td><td>21</td></tr><tr><td>2.1</td><td>Étapes clés de l'analyse d'images agronomiques</td><td>23</td></tr><tr><td>2.2</td><td>Spectre électromagnétique et spectre visible</td><td>24</td></tr><tr><td>2.3</td><td>Les différents composants d'une caméra</td><td>24</td></tr><tr><td>2.4</td><td>Effet de la longueur de la focale sur le champ de vision</td><td>25</td></tr><tr><td>2.5</td><td>Effet de l'ouverture sur la profondeur de vision</td><td>25</td></tr><tr><td>2.6</td><td>Effet du gain sur le bruit de l'image</td><td>25</td></tr><tr><td>2.7</td><td>Effet du temps d'intégration (en seconde) sur le flou de bougé</td><td>25</td></tr><tr><td>2.8</td><td>Filtre de Bayer</td><td>26</td></tr><tr><td>2.9</td><td>Triple prismes pour diviser la lumière incidente</td><td>26</td></tr><tr><td>2.10</td><td>Filtre rotatif à 4 bandes spectrales : bleu, vert, rouge et infrarouge</td><td>27</td></tr><tr><td>2.11</td><td>Modèle de distorsion</td><td>28</td></tr><tr><td>2.12</td><td>Correction des couleurs par apprentissage profond proposé par Afifi</td><td>29</td></tr><tr><td>2.13</td><td>Différentes corrections radiométriques proposé par Honkavaara</td><td>29</td></tr><tr><td>2.14</td><td>Comparaison des méthodes de débruitage</td><td>30</td></tr><tr><td>2.15</td><td>Suppression de l'ombre par apprentissage profond proposé par Zhang</td><td>30</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>2.16</td>
      <td>Signatures spectrales de certains matériaux</td>
      <td>31</td>
    </tr>
    <tr>
      <td>2.17</td>
      <td>Structure cellulaire d'une feuille et interaction avec le spectre électromagnétique</td>
      <td>31</td>
    </tr>
    <tr>
      <td>2.18</td>
      <td>Une faible activité photo-synthétique induit une faible valeur de NDVI</td>
      <td>32</td>
    </tr>
    <tr>
      <td>2.19</td>
      <td>Segmentation Grabcut</td>
      <td>34</td>
    </tr>
    <tr>
      <td>2.20</td>
      <td>Architecture encodeur-décodeur d'un réseau convolutif</td>
      <td>35</td>
    </tr>
    <tr>
      <td>2.21</td>
      <td>Découpage régulier sans recouvrement</td>
      <td>36</td>
    </tr>
    <tr>
      <td>2.22</td>
      <td>Analyse multi-échelle des super-pixels et classification</td>
      <td>37</td>
    </tr>
    <tr>
      <td>2.23</td>
      <td>Exemple de watershed sur des images drones</td>
      <td>37</td>
    </tr>
    <tr>
      <td>2.24</td>
      <td>Polygone initial, contour actif, et contour avec contrainte</td>
      <td>38</td>
    </tr>
    <tr>
      <td>2.25</td>
      <td>Structure d'un réseau "You Only Look Once" (YOLO)</td>
      <td>38</td>
    </tr>
    <tr>
      <td>2.26</td>
      <td>Architecture d'un réseau de type Mask-RCNN</td>
      <td>39</td>
    </tr>
    <tr>
      <td>2.27</td>
      <td>Espace latent et instances associées aux clusters</td>
      <td>40</td>
    </tr>
    <tr>
      <td>2.28</td>
      <td>Segmentation des instances par détection des contours des nucléus</td>
      <td>40</td>
    </tr>
    <tr>
      <td>2.29</td>
      <td>Types de caractéristiques</td>
      <td>41</td>
    </tr>
    <tr>
      <td>2.30</td>
      <td>Caractéristiques de la forme d'une feuille</td>
      <td>41</td>
    </tr>
    <tr>
      <td>2.31</td>
      <td>Détection des rangs de culture et distance des adventices</td>
      <td>42</td>
    </tr>
    <tr>
      <td>2.32</td>
      <td>Exemple de transformation d'une image de haricot</td>
      <td>42</td>
    </tr>
    <tr>
      <td>2.33</td>
      <td>Exemple d'un CNN pour la classification de cellule</td>
      <td>43</td>
    </tr>
    <tr>
      <td>2.34</td>
      <td>Taxonomie des algorithmes d'apprentissage machine</td>
      <td>44</td>
    </tr>
    <tr>
      <td>2.35</td>
      <td>Règle de décision de Bayes</td>
      <td>45</td>
    </tr>
    <tr>
      <td>2.36</td>
      <td>Classification KNN : influence de K et de l'heuristique</td>
      <td>45</td>
    </tr>
    <tr>
      <td>2.37</td>
      <td>Perceptron</td>
      <td>46</td>
    </tr>
    <tr>
      <td>2.38</td>
      <td>Arbre de décision simple en agriculture de précision</td>
      <td>46</td>
    </tr>
    <tr>
      <td>2.39</td>
      <td>Algorithme de classification K-Mean</td>
      <td>47</td>
    </tr>
    <tr>
      <td>2.40</td>
      <td>Illustration de la méthode de clustering DBSCAN</td>
      <td>47</td>
    </tr>
    <tr>
      <td>2.41</td>
      <td>Optimisation de l'espace latent</td>
      <td>48</td>
    </tr>
    <tr>
      <td>2.42</td>
      <td>Evaluation des performances d'un modèle après apprentissage</td>
      <td>49</td>
    </tr>
    <tr>
      <td>2.43</td>
      <td>Architecture CNN proposée</td>
      <td>52</td>
    </tr>
    <tr>
      <td>2.44</td>
      <td>Comparaison de la précision de reconnaissance entre décomposition en ondelettes et apprentissage profond, en fonction du nombre d'échantillons</td>
      <td>52</td>
    </tr>
    <tr>
      <td>2.45</td>
      <td>Algorithme proposé par [Louargant et al., 2018], (a) détection des rangs (b) constitution d'une base d'apprentissage (c) classification spectrale et (d) reclassement des adventices intra-rangs</td>
      <td>53</td>
    </tr>
    <tr>
      <td>2.46</td>
      <td>Schéma de la méthode proposée par [Bah et al., 2018a]</td>
      <td>54</td>
    </tr>
    <tr>
      <td>2.47</td>
      <td>Images résultantes : (a) Image prétraitée. (b) Méthode 1, chaîne de traitement. (c) Méthode 2, Yolo. Et (d) Méthode 3, mask-RCNN.</td>
      <td>55</td>
    </tr>
    <tr>
      <td>2.48</td>
      <td>Chaînes de traitement</td>
      <td>56</td>
    </tr>
    <tr>
      <td>3.1</td>
      <td>Parcelle pour les premières expérimentations (rouge) à gauche et parcelles utilisées par le consortium ROSEAU (droite)</td>
      <td>57</td>
    </tr>
    <tr>
      <td>3.2</td>
      <td>Système optique de la caméra Airphen (Société Hiphen, France)</td>
      <td>58</td>
    </tr>
    <tr>
      <td>3.3</td>
      <td>Réponse spectrale des 6 filtres interférentiels placés devant les capteurs de la caméra multispectrale</td>
      <td>58</td>
    </tr>
    <tr>
      <td>3.4</td>
      <td>Robot et caméra utilisés par le consortium ROSEAU</td>
      <td>59</td>
    </tr>
    <tr>
      <td>3.5</td>
      <td>Fausses couleurs à gauche et vérité terrain manuelle correspondante à droite</td>
      <td>60</td>
    </tr>
    <tr>
      <td>3.6</td>
      <td>Annotation pour la discrimination des feuilles, entre culture et adventices</td>
      <td>60</td>
    </tr>
    <tr>
      <td>3.7</td>
      <td>Exemples issues de la base de données CVPPP (haut) et vérité terrain (bas)</td>
      <td>61</td>
    </tr>
    <tr>
      <td>3.8</td>
      <td>Exemples issues de la base de données Komatsuna (haut) et vérité terrain (bas)</td>
      <td>61</td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>3.9</td>
      <td>Relation entre paramètres du monde réel et ceux du monde virtuel</td>
      <td>62</td>
    </tr>
    <tr>
      <td>3.10</td>
      <td>Échiquier de calibration géométrique</td>
      <td>63</td>
    </tr>
    <tr>
      <td>3.11</td>
      <td>Visualisation de la correction géométrique pour la bande 450 nm</td>
      <td>64</td>
    </tr>
    <tr>
      <td>3.12</td>
      <td>Image non recalée, prise à 1.60 m</td>
      <td>65</td>
    </tr>
    <tr>
      <td>3.13</td>
      <td>Disposition of each band on the Airphen multi-sensors camera</td>
      <td>68</td>
    </tr>
    <tr>
      <td>3.14</td>
      <td>Each step of the alignment procedure, with (step 1) roughly corrected from affine correction and (step 2) enhancement via key-points and perspective</td>
      <td>69</td>
    </tr>
    <tr>
      <td>3.15</td>
      <td>Gradient orientation in spectral band [Rabatel and Labbe, 2016]. Orientation of the gradient is not the same depending to the spectral band.</td>
      <td>70</td>
    </tr>
    <tr>
      <td>3.16</td>
      <td>Example of each correction and the manufacturers results</td>
      <td>72</td>
    </tr>
    <tr>
      <td>3.17</td>
      <td>Affine matrix value by height</td>
      <td>72</td>
    </tr>
    <tr>
      <td>3.18</td>
      <td>Affine matrix value by height</td>
      <td>73</td>
    </tr>
    <tr>
      <td>3.19</td>
      <td>features extractor performances after filtering and homography association</td>
      <td>74</td>
    </tr>
    <tr>
      <td>3.20</td>
      <td>key-point extractor performances</td>
      <td>75</td>
    </tr>
    <tr>
      <td>3.21</td>
      <td>(up) The mean distance of detected key-point before perspective correction with 570 nm as spectral reference (down) Perspective re-projection error with GFTT using the first modality and 570 nm as reference</td>
      <td>75</td>
    </tr>
    <tr>
      <td>4.1</td>
      <td>AIRPHEN camera composed of 6 sensors</td>
      <td>83</td>
    </tr>
    <tr>
      <td>4.2</td>
      <td>False color in the left and corresponding manual ground truth on the right.</td>
      <td>84</td>
    </tr>
    <tr>
      <td>4.3</td>
      <td>Linear combination model.</td>
      <td>86</td>
    </tr>
    <tr>
      <td>4.4</td>
      <td>Linear ratio model.</td>
      <td>86</td>
    </tr>
    <tr>
      <td>4.5</td>
      <td>Polynomial model with Bernstein expansions between B<sub>4,1</sub> and B<sub>4,4</sub>.</td>
      <td>87</td>
    </tr>
    <tr>
      <td>4.6</td>
      <td>Universal function approximation model (depth = 3, width = 5).</td>
      <td>88</td>
    </tr>
    <tr>
      <td>4.7</td>
      <td>Dense-morphological model.</td>
      <td>88</td>
    </tr>
    <tr>
      <td>4.8</td>
      <td>Input Band Filter inserted at the beginning of the model.</td>
      <td>89</td>
    </tr>
    <tr>
      <td>4.9</td>
      <td>Spatial refinement block inserted at the end of a model.</td>
      <td>89</td>
    </tr>
    <tr>
      <td>4.10</td>
      <td>Optimized model for standard indices</td>
      <td>91</td>
    </tr>
    <tr>
      <td>4.11</td>
      <td>Network synthesis with ibf, evaluated index equation, and sprb.</td>
      <td>93</td>
    </tr>
    <tr>
      <td>4.12</td>
      <td>first 80 epochs of loss of generic models with ibf in kernel size of 1.</td>
      <td>96</td>
    </tr>
    <tr>
      <td>4.13</td>
      <td>Correct vegetation/soil discrimination despite shadows.</td>
      <td>97</td>
    </tr>
    <tr>
      <td>4.14</td>
      <td>Vegetation/soil discrimination issue with abrupt transition between shadow and light.</td>
      <td>97</td>
    </tr>
    <tr>
      <td>4.15</td>
      <td>Vegetation/soil discrimination issue caused by specular lights on leaves.</td>
      <td>98</td>
    </tr>
    <tr>
      <td>4.16</td>
      <td>Visual comparison between some relevant models. NDVI (63.98 mIoU), MTVI1 (73.71 mIoU), linear 1 baseline (78.58 mIoU), dense 7 ibf-sprb (82.19 mIoU). Blue indicates sure soil, red indicates sure vegetation, and the other colors indicate uncertainty.</td>
      <td>101</td>
    </tr>
    <tr>
      <td>5.1</td>
      <td>Mélange de haricot (culture) et de moutarde (adventice) dans le challenge RoSE</td>
      <td>103</td>
    </tr>
    <tr>
      <td>5.2</td>
      <td>Structure d'un réseau "You Only Look Once" (YOLO)</td>
      <td>104</td>
    </tr>
    <tr>
      <td>5.3</td>
      <td>Aerial view of the experimentation plot located in Montoldre (now INRAE)</td>
      <td>108</td>
    </tr>
    <tr>
      <td>5.4</td>
      <td>The experimental set-up : the multispectral camera and the robotic plateform</td>
      <td>109</td>
    </tr>
    <tr>
      <td>5.5</td>
      <td>Example of images from the CVPPP dataset (top) and their corresponding ground truth (bottom)</td>
      <td>109</td>
    </tr>
    <tr>
      <td>5.6</td>
      <td>Example of RGB images from the Komatsuna dataset (top) and their corresponding ground truth (bottom)</td>
      <td>110</td>
    </tr>
    <tr>
      <td>5.7</td>
      <td>Diagram of the CNN network architecture and losses (dotted). Multiple arrow show concatenation as input layer.</td>
      <td>111</td>
    </tr>
  </tbody>
</table>
<table>
  <tbody>
    <tr>
      <td>5.8</td>
      <td>Example of input data : from left to right, the first three bands (RGB) of the Airphen multispectral camera, three edge classes and the inner of individual class</td>
      <td>111</td>
    </tr>
    <tr>
      <td>5.9</td>
      <td>Universal function approximator based on DenseNet. Multiple arrows shows concatenation as input layer</td>
      <td>112</td>
    </tr>
    <tr>
      <td>5.10</td>
      <td>Synthesis of the core network based on MFP-Unet. Red arrows shows MaxPoo-ling. Blue arrows shows Conv+UpSampling. Black arrows shows Conv. Sub-scale ratio are labelled on corresponding layer. The final output is the concatenation of features UpSampling. Multiple arrows shows concatenation as input layer</td>
      <td>113</td>
    </tr>
    <tr>
      <td>5.11</td>
      <td>Auxiliary output and classification module. Multiple arrows shows concatenation as input layer</td>
      <td>114</td>
    </tr>
    <tr>
      <td>5.12</td>
      <td>Sorted histogram of the number of pixels associated to each component, the blue (resp. red) line represents the true (resp. predicted) number of pixels for each component.</td>
      <td>115</td>
    </tr>
    <tr>
      <td>5.13</td>
      <td>The refinement of the CNN output through watershed and Deep-Indices. The Seed of the equation 5.5 can be seen on the “watershed seed” image.</td>
      <td>116</td>
    </tr>
    <tr>
      <td>5.14</td>
      <td>Visual comparison of Komatsuna dataset after the training, the red arrow in the input image shows a green bottle</td>
      <td>118</td>
    </tr>
    <tr>
      <td>5.15</td>
      <td>Visual example of LSC results for A1</td>
      <td>121</td>
    </tr>
    <tr>
      <td>5.16</td>
      <td>Visual example of LSC results for A2</td>
      <td>121</td>
    </tr>
    <tr>
      <td>5.17</td>
      <td>Visual example of LSC results for A3</td>
      <td>122</td>
    </tr>
    <tr>
      <td>5.18</td>
      <td>Visual example of LSC results for A4</td>
      <td>122</td>
    </tr>
    <tr>
      <td>5.19</td>
      <td>Example of the visual results for the Airphen dataset</td>
      <td>124</td>
    </tr>
    <tr>
      <td>5.20</td>
      <td>Example of the Watershed output versus Mask-RCNN for Airphen dataset</td>
      <td>125</td>
    </tr>
    <tr>
      <td>6.1</td>
      <td>key steps of image analysis for crop/weed discrimination with (a) acquisition enhancement (b) vegetation indices (c) instance segmentation (d) feature ex-traction (e) classification</td>
      <td>131</td>
    </tr>
    <tr>
      <td>6.2</td>
      <td>Aerial view of the experimentation plot located in Montoldre (now INRAE)</td>
      <td>133</td>
    </tr>
    <tr>
      <td>6.3</td>
      <td>The Airphen six-band multispectral camera</td>
      <td>133</td>
    </tr>
    <tr>
      <td>6.4</td>
      <td>Hyperparameter optimization procedure</td>
      <td>140</td>
    </tr>
    <tr>
      <td>6.5</td>
      <td>Sequential Feature Selection Algorithm</td>
      <td>141</td>
    </tr>
    <tr>
      <td>6.6</td>
      <td>Best features among all extracted feature for classification through ASHA algo-rithm. Best overall result is highlighted.</td>
      <td>145</td>
    </tr>
    <tr>
      <td>6.7</td>
      <td>This figure shows the classification of leaves, from an image taken in June 2019. The blue color corresponds to well classified weeds. The green color corresponds to well classified crops. While the purple and red color correspond respectively to weeds and crops poorly classified.</td>
      <td>146</td>
    </tr>
    <tr>
      <td>6.8</td>
      <td>This figure shows the classification of leaves, from parts of four images taken in June 2019. The blue color corresponds to well classified weeds. The green color corresponds to well classified crops. While the purple and red color correspond respectively to weeds and crops poorly classified. Here we see that leaves bet-ween crop rows are correctly classified, as are monocots. On the other hand, the emerging leaves of crops and some weed type leaves of dicotyledons are incorrect.</td>
      <td>147</td>
    </tr>
    <tr>
      <td>7.1</td>
      <td>Pipeline final pour la détection et la discrimination des adventices</td>
      <td>154</td>
    </tr>
  </tbody>
</table>
## Liste des tableaux

<table><tr><td>2.1</td><td>Invariance et sources d'erreurs pour ces trois types de propriétés</td><td>43</td></tr><tr><td>3.1</td><td>Dates d'acquisition, cultures et nombre d'images acquises</td><td>59</td></tr><tr><td>3.2</td><td>Nombre d'images obtenues lors des campagnes d'acquisition du challenge RoSE</td><td>59</td></tr><tr><td>3.3</td><td>Résolution spatiale des images à différentes hauteurs</td><td>62</td></tr><tr><td>3.4</td><td>Érreur résiduelle, en pixel, après correction géométrique</td><td>63</td></tr><tr><td>3.5</td><td>Correction de la lentille pour la bande spectral 450 nm</td><td>64</td></tr><tr><td>3.6</td><td>list of algorithms with 3 modalities of their parameters</td><td>71</td></tr><tr><td>4.1</td><td>Acquisition sources and global illumination.</td><td>83</td></tr><tr><td>4.2</td><td>Synthesized standard indices performances : the nine best models are presented.</td><td>92</td></tr><tr><td>4.3</td><td>Score of DeepIndices with/without ibf and sprb for a kernel size of 1</td><td>93</td></tr><tr><td>4.4</td><td>Score of DeepIndices with/without ibf and sprb for a kernel size of 3</td><td>93</td></tr><tr><td>4.5</td><td>Score of DeepIndices with/without ibf and sprb for a kernel size of 5</td><td>94</td></tr><tr><td>4.6</td><td>Score of DeepIndices with/without ibf and sprb for a kernel size of 7</td><td>94</td></tr><tr><td>4.7</td><td>Score of DeepIndices in different modalities for a kernel size of 1 without initial image processing</td><td>95</td></tr><tr><td>4.8</td><td>Top optimized fixed vegetation model equations</td><td>100</td></tr><tr><td>5.1</td><td>Evaluation metrics for the Komatsuna dataset</td><td>118</td></tr><tr><td>5.2</td><td>Comparison of the ResNet 101 based on Detectron II framework of facebook and the UPGen based on Mask-RCNN. Our solution is the most effective in both metrics.</td><td>119</td></tr><tr><td>5.3</td><td>Number of images for each sub LSC dataset</td><td>119</td></tr><tr><td>5.4</td><td>Evaluation metrics of the overall LSC dataset</td><td>120</td></tr><tr><td>5.5</td><td>Evaluation metrics of the independent sub LSC dataset</td><td>120</td></tr><tr><td>5.6</td><td>Comparison of our solution with state-of-the-art challengers in this dataset.</td><td>123</td></tr><tr><td>5.7</td><td>Evaluation metrics for the Airphen dataset</td><td>123</td></tr><tr><td>5.8</td><td>Evaluation metrics for the Airphen dataset using Mask-RCNN</td><td>124</td></tr><tr><td>6.1</td><td>Synthesis of colors moments</td><td>137</td></tr><tr><td>6.2</td><td>Five epochs of the optimization procedure through ASHA algorithm. The objective column shows the model performance</td><td>140</td></tr><tr><td>6.3</td><td>Hyperparameter optimization through ASHA algorithm, the objective column shows the performance of the underlying classifier</td><td>142</td></tr><tr><td>6.4</td><td>Feature optimization through ASHA algorithm. To show the importance of the feature optimisation process, we added the column Worst, which is the least discriminant parameters. The column Objective shows the best performance of the model.</td><td>142</td></tr><tr><td>6.5</td><td>Best features for spatial features. Best results for a specific feature count are highlighted</td><td>143</td></tr></table>
<table>
  <tbody>
    <tr>
      <td>6.6</td>
      <td>Best classification features for each feature type through ASHA algorithm. Each line presents the results for a specific feature type. The feature count is the number of feature(s) combined for a specific feature type. The presented results are the optimal combinations for each feature counts. Column "full" is the result obtained with all the features for a specific feature type. Best results for a type of features are in bold, best results for a specific feature count are highlighted.</td>
      <td>143</td>
    </tr>
    <tr>
      <td>6.7</td>
      <td>Best features individually for each feature type for classification through ASHA algorithm. Each line presents the results for a specific feature type. The feature count is the number of feature(s) combined for a specific feature type. The presented results are the optimal combinations for each feature counts. Column "full" is the result obtained with all the features for a specific feature type. Best results for a type of features are in bold, best results for a specific feature count are highlighted.</td>
      <td>144</td>
    </tr>
    <tr>
      <td>6.8</td>
      <td>Number of features after hyperparameter optimization (FV) and number of hyperparameters (HP) per feature extraction methods</td>
      <td>150</td>
    </tr>
    <tr>
      <td>6.9</td>
      <td>Top features for each shape types</td>
      <td>150</td>
    </tr>
    <tr>
      <td>6.10</td>
      <td>Top 10 for each spectral types</td>
      <td>151</td>
    </tr>
  </tbody>
</table>
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