Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence

Abstract Grapevine yellows (GY) are a significant threat to grapes due to the severe symptoms and lack of treatments. Conventional diagnosis of the phytoplasmas associated to GYs relies on symptom identification, due to sensitivity limits of diagnostic tools (e.g. real time PCR) in asymptomatic vines, where the low concentration of the pathogen or its erratic distribution can lead to a high rate of false-negatives. GY's primary symptoms are leaf discoloration and irregular wood ripening, which can be easily confused for symptoms of other diseases making recognition a difficult task. Herein, we present a novel system, utilizing convolutional neural networks, for end-to-end detection of GY in red grape vine (cv. Sangiovese), using color images of leaf clippings. The diagnostic test detailed in this work does not require the user to be an expert at identifying GY. Data augmentation strategies make the system robust to alignment errors during data capture. When applied to the task of recognizing GY from digital images of leaf clippings—amongst many other diseases and a healthy control—the system has a sensitivity of 98.96% and a specificity of 99.40%. Deep learning has 35.97% and 9.88% better predictive value (PPV) when recognizing GY from sight, than a baseline system without deep learning and trained humans respectively. We evaluate six neural network architectures: AlexNet, GoogLeNet, Inception v3, ResNet-50, ResNet-101 and SqueezeNet. We find ResNet-50 to be the best compromise of accuracy and training cost. The trained neural networks, code to reproduce the experiments, and data of leaf clipping images are available on the internet. This work will advance the frontier of GY detection by improving detection speed, enabling a more effective response to the disease

Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence

Grapevine yellows (GY) are a significant threat to grapes due to the severe symptoms and lack of treatments. Conventional diagnosis of the phytoplasmas associated to GYs relies on symptom identification, due to sensitivity limits of diagnostic tools (e.g. real time PCR) in asymptomatic vines, where the low concentration of the pathogen or its erratic distribution can lead to a high rate of false-negatives. GY's primary symptoms are leaf discoloration and irregular wood ripening, which can be easily confused for symptoms of other diseases making recognition a difficult task. Herein, we present a novel system, utilizing convolutional neural networks, for end-to-end detection of GY in red grape vine (cv. Sangiovese), using color images of leaf clippings. The diagnostic test detailed in this work does not require the user to be an expert at identifying GY. Data augmentation strategies make the system robust to alignment errors during data capture. When applied to the task of recognizing GY from digital images of leaf clippings—amongst many other diseases and a healthy control—the system has a sensitivity of 98.96% and a specificity of 99.40%. Deep learning has 35.97% and 9.88% better predictive value (PPV) when recognizing GY from sight, than a baseline system without deep learning and trained humans respectively. We evaluate six neural network architectures: AlexNet, GoogLeNet, Inception v3, ResNet-50, ResNet-101 and SqueezeNet. We find ResNet-50 to be the best compromise of accuracy and training cost. The trained neural networks, code to reproduce the experiments, and data of leaf clipping images are available on the internet. This work will advance the frontier of GY detection by improving detection speed, enabling a more effective response to the disease

Peanut leaf spot disease identification using pre-trained deep convolutional neural network

Reduction of quality and quantity of agricultural products, particularly peanut or groundnut, is usually associated with disease. This could be solved through automatic identification and diagnoses using deep learning. However, this technology is not yet explored and examined in the case of peanut leaf spot disease due to some aspects, such as the availability of sufficient data to be used for training and testing the model. This study is intended to explore the use of pre-trained visual geometry group–16 (VGG16), visual geometry group–19 (VGG19), InceptionV3, MobileNet, DenseNet, Xception, InceptionResNetV2, and ResNet50 architectures and deep learning optimizers such as stochastic gradient descent (SGD) with Momentum, adaptive moment estimation (Adam), root mean square propagation (RMSProp), and adaptive gradient algorithm (Adagrad) in creating a model that can identify leaf spot disease by using a total of 1,000 images of leaves captured using a mobile camera. Confusion matrix was used to assess the accuracy and precision of the results. The result of the study shows that DenseNet-169 trained using SGD with momentum, Adam, and RMSProp attained the highest accuracy of 98%, while DenseNet-169 trained using RMSProp achieved the highest precision of 98% among pre-trained deep convolutional neural network architectures. Furthermore, this result could be beneficial in agricultural automation and disease identification systems for peanut or groundnut plants

Flavescenza Dorata: aggiornamento su tecniche di diagnosi e interazioni pianta/fitoplasma

La Flavescenza Dorata (FD) è una patologia della vite che rientra nella categoria delle fitoplasmosi. L’agente eziologico che la causa è infatti un fitoplasma, un procariote appartenente alla classe dei Mollicutes (caratterizzati dall’assenza di parete cellulare) che colonizza i tubi cribrosi del floema della vite. La trasmissione avviene tramite un insetto vettore appartenente alla famiglia dei cicadellidi, Scaphoideus titanus Ball, e che quando infettivo ospita il fitoplasma nelle proprie ghiandole salivari. Lo studio del fitoplasma associato a FD è reso particolarmente complicato dall’impossibilità di coltivarlo in vitro, in quanto esso è un biotrofo assoluto (necessita di un ospite vivo per sopravvivere). Per tale motivo si sa ancora relativamente poco dei fitoplasmi. L’attività di ricerca svolta intorno a questa malattia punta soprattutto a elaborare nuovi metodi di prevenzione e di diagnosi tempestiva, e lo scopo di questa tesi è quello di illustrare alcune delle sperimentazioni svolte per lo sviluppo di tali metodi.Flavescence Doree (FD) is a grapevine disease associated to phytoplasmas. The etiological agent is, in fact, a phytoplasma, a prokaryotic that belongs to the Mollicutes class (characterized by the absence of cell wall) which colonizes the sieve tubes of grapevine phloem. The disease is transmitted by a vector insect belonging to the Cicadellidae family, Scaphoideus titanus Ball, which, when infectious, hosts the phytoplasma within its salivary glands. Studies around the phytoplasma associated with FD are especially complicated because of the impossibility to cultivate it in vitro, since it is an obligate biotrophic (it requires a living host to survive). For such reason relatively little is still known about phytoplasmas. Research activity made about this disease mainly aims to elaborate new methods of prevention and timely diagnosis, and the goal of this thesis is to shed light on some of the experiments conducted to develop such method

A Review on Tomato Leaf Disease Detection using Deep Learning Approaches

Agriculture is one of the major sectors that influence the India economy due to the huge population and ever-growing food demand. Identification of diseases that affect the low yield in food crops plays a major role to improve the yield of a crop. India holds the world's second-largest share of tomato production. Unfortunately, tomato plants are vulnerable to various diseases due to factors such as climate change, heavy rainfall, soil conditions, pesticides, and animals. A significant number of studies have examined the potential of deep learning techniques to combat the leaf disease in tomatoes in the last decade. However, despite the range of applications, several gaps within tomato leaf disease detection are yet to be addressed to support the tomato leaf disease diagnosis. Thus, there is a need to create an information base of existing approaches and identify the challenges and opportunities to help advance the development of tools that address the needs of tomato farmers. The review is focussed on providing a detailed assessment and considerations for developing deep learning-based Convolutional Neural Networks (CNNs) architectures like Dense Net, ResNet, VGG Net, Google Net, Alex Net, and LeNet that are applied to detect the disease in tomato leaves to identify 10 classes of diseases affecting tomato plant leaves, with distinct trained disease datasets. The performance of architecture studies using the data from plantvillage dataset, which includes healthy and diseased classes, with the assistance of several different architectural designs. This paper helps to address the existing research gaps by guiding further development and application of tools to support tomato leaves disease diagnosis and provide disease management support to farmers in improving the crop

Ensuring Agricultural Sustainability through Remote Sensing in the Era of Agriculture 5.0

This work was supported by the projects: "VIRTUOUS" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2019. Ref. 872181, "SUSTAINABLE" funded by the European Union's Horizon 2020 Project H2020-MSCA-RISE-2020. Ref. 101007702 and the "Project of Excellence" from Junta de Andalucia 2020. Ref. P18-H0-4700. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Timely and reliable information about crop management, production, and yield is considered of great utility by stakeholders (e.g., national and international authorities, farmers, commercial units, etc.) to ensure food safety and security. By 2050, according to Food and Agriculture Organization (FAO) estimates, around 70% more production of agricultural products will be needed to fulfil the demands of the world population. Likewise, to meet the Sustainable Development Goals (SDGs), especially the second goal of “zero hunger”, potential technologies like remote sensing (RS) need to be efficiently integrated into agriculture. The application of RS is indispensable today for a highly productive and sustainable agriculture. Therefore, the present study draws a general overview of RS technology with a special focus on the principal platforms of this technology, i.e., satellites and remotely piloted aircrafts (RPAs), and the sensors used, in relation to the 5th industrial revolution. Nevertheless, since 1957, RS technology has found applications, through the use of satellite imagery, in agriculture, which was later enriched by the incorporation of remotely piloted aircrafts (RPAs), which is further pushing the boundaries of proficiency through the upgrading of sensors capable of higher spectral, spatial, and temporal resolutions. More prominently, wireless sensor technologies (WST) have streamlined real time information acquisition and programming for respective measures. Improved algorithms and sensors can, not only add significant value to crop data acquisition, but can also devise simulations on yield, harvesting and irrigation periods, metrological data, etc., by making use of cloud computing. The RS technology generates huge sets of data that necessitate the incorporation of artificial intelligence (AI) and big data to extract useful products, thereby augmenting the adeptness and efficiency of agriculture to ensure its sustainability. These technologies have made the orientation of current research towards the estimation of plant physiological traits rather than the structural parameters possible. Futuristic approaches for benefiting from these cutting-edge technologies are discussed in this study. This study can be helpful for researchers, academics, and young students aspiring to play a role in the achievement of sustainable agriculture.European Commission 101007702 872181Junta de Andalucia P18-H0-470

Model for detection of Xanthomonas campestris applying machine learning techniques improved by optimization algorithms.

Esta propuesta se centra en la elaboración de un modelo que permita la detección temprana de la enfermedad Xanthomonas Campestris aplicando técnicas de Machine Learning, caracterizadas por su alta interpretabilidad, mejoradas mediante algoritmos de optimización, permitiendo identificar de manera precisa el estado de una planta (Sana o enferma), con el objeto de que los agricultores puedan tomar acciones tempranas reduciendo el impacto que genera la enfermedad en la presentación y rendimiento del cultivo.This proposal focuses on the elaboration of a model that allows the early detection of the Xanthomonas Campestris disease by applying Machine Learning techniques, characterized by their high interpretability, improved by means of optimization algorithms, allowing to accurately identify the state of a plant (Healthy or diseased), so that farmers can take early action reducing the impact generated by the disease in the presentation and yield of the crop

O impacte da inteligência artificial na sustentabilidade ambiental : uma agricultura sustentável

Mestrado em Gestão de Sistemas de InformaçãoPara lidar com o aumento da procura e de várias tendências disruptivas com sucesso, a indústria agrícola precisará superar os desafios de uma implementação de conectividade avançada. O presente estudo tem assim as seguintes questões de investigação: 1. Como pode a IA contribuir para uma gestão sustentável da agricultura? 2. Como introduzir o uso de IA na agricultura de uma forma continuada? E tem como objetivos de investigação: 1.Analisar as principais características das ferramentas baseadas em IA que permitem uma gestão sustentável da agricultura; 2.Apresentar os principais fatores limitadores para a adoção de IA na agricultura; 3.Compreender até que ponto o volume de dados é um desafio a nível do bom desempenho das ferramentas de IA; e 4.Relacionar o papel das universidades e empresas e a adoção de IA na agricultura. O método utilizado foi a realização de entrevistas semiestruturadas a peritos nas áreas da agricultura sustentável e das novas tecnologias emergentes de IA. Este estudo conclui que a IA contribui para a sustentabilidade da agricultura a três níveis: ambiental, económico e a nível dos dados. Porém alguns desafios dificultam esta adoção, nomeadamente: a dimensão territorial; a capacidade financeira dos agricultores; a idade mais avançada dos agricultores bem como a sua mentalidade cética e adversa a este tipo de tecnologias ou ainda o excesso e diversidade de dados existentes. Também, as empresas e universidades têm um peso importante na medida em que contribuem para que haja uma disseminação maior da informação através dos casos de estudo e da experimentação.To successfully cope with rising demand and several disruptive trends, the agricultural industry will need to overcome the challenges of an advanced connectivity implementation. The present study therefore has the following research questions: 1. How can AI contribute to sustainable management of agriculture? 2. How to introduce the use of AI in agriculture in a continuous way? And as research objectives: 1. Analyze the main characteristics of AI-based tools that allow sustainable management of agriculture; 2. Present the main limiting factors for the adoption of AI in agriculture; 3. Understand to what extent the volume of data is a challenge in terms of the good performance of AI tools; and 4. Relate the role of universities and companies and the adoption of AI in agriculture. The method used was the case study, using semi-structured interviews with experts in the areas of sustainable agriculture and the new emerging AI technologies. From the study, it was possible to conclude that AI contributes to the sustainability of agriculture at three levels: environmental, economic and at the data level. However, some challenges hinder this adoption, namely: the territorial dimension, the financial capacity of farmers; the older age of farmers as well as their skeptical and adverse mindset to this type of technologies; the excess and diversity of existing data, among others addressed in the study. Also, companies and universities have an important weight in that they contribute to a greater dissemination of information through case studies and experimentation.info:eu-repo/semantics/publishedVersio

Identification des problèmes phytosanitaires de la vigne au sein de la parcelle : association de l’imagerie à ultra-haute résolution spatiale et de l’apprentissage profond

Notre époque est indéniablement marquée par les changements climatiques et la baisse drastique de la biodiversité, questionnant la durabilité de l'ensemble de nos systèmes productifs. En agriculture, la remise en cause des pratiques dites conventionnelles est de plus en plus prégnante. L'usage des pesticides est l'une des pratiques les plus controversées. Bien qu'ayant contribués à l'accroissement sans précédent des rendements agricoles dans les années 1970, ces produits inquiètent désormais par leur dangerosité, autant pour la santé humaine que pour celle de l'environnement. Ils impactent notamment de façon directe la santé des populations d'insectes, dont les pollinisateurs, et du microbiote des sols. Sur le long terme, si l'usage actuel persiste, un déséquilibre des écosystèmes est à craindre. Il y a donc une urgence à sortir du système actuel de gestion de problèmes phytosanitaires. Cette évolution ne va pas être du même niveau de simplicité pour toutes les cultures. La viticulture utilise des quantités importantes de pesticides. En France, en 2006, la vigne représentait 3,3 % de la surface agricole mais 14,4 % des pesticides utilisés. Réaliser des traitements adaptés à la situation phytosanitaire réelle de la parcelle contribuerait à réduire cette consommation. Cependant, connaître l'état de santé précis d'une parcelle donnée n'est pas une information facile à obtenir. Prospecter tout un vignoble prend beaucoup de temps, ce qui rend sa réalisation régulière difficile. Dans ce projet, nous souhaitons faciliter la prospection par son automatisation. Celle-ci pourrait se matérialiser par une caméra montée sur un robot, un tracteur ou un drone, dont les images seraient ensuite analysées automatiquement, permettant à l'agriculteur d'obtenir une carte de l'état de santé de ses parcelles. Toutefois, développer un tel outil est loin d'être simple du fait de la complexité des cultures. Celui-ci doit être capable de mener une analyse malgré la diversité des variétés, des stades phénologiques mais aussi des ravageurs, des maladies et de l'ensemble de leurs symptômes. La diversité des conditions d'acquisitions possibles et la complexité du feuillage et de l'arrière-plan constituent également des défis pour l'analyse, empêchant le développement d'un outil de prospection automatique fiable. Dans ce projet de doctorat, nous avons utilisé de l'apprentissage profond, et plus particulièrement des réseaux de neurones convolutifs, comme technique d'analyse d'images pour la reconnaissance de problèmes phytosanitaires de la vigne. Nous avons plus particulièrement étudié deux maladies : le mildiou et la flavescence dorée. Deux jeux de données conséquents et reflétant la complexité des cultures ont été bâtis à partir de photos Rouge-Vert-Bleu acquises dans des vignobles. L'objectif de ce projet était d'évaluer si les réseaux de neurones convolutifs sont adaptés à l'analyse d'images pour un outil de prospection automatique. Pour cela, tout au long de nos travaux, nous avons analysé la réponse des modèles entraînés à plusieurs scénarios. Tout d'abord, nous avons confronté les modèles à des images acquises en champ, donc possédant une complexité intrinsèque. Nous avons également évalué la capacité de ces réseaux à reconnaître une maladie possédant différents symptômes présents sur plusieurs organes, le mildiou. Nous avons ensuite évalué la robustesse de l'analyse face au changement de cépages et face au changement des conditions d'acquisition des images, dont un changement de la résolution spatiale et de la plateforme d'acquisition. Nos résultats montrent que les réseaux de neurones convolutifs sont tout à fait appropriés pour la reconnaissance de problèmes phytosanitaires, fournissant des analyses à la fiabilité inédite. Pour la reconnaissance du mildiou et de six autres classes, le modèle développé a obtenu 95,48 % de bonnes prédictions. Quant à celle de la flavescence dorée, le modèle développé a atteint un taux de vrais positifs de 98,48 % sur les images issues de notre propre acquisition, et de 100 % sur un ensemble réduit d'images provenant d'une source externe. Cependant, nous avons également pu identifier plusieurs limites qui restent à surmonter. Principalement, il s'agit de l'incapacité de nos modèles à analyser des images dont le contenu, bien que thématiquement proche, voire similaire, est trop éloigné de celui des images utilisées en entraînement. Néanmoins, plusieurs techniques innovantes, telles que l'apprentissage actif (active learning) ou auto-supervisé (self-supervised learning), peuvent être adoptées pour surmonter ce problème sans forcément passer par la constitution d'un ensemble d'entraînement annoté parfaitement exhaustif. Ainsi, bien que des améliorations soient nécessaires, le contexte actuel est des plus enthousiasmants pour mener à bien le développement d'un outil de reconnaissance de problèmes phytosanitaires, et par extension, d'outils de prospection automatique pour les vignes, mais aussi pour toutes les autres cultures.Abstract : Climate change and the severe decline in biodiversity undeniably mark our era, questioning the sustainability of all our production systems. In agriculture, the reconsideration of so-called conventional practices is more and more prevalent. Use of pesticides is one of the most controversial practices. Despite their contribution to the unprecedented increase in agricultural yields in the 1970s, these products are now a matter of concern because of their danger to both human health and the environment. In particular, they directly impact the health of insect populations, including pollinators, as well as soil microbiota. In the long term, if current use persists, imbalance in the ecosystems is to be expected. Therefore, urgent action is needed to move away from the current phytosanitary management system. This change will not be as simple for all crops. Viticulture uses significant quantities of pesticides. In France, in 2006, vineyards covered 3.3 % of the agricultural surface but accounted for 14.4 % of the pesticides used. Applying treatments according to the real health conditions of the field would reduce this consumption. However, it is not simple to know the precise health status of a given plot. Scouting an entire vineyard requires a significant amount of time, which makes it difficult to do so on a regular basis. In this project, we intend to facilitate scouting by its automation. It could be achieved by a camera mounted on a robot, a tractor or a drone, whose images would then be automatically analyzed, providing the farmer with a health map of his fields. However, developing such a tool is not easy given the crops complexity. It must be able to analyze images despite the diversity of varieties, phenological stages, as well as the diversity of pests, diseases and all their symptoms. The variety of acquisition conditions and the complexity of the foliage and background also constitute challenges for the analysis, hindering the development of a reliable automatic scouting tool. In this study, we used deep learning, more specifically convolutional neural networks, as a technique to analyze images for the recognition of grapevine phytosanitary problems. In particular, we studied two diseases: downy mildew and flavescence dorée. Two large datasets reflecting the complexity of the crops were built from Red-Green-Blue photos taken in vineyards. The goal of this project was to evaluate whether convolutional neural networks are suitable for image analysis for an automatic scouting tool. Therefore, throughout our studies, we analyzed the response of the trained models to several scenarios, firstly to images captured in the field but also to diseases with many symptoms affecting several organs. We also evaluated the robustness of the analysis to changes in grape varieties and to changes in image acquisition conditions, including a change in spatial resolution and acquisition platform. Our results show that convolutional neural networks are well suited for our application, providing unprecedented reliable analyses. For the recognition of downy mildew and six other classes, the developed model achieved 95.48 % of good predictions. Regarding flavescence dorée, the model developed reached a true positive rate of 98.48 % on images from our own acquisition and 100 % on a reduced set of images from an external source. However, we were also able to identify several limitations that still need to be overcome. Mainly, the inability of our models to analyze images whose content, although thematically close or even similar, is too far away from the images used in training. Nevertheless, several innovative techniques, such as active learning or self-supervised learning, could possibly overcome this problem without the need for a perfectly comprehensive training dataset. Therefore, although improvements are necessary, the current context is most exciting to carry out the development of a phytosanitary problem recognition tool, and by extension, of automatic prospecting tools for grapevines, as well as all other crops

Development of in-field data acquisition systems and machine learning-based data processing and analysis approaches for turfgrass quality rating and peanut flower detection

Digital image processing and machine vision techniques provide scientists with an objective measure of crop quality that adds to the validity of study results without burdening the evaluation process. This dissertation aimed to develop in-field data acquisition systems and supervised machine learning-based data processing and analysis approaches for turfgrass quality classification and peanut flower detection. The new 3D Scanner App for Apple iPhone 12 Pro's camera with a LiDAR sensor provided high resolution of rendered turfgrass images. The battery life lasted for the entire time of data acquisition for an experimental field (49 m × 15 m size) that had 252 warm-season turfgrass plots. The utilized smartphone as an image acquisition tool at the same time achieved a similar outcome to the traditional image acquisition methods described in other studies. Experiments were carried out on turfgrass quality classification grouped into two classes (“Poor”, “Acceptable”) and four classes (“Very poor,” “Poor,” “Acceptable,” “High”) using supervised machine learning techniques. Gray-level Co-occurrence Matrix (GLCM) feature extractor with Random Forest classifier achieved the highest accuracy rate (81%) for the testing dataset for two classes. For four classes, Gabor filter was the best feature extractor and performed the best with Support Vector Machine (SVM) and XGBoost classifiers achieving 82% accuracy rates. The presented method will further assist researchers to develop a smartphone application for turfgrass quality rating. The study also applied deep learning-based features to feed machine learning classifiers. ResNet-101 deep feature extractor with SVM classifier achieved accuracy rate of 91% for two classes. ResNet-152 deep feature extractor with the SVM classifier achieved 86% accuracy rate for four classes. YOLOX-L and YOLOX-X models were compared with different data augmentation configurations to find the best YOLOX object detector for peanut flower detection. Peanut flowers were detected from images collected from a research field. YOLOX-X with weak data augmentation configurations achieved the highest mean average precision result at the Intersection over Union threshold of 50%. The presented method will further assist researchers in developing a counting method on flowers in images. The presented detection technique with required minor modifications can be implemented for other crops or flowers