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

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

Advancements in Multi-temporal Remote Sensing Data Analysis Techniques for Precision Agriculture

L'abstract è presente nell'allegato / the abstract is in the attachmen

Development of soft computing and applications in agricultural and biological engineering

Soft computing is a set of “inexact” computing techniques, which are able to model and analyze very complex problems. For these complex problems, more conventional methods have not been able to produce cost-effective, analytical, or complete solutions. Soft computing has been extensively studied and applied in the last three decades for scientific research and engineering computing. In agricultural and biological engineering, researchers and engineers have developed methods of fuzzy logic, artificial neural networks, genetic algorithms, decision trees, and support vector machines to study soil and water regimes related to crop growth, analyze the operation of food processing, and support decision-making in precision farming. This paper reviews the development of soft computing techniques. With the concepts and methods, applications of soft computing in the field of agricultural and biological engineering are presented, especially in the soil and water context for crop management and decision support in precision agriculture. The future of development and application of soft computing in agricultural and biological engineering is discussed

An advanced deep learning models-based plant disease detection: A review of recent research

Plants play a crucial role in supplying food globally. Various environmental factors lead to plant diseases which results in significant production losses. However, manual detection of plant diseases is a time-consuming and error-prone process. It can be an unreliable method of identifying and preventing the spread of plant diseases. Adopting advanced technologies such as Machine Learning (ML) and Deep Learning (DL) can help to overcome these challenges by enabling early identification of plant diseases. In this paper, the recent advancements in the use of ML and DL techniques for the identification of plant diseases are explored. The research focuses on publications between 2015 and 2022, and the experiments discussed in this study demonstrate the effectiveness of using these techniques in improving the accuracy and efficiency of plant disease detection. This study also addresses the challenges and limitations associated with using ML and DL for plant disease identification, such as issues with data availability, imaging quality, and the differentiation between healthy and diseased plants. The research provides valuable insights for plant disease detection researchers, practitioners, and industry professionals by offering solutions to these challenges and limitations, providing a comprehensive understanding of the current state of research in this field, highlighting the benefits and limitations of these methods, and proposing potential solutions to overcome the challenges of their implementation

Plant Disease Detection and Classification by Deep Learning

Plant diseases affect the growth of their respective species, therefore their early identification is very important. Many Machine Learning (ML) models have been employed for the detection and classification of plant diseases but, after the advancements in a subset of ML, that is, Deep Learning (DL), this area of research appears to have great potential in terms of increased accuracy. Many developed/modified DL architectures are implemented along with several visualization techniques to detect and classify the symptoms of plant diseases. Moreover, several performance metrics are used for the evaluation of these architectures/techniques. This review provides a comprehensive explanation of DL models used to visualize various plant diseases. In addition, some research gaps are identified from which to obtain greater transparency for detecting diseases in plants, even before their symptoms appear clearly

Two-stage automatic diagnosis of Flavescence Dorée based on proximal imaging and artificial intelligence: a multi-year and multi-variety experimental study

“Flavescence dorée” (FD) is a grape vine disease caused by the bacterial agent “Candidatus Phytoplasma vitis” and spread by the leafhopper Scaphoideus titanus Ball (Hemiptera: Cicadellidae). The disease is very closely monitored in Europe, as it reduces vine productivity and causes vine death and is also highly transmissible. Currently, the control method used against this disease is a two-pronged approach: i) the spraying of insecticide on a regular basis to kill the vector, and ii) a survey of each row in a vineyard by experts in this disease. Unfortunately, these experts are not able to carry out such a task every year on every vineyard and need an aid for planning their survey.In this study, we propose and evaluate an original automatic method for the detection of FD based on computer vision and artificial intelligence algorithms applied to images acquired by proximal sensing. A two-step approach was used, mimicking an expert’s scouting in the vine rows: (i) the three known isolated symptoms (red or yellow leaves depending on variety, together with a lack of shoot lignification and the presence of desiccated bunches) were detected, (ii) isolated detections were combined to make a diagnosis at image scale; i.e., vine scale. A detection network was used to detect and classify non-healthy leaves into three classes: ‘FD symptomatic leaf', 'Esca leaf' and 'Confounding leaf'; while a segmentation network was used for the retrieval of FD symptomatic shoots and bunches. Finally, the association of detected symptoms was performed by a RandomForest classifier for diagnosis at the image scale. The experimental evaluation was conducted on more than 1000 images collected from 14 blocks planted with five different grape varieties. The detection of the isolated symptoms achieved a precision of between 0.67 and 0.82 and a recall of between 0.39 and 0.59. The classification at the image scale obtained very good results when applied to images acquired under the same conditions, with the same grape varieties as the training images (precision and recall of more than 0.89). The results of the tests on the other grape varieties show the importance of having some of them in the training base in these AI-based approaches.Prospect FD : développement d'un outil d'aide à la décision pour la prospection de la flavescence dorée en vign

ADAPTIVE PROCESSING ARCHITECTURE OF MULTISENSOR SIGNALS FOR LOW-IMPACT TREATMENTS OF PLANT DISEASES.

Intelligent sensing for production of high-value crops Scientific and technical quality This thesis has been realized within the CROPS project. CROPS will develop scientific know-how for a highly configurable, modular and clever carrier platform that includes modular parallel manipulators and intelligent tools (sensors, algorithms, sprayers, grippers) that can be easily installed onto the carrier and are capable of adapting to new tasks and conditions. Several technological demonstrators will be developed for high value crops like greenhouse vegetables, fruits in orchards, and grapes for premium wines. The CROPS robotic platform will be capable of site-specific spraying (targets spray only towards foliage and selective targets) and selective harvesting of fruit (detects the fruit, determines its ripeness, moves towards the fruit, grasps it and softly detaches it). Another objective of CROPS is to develop techniques for reliable detection and classification of obstacles and other objects to enable successful autonomous navigation and operation in plantations and forests. The agricultural and forestry applications share many research areas, primarily regarding sensing and learning capabilities. The project started in October 2010 and will run for 48 month. The aim of this thesis is to lay the foundations, suggesting the guidelines, of one task addressed by the CROPS project, in particular, the aim of this work is to study the application of a VIS-NIR imaging approach (intelligent sensing), based on a relatively simple algorithm, to detect symptoms of powdery mildew and downy mildew disease at early stages of infection (sustainable production of high-value crops). Also a preliminary work for botrytis detection will be shown. Concept and objectives Many site-specific agricultural and forestry tasks, such as cultivating, transplanting, spraying, trimming, selective harvesting, and transportation, could be performed more efficiently if carried out by robotic systems. However, to date, agriculture and forestry robots are still not available, partly due to the complex, and often contradictory, demands for developing such systems. On the one hand, agro-forestry robots must be of reasonable cost, but on the other, they must be able to deal with complex, dynamic, and partly changing tasks. Addressing problems such as continuously changing conditions (e.g., rain and illumination), high variability in both the products (size, and shape) and the environment (location and soil properties), the delicate nature of the products, and hostile environmental conditions (e.g. dust, dirt, extreme temperature and humidity) requires advanced sensing, manipulation, and control. Since it is impossible to model a-priori all environments and task conditions, the robot must be able to learn new tasks and new working conditions. The solution to these demands lies in a modular and configurable design that will keep costs to a minimum by applying a basic configuration to a range of agricultural applications. At least a 95% yield rate is necessary for economical feasibility of an agro-forestry robotic system. Objectives An objective of CROPS project is to develop an \u201cintelligent tools\u201d (sensors, algorithms, sprayers) that can easily be installed onto a modular and clever carrier platform. The CROPS robotic platform will be capable of site-specific spraying (targeted spraying only on foliage and selected targets). Research efforts To achieve the novel systems described above, we will focus on intelligent sensing of disease detection on crop canopy (investigating different types and/or multiple sensors with decision making models). Technology evaluation Technology evaluation of the developed systems will include the performance evaluation of the different components (e.g., capacities, success rates/misses). Progress beyond the state-of-the-art Despite the extensive research conducted to date in applying robots to a variety of agriculture and forestry tasks (e.g., transplanting, spraying, trimming, selective harvesting), limited operating efficiencies (speeds, success rates) and lack of economic justification have severely limited commercialization. The few commercial autonomous agriculture and forestry robots that are available on the market include a cow milking robot, a robot for cutting roses (RomboMatic), and various remote-controlled forest harvesters. These robots either have a low level of autonomy or are able to perform only simple operations in structured and static environments (e.g. dairy farms and plant breeding facilities). Developing capabilities for robots operating in unstructured outdoor environments or dealing with the highly variable objects that exist in agriculture and forestry is still open-ended, and one of CROPS aims is to address this problem. Current state-of-the-art Field trials have routinely shown that most crop damage due to diseases and pests can be efficiently controlled when treatments are applied timely and accurately by hand to susceptible targets (i.e., by intelligent spraying). Site-specific spraying targeted solely to trees and/or to infected areas can reduce pesticide use by 20\u201340%. An issue of relevance to targeted agriculture is the detection of diseases in field crops. Since such events often have a visual manifestation, state-of-the-art methods for achieving this goal include fluorescence imaging or the analysis of spectral reflectance in carefully selected spectral bands. While reports of these methods used separately achieved performance at 75\u201390% accuracy, attempts to combine them have boosted disease discrimination accuracy to 95%. We must note here, however, that despite these promising results, very little research has been conducted on in-field disease detection. Expected progress The diseased detection approach for precision pesticide spraying will be developed investigating image processing techniques (after a laboratory spectral evaluation and greenhouse testing) for high-precision close-range targeted spraying to selectively and precisely apply chemicals solely to targets susceptible to specific diseases/pests, with a mean 90% success rate. Local changes in spectral reflection of parts of the canopy will be used as an indication of disease. \u201cSoft-sensor\u201d for detection of ripeness and diseases (noncontact rapid sensing system) will be developed by multispectral sensor (multispectral spectral camera). These \u201csoft sensor\u201d can be used as a decision model for targeted spraying

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops

Methods for Detecting and Classifying Weeds, Diseases and Fruits Using AI to Improve the Sustainability of Agricultural Crops: A Review

The rapid growth of the world’s population has put significant pressure on agriculture to meet the increasing demand for food. In this context, agriculture faces multiple challenges, one of which is weed management. While herbicides have traditionally been used to control weed growth, their excessive and random use can lead to environmental pollution and herbicide resistance. To address these challenges, in the agricultural industry, deep learning models have become a possible tool for decision-making by using massive amounts of information collected from smart farm sensors. However, agriculture’s varied environments pose a challenge to testing and adopting new technology effectively. This study reviews recent advances in deep learning models and methods for detecting and classifying weeds to improve the sustainability of agricultural crops. The study compares performance metrics such as recall, accuracy, F1-Score, and precision, and highlights the adoption of novel techniques, such as attention mechanisms, single-stage detection models, and new lightweight models, which can enhance the model’s performance. The use of deep learning methods in weed detection and classification has shown great potential in improving crop yields and reducing adverse environmental impacts of agriculture. The reduction in herbicide use can prevent pollution of water, food, land, and the ecosystem and avoid the resistance of weeds to chemicals. This can help mitigate and adapt to climate change by minimizing agriculture’s environmental impact and improving the sustainability of the agricultural sector. In addition to discussing recent advances, this study also highlights the challenges faced in adopting new technology in agriculture and proposes novel techniques to enhance the performance of deep learning models. The study provides valuable insights into the latest advances and challenges in process systems engineering and technology for agricultural activities