Detection of "Flavescence dorée" Grapevine Disease Using Unmanned Aerial Vehicle (UAV) Multispectral Imagery

Flavescence dorée is a grapevine disease affecting European vineyards which has severe economic consequences and containing its spread is therefore considered as a major challenge for viticulture. Flavescence dorée is subject to mandatory pest control including removal of the infected vines and, in this context, automatic detection of Flavescence dorée symptomatic vines by unmanned aerial vehicle (UAV) remote sensing could constitute a key diagnosis instrument for growers. The objective of this paper is to evaluate the feasibility of discriminating the Flavescence dorée symptoms in red and white cultivars from healthy vine vegetation using UAV multispectral imagery. Exhaustive ground truth data and UAV multispectral imagery (visible and near-infrared domain) have been acquired in September 2015 over four selected vineyards in Southwest France. Spectral signatures of healthy and symptomatic plants were studied with a set of 20 variables computed from the UAV images (spectral bands, vegetation indices and biophysical parameters) using univariate and multivariate classification approaches. Best results were achieved with red cultivars (both using univariate and multivariate approaches). For white cultivars, results were not satisfactory either for the univariate or the multivariate. Nevertheless, external accuracy assessment show that despite problems of Flavescence dorée and healthy pixel misclassification, an operational Flavescence dorée mapping technique using UAV-based imagery can still be proposed

Precision agriculture trends in fruit growing from 2016 to 2020.

Brazilian fruit culture has a great influence on the social and economic sector in the most diverse regions of the country, generating employment and income in the exercise of its activities. As it is an activity carried out most often in a manual and conventional manner, fruit culture has a great potential for technological growth, especially when adopting the concepts applied by precision agriculture on the crops of grains, fibers and energy, creating a new segment, Precision Fruit Farming. The present work aims to carry out a bibliographic review on the main trends that have emerged in the last five years on Precision Fruit growing, highlighting its future perspectives and the history of technological evolution. 83 articles were analyzed, classified in different perennial cultures and applications, such as machine learning, remote sensing, robotics, using UAV to obtain different vegetation indexes, among others. Index Terms: Machine Learning; Vegetation Indexes; Robotics; Tendências da agricultura de precisão em fruticultura no período de 2016 A 2020 Resumo - A fruticultura brasileira exerce grande influência sobre o setor social e econômico nas mais diversas regiões do País, gerando emprego e renda no exercício de suas atividades. Por se tratar de uma atividade realizada, na maioria das vezes, de forma manual e convencional, a fruticultura possui grande potencial de crescimento tecnológico, principalmente ao adotar os conceitos aplicados pela agricultura de precisão sobre as culturas de grãos, fibras e energia, criando um novo segmento, a Fruticultura de Precisão. O presente estudo objetivou realizar uma revisão bibliográfica sobre as principais tendências que surgiram nos últimos cinco anos sobre a Fruticultura de Precisão, destacando suas perspectivas futuras e o histórico de evolução tecnológica. Foram analisados 83 artigos, classificados em diferentes culturas perenes e aplicações, como machine learning (aprendizado de máquinas), sensoriamento remoto, robótica, utilização de VANT para obtenção de diferentes índices de vegetação, entre outras. Termos para indexação: Aprendizagem de Máquina; Índices de Vegetação; Robótica; Sensoriamento Remoto; VANT

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

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

Ground and Aerial Robots for Agricultural Production: Opportunities and Challenges

Crop and animal production techniques have changed significantly over the last century. In the early 1900s, animal power was replaced by tractor power that resulted in tremendous improvements in field productivity, which subsequently laid foundation for mechanized agriculture. While precision agriculture has enabled site-specific management of crop inputs for improved yields and quality, precision livestock farming has boosted efficiencies in animal and dairy industries. By 2020, highly automated systems are employed in crop and animal agriculture to increase input efficiency and agricultural output with reduced adverse impact on the environment. Ground and aerial robots combined with artificial intelligence (AI) techniques have potential to tackle the rising food, fiber, and fuel demands of the rapidly growing population that is slated to be around 10 billion by the year 2050. This Issue Paper presents opportunities provided by ground and aerial robots for improved crop and animal production, and the challenges that could potentially limit their progress and adoption. A summary of enabling factors that could drive the deployment and adoption of robots in agriculture is also presented along with some insights into the training needs of the workforce who will be involved in the next-generation agriculture

Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review

Disease diagnosis is one of the major tasks for increasing food production in agriculture. Although precision agriculture (PA) takes less time and provides a more precise application of agricultural activities, the detection of disease using an Unmanned Aerial System (UAS) is a challenging task. Several Unmanned Aerial Vehicles (UAVs) and sensors have been used for this purpose. The UAVs’ platforms and their peripherals have their own limitations in accurately diagnosing plant diseases. Several types of image processing software are available for vignetting and orthorectification. The training and validation of datasets are important characteristics of data analysis. Currently, different algorithms and architectures of machine learning models are used to classify and detect plant diseases. These models help in image segmentation and feature extractions to interpret results. Researchers also use the values of vegetative indices, such as Normalized Difference Vegetative Index (NDVI), Crop Water Stress Index (CWSI), etc., acquired from different multispectral and hyperspectral sensors to fit into the statistical models to deliver results. There are still various drifts in the automatic detection of plant diseases as imaging sensors are limited by their own spectral bandwidth, resolution, background noise of the image, etc. The future of crop health monitoring using UAVs should include a gimble consisting of multiple sensors, large datasets for training and validation, the development of site-specific irradiance systems, and so on. This review briefly highlights the advantages of automatic detection of plant diseases to the growers

Effetto varietale e della forma di allevamento sull'applicazione di tecniche avanzate di viticoltura di precisione: un caso studio in Valpolicella.

Negli ultimi anni le esigenze e gli standard qualitativi del settore vitivinicolo sono sempre più in aumento, questo ha portato anche ad un’innovazione in ambito tecnologico che ha permesso di sviluppare sistemi di sensoristica in grado di monitorare molti aspetti dell’ambiente di coltivazione della vite e delle sue stesse risposte fisiologiche, qualitative e produttive. Tali sistemi sono spesso accompagnati da geolocalizzatori e possono essere applicati in prossimità della coltura o montati su mezzi aerei o su satelliti, che acquisiscono dati tramite i quali è possibile estrapolare importanti informazioni sullo stato fisiologico della pianta e spazializzare i dati dai diversi parametri misurati nel vigneto. Perché i dati sperimentali siano attendibili, è importante osservare che ci siano delle corrispondenze tra i dati ottenuti dall’utilizzo degli strumenti sensoristici e le misure fatte direttamente sulla pianta. In questa tesi, sono stati presi in considerazione tre differenti appezzamenti nell’areale vitivinicolo veronese in cui vengono utilizzate sia forme di allevamento a pergola ma anche a guyot, nei quali vengono coltivate le varietà principali della zona. I vigneti sono stati caratterizzati per il loro indice vegetazionale tramite informazione spettrale ricavata da immagine satellitare e una camera multispettrale montata su drone. Sono stati svolti dei rilievi mediante l’utilizzo di sensori da remoto, che sensori prossimali per quanto riguarda lo studio dell’apparato vegetativo della pianta e successivamente sono stati presi dei parametri vegeto- direttamente sulla pianta come la fertilità delle gemme, la lunghezza dei germogli per calcolare la cinetica della crescita e la superficie fogliare. infine, sono stati presi anche parametri dell’uva alla maturazione e quindi la resa per ceppo, peso per grappolo, spessore della buccia, È importante osservare come i dati sperimentali raccolti possono aiutarci a comprender meglio le relazioni che vi sono tra i vari sistemi di allevamento e le principali varietà utilizzate. L’obbiettivo di questa tesi è quello di osservare e stabilire la correlazione tra le forme di allevamento e le varietà tipiche veronesi andando ad utilizzare tecniche avanzate di viticultura diprecisione.In recent years, the demands and quality standards of the wine sector have been increasing; this has also led to innovation in technology that has enabled the development of sensor systems capable of monitoring many aspects of the vine-growing environment and its own physiological, quality and production responses. Such systems are often accompanied by geolocators and can be applied close to the crop or mounted on aerial or satellite vehicles, which acquire data through which important information about the physiological state of the plant can be extrapolated and spatialized from the various parameters measured in the vineyard. For the experimental data to be reliable, it is important to observe that there are correspondences between the data obtained from the use of sensor instruments and the measurements made directly on the plant. In this thesis, three different plots were considered in the Verona wine-growing area in which both pergola and guyot forms of training are used, in which the main varieties of the area are grown. The vineyards were characterized for their vegetation index by spectral information obtained from satellite imagery and a drone-mounted multispectral camera. Surveys were carried out using remote sensors, which proximal sensors regarding the study of the plant's vegetative system, and then vegetative parameters were taken directly on the plant such as bud fertility, shoot length to calculate growth kinetics and leaf area. finally, parameters of the grape at maturity were also taken and thus yield per stump, weight per bunch, skin thickness, It is important to note how the experimental data collected can help us better understand the relationships between the various training systems and the main varieties used. The objective of this thesis is to observe and establish the correlation between the forms of farming and the typical Veronese varieties by going to use advanced techniques of viticulture diprecision

Previsão da produção na casta “Castelão” com recurso a análise de imagem

Mestrado em Engenharia de Viticultura e Enologia (Double degree) / Instituto Superior de Agronomia. Universidade de Lisboa / Faculdade de Ciências. Universidade do PortoA grande variabilidade espacial que carateriza a vinha, bem como a importância socioeconómica do setor da vinha e do vinho têm contribuído para a crescente procura de novos métodos para estimar a produção. Os métodos tradicionais são destrutivos, demorados e dispendiosos ou têm apenas em conta o conhecimento histórico e a experiência do viticultor. Outros métodos têm sido investigados e introduzidos, com especial relevância dada às metodologias com base em sensores remotos e análise de imagem, principalmente por ser não destrutiva, não evasiva e de baixo custo. Assim, o presente ensaio tem como objetivo prever a produção na variedade Castelão, numa vinha comercial localizada na região de Lisboa, com base em imagens recolhidas, com um robô autónomo, o VINBOT. As imagens foram recolhidas em três estados fenológicos, bago de ervilha, pintor e cacho maduro. Para além das imagens recolhidas com o VINBOT, foram também capturadas imagens destrutivas em segmentos adjacentes para construção dos modelos para estimar a área de cachos após desfolha e posteriormente estimar o peso dos cachos. Foram ainda calculados os fatores de crescimento do cacho do bago de ervilha à vindima e do pintor à vindima e analisadas as oclusões de cachos por outros cachos. Os dados recolhidos com o VINBOT permitiram caraterizar a variabilidade da parcela e estimar a sua produção, com erros estimados de 23%, 60% e 11%, para os estados fenológicos bago de ervilha, pintor e cacho maduro, respetivamente. Por outro lado, verificou-se que, os valores estimados e observados para cada um dos 72 segmentos, em cada um dos estados fenológicos, seguem a mesma tendência. Os resultados obtidos mostram que esta pode ser uma alternativa viável aos métodos tradicionais de estimativa da produção, principalmente num momento próximo da vindimaN/