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From deepest of heart containing the warm pleasuring well wishes we are feeling very lucky and honoured to present you the thoroughly revised, willingly prepared and studied with high efforts, the first edition of.We hope the book will become helpful to all the readers of this book who have taken it as a source of knowledge what they seek for. The book is written with immense hard work; dedication and desperation. We have tried to put all the information available to me on these topics for the readers and tried to make it as easy as possible for the easy and correct understanding of the topics by readers. The book is written with dedicated practices of restless work with determination and passion for the writing of a book helpful on this subject. The book is containing the information mostly for the students but we believe that it can also be helpful for everyone.The book is consisting 7 Units al together in it. The unit 1 is focusing on the topic History of Development of Microbiology. The unit 2 is containing the about Diversity of Microorganisms. Unit 3 is filled with the Information about the Different types of Microscopy. The details on the Different types of sterilization technique is described in unit 4. Microbes in Human Health (Medical Microbiology and Immunology) & Environmental Microbiology is widely discussed in unit 5. The unit 6 contains about Industrial Microbiology or Microbes used in Industry. The Unit 7 is discussed about the Food and Dairy Microbiology.We are thankful to the publishers for the speedy and quality production. We shall welcome the constructive suggestion, if any, from the reader.

This piece addresses the political dimension of sustainability in the agricultural bioeconomy by focusing on power, participation, and property rights around key technologies. Bioeconomy policies aim to establish economic systems based on renewable resources such as plants and microorganisms to reduce dependence on fossil resources. To achieve this, they rely on economic growth and increased biomass production through high-tech innovations. This direction has sparked important critique of the environmental and social sustainability of such projects. However, little attention has been paid in the bioeconomy literature to the political dimension surrounding key precision technologies such as data-driven precision agriculture (PA) or precision breeding technologies using new genomic techniques (NGT). The political dimension includes questions of power, participation, and property rights regarding these technologies and the distribution of the benefits and burdens they generate. This lack of attention is particularly pertinent given the recurring and promising claims that precision technologies not only enhance environmental sustainability, but also contribute to the democratization of food and biomass production. This contribution addresses this claim in asking whether we can really speak of a democratization of the agricultural bioeconomy through these precision technologies. Drawing on (own) empirical research and historical evidence, it concludes that current patterns are neither driving nor indicative of a democratization. On the contrary, corporate control, unequal access, distribution, and property rights over data and patents point to few gains for small firms and breeders, but to a reproduction of farmers' dependencies, and less transparency for consumers., Peer Reviewed

Agriculture is a set of economic activities centered on the cultivation of the land and the treatment of the soil for the production of foodstuffs such as food, fiber and other essential goods, covering a vast area of the planet. Technology has become an essential tool for transforming the environment and improving the quality of life. Precision agriculture is an integral part of the agricultural revolution, using technologies such as satellites, drones, sensors and GPS to optimize agronomic processes and improve decision making. In Ecuador, it has gained relevance in sectors such as floriculture, banana and sugar companies, where it is used for automated irrigation, pest control and condition monitoring. In addition, the use of precision agriculture in times of climatic crises, such as the El Niño phenomenon, stands out. Ingenio San Carlos exemplifies the successful application of precision agriculture by subdividing its agricultural areas to adapt management to individual soil characteristics. In general, precision agriculture is consolidated as a profitable and sustainable strategy for the agricultural future, focusing on improving the use of natural resources to maximize the results in agricultural production., La agricultura es un conjunto de actividades económicas centradas en el cultivo de la tierra y el tratamiento del suelo para la producción de alimentos tales como víveres, fibras y otros bienes esenciales, abarcando una extensa área en el planeta. La tecnología, se ha convertido en una herramienta esencial para transformar el entorno y mejorar la calidad de vida. La agricultura de precisión, es parte integral de la revolución agrícola, utiliza tecnologías como satélites, drones, sensores y GPS para optimizar procesos agronómicos y mejorar la toma de decisiones. En Ecuador, ha ganado relevancia en sectores como la floricultura, bananeras y empresas azucareras, donde se emplea para riegos automatizados, control de plagas y monitoreo de condiciones. Además, se destaca el uso de la agricultura de precisión en momentos de crisis climáticas, como el Fenómeno del Niño. El Ingenio San Carlos ejemplifica la aplicación exitosa de la agricultura de precisión al subdividir sus áreas agrícolas para adaptar el manejo a las características individuales del suelo. En general, la agricultura de precisión se consolida como una estrategia rentable y sostenible para el futuro agrícola, enfocándose en el perfeccionamiento del uso de recursos naturales para maximizar los resultados en la producción agrícola.

This article shows the details of the design and implementation of a wireless sensor network (WSN) system, through the use of an Arduino prototyping platform and Lora communication modules, to collect soil humidity, temperature, and PH data in a fruit crop. Data is captured and stored to generate a time series of data to improve decision-making when variation in the essential nutrient application was required. The case study was a parcel in the village of Piedra Larga, in the municipality of Ciénega - Boyacá, where the WSN was deployed that collects the data and allow a visual representation to compare with reference levels and determine the nutrient level requirements. An irrigation monitoring system is implemented by applying artificial intelligence to assist the farmer with two key tasks: i) the activation of the drip irrigation system seeking the efficient use of water, and ii) improving fruit production by controlling the percentage of nutrients. The mobile application shows real-time data monitoring of environmental and soil variables, for the analysis of results and the concentrations of the nutrient mixture together with the drip control to be applied to the crop. An optimal estimation of the required nutrient concentrations was estimated from a neural network to simplify and improve the efficiency of the farmer’s agricultural activities, such as saving water consumption by 40% and improving fruit production by up to a 30% ., En este artículo se muestran los detalles del diseño e implementación de un sistema de red inalámbrica de sensores (WSN), mediante el uso de una plataforma de prototipos Arduino y módulos de comunicación Lora, para recolectar datos de humedad, temperatura y PH del suelo en un cultivo frutal. Se captura y almacena los datos para generar una serie temporal de datos que permita mejorar la toma de decisiones cuando se requerían variación en la aplicación nutrientes esenciales. El caso de estudio fue una parcela en la Vereda de Piedra Larga, en el municipio de Ciénega - Boyacá, donde se desplegó la WSN que recolecta los datos y permite una representación visual para comparar con niveles de referencia y determinar los requerimientos a nivel de nutrientes. Se implementa un sistema de monitoreo de riego aplicando inteligencia artificial para asistir al agricultor con dos tareas clave: i) la activación del sistema de riego por goteo buscando el uso eficiente del agua, y ii) mejorar la producción de frutos controlando el porcentaje de nutrientes. La aplicación móvil muestra datos en tiempo real monitoreo de variables ambientales y del suelo, para el análisis de resultados y las concentraciones de mezcla de nutrientes junto al control por goteo a aplicar al cultivo. Se realizó una estimación óptima de las concentraciones requeridas de nutrientes a partir de una red neuronal para simplificar y mejorar la eficiencia en las actividades agrícolas del agricultor como ahorro en el consumo de agua en un 40% y mejora de la producción de frutas hasta en un 30%.

The adoption of advanced agricultural practices in Serbia represents a significant leap towards sustainability in the sector. Through precision nitrogen fertilization and reduced tillage, a notable 32% enhancement in energy efficiency within cropping systems is feasible, reducing energy consumption per unit of food produced from 5 GJ to 3.4 GJ per hectare, alongside a tangible decrease in greenhouse gas emissions, thus bolstering efforts to combat climate change. Embracing these integrated techniques in Serbia and beyond holds promise in addressing the imperative to augment agricultural productivity while safeguarding ecological balance, amid projections of global mineral fertilizer consumption reaching 200 million tons by 2025. Advocating for the adoption of organic fertilizers in tandem with precision agriculture technologies offers further potential for mitigating adverse environmental impacts, bolstering soil health, biodiversity, and optimizing resource allocation. Moreover, these practices can lead to significant economic efficiency gains, reducing production costs and increasing overall profitability in agricultural operations. Additionally, investing in sustainable agriculture can create new job opportunities and stimulate economic growth in rural areas. Energy efficiency emerges as a cornerstone in modern agriculture, with the transition to integrated and low-intensity farming systems poised to slash energy usage by over 60%, making a significant contribution to overall energy conservation. Achieving these objectives necessitates a thorough assessment of existing agricultural practices in Serbia, alongside collaborative efforts from governmental bodies, agricultural experts, and farmers, underpinned by adequate funding and training. With the right approach and support, Serbia has the potential to emerge as a regional leader in sustainable agriculture, charting a path towards a productive, environmentally conscious, and economically resilient future.

The adoption of advanced agricultural practices in Serbia represents a significant leap towards sustainability in the sector. Through precision nitrogen fertilization and reduced tillage, a notable 32% enhancement in energy efficiency within cropping systems is feasible, reducing energy consumption per unit of food produced from 5 GJ to 3.4 GJ per hectare, alongside a tangible decrease in greenhouse gas emissions, thus bolstering efforts to combat climate change. Embracing these integrated techniques in Serbia and beyond holds promise in addressing the imperative to augment agricultural productivity while safeguarding ecological balance, amid projections of global mineral fertilizer consumption reaching 200 million tons by 2025. Advocating for the adoption of organic fertilizers in tandem with precision agriculture technologies offers further potential for mitigating adverse environmental impacts, bolstering soil health, biodiversity, and optimizing resource allocation. Moreover, these practices can lead to significant economic efficiency gains, reducing production costs and increasing overall profitability in agricultural operations. Additionally, investing in sustainable agriculture can create new job opportunities and stimulate economic growth in rural areas. Energy efficiency emerges as a cornerstone in modern agriculture, with the transition to integrated and low-intensity farming systems poised to slash energy usage by over 60%, making a significant contribution to overall energy conservation. Achieving these objectives necessitates a thorough assessment of existing agricultural practices in Serbia, alongside collaborative efforts from governmental bodies, agricultural experts, and farmers, underpinned by adequate funding and training. With the right approach and support, Serbia has the potential to emerge as a regional leader in sustainable agriculture, charting a path towards a productive, environmentally conscious, and economically resilient future.

Webinar 26 april 2024.

Presentatie bij webinar Circulaire precisiebemesting in een stroomversnelling : renure in de aardappelteelt : webinar 16 augustus 2024.

PowerPoint-presentatie met factsheets bij webinar Circulaire precisiebemesting in een stroomversnelling : renure in de aardappelteelt : webinar 16 augustus 2024.

Recente initiatieven aan de aanbod- en vraagzijde van de markt voor plantenvoeding zullen de adoptie van de circulaire precisiebemesting aanzienlijk versnellen. Dit webinar infrormeert u over de majeure consequenties daarvan voor de bemestingspraktijk en over de positieve effecten daarvan op emissiereductie door de landbouw, of het nu gaat om CO2, om nitraat of om de stikstofdeken. Het webinar vond plaats op vrijdag 16 augustus van 13.30 uur tot 15.00 uur onder auspiciën van De Boer aan het Roer.

The “EscaYard” dataset comprises multimodal data collected from vineyards to support agricultural research, specifically focusing on vine health and productivity. Data collection involved two primary methods: (1) unmanned aerial vehicle (UAV) for capturing multispectral images and 3D point clouds, and (2) smartphones for detailed ground-level photography. The UAV used was DJI Matrice 210 V2 RTK, equipped with a Micasense Altum sensor, flying at 30 m above ground level to ensure detailed coverage. Ground-level data were collected using smartphones (iPhone X and Xiaomi Poco X3 Pro), which provided high-resolution images of individual plants. These images were geotagged, enabling location mapping, and included data on the phytosanitary status and number of grape clusters per plant. Additionally, the dataset contains RTK GNSS data, offering high-precision location information for each vine, enhancing the dataset's value for spatial analysis. Moreover, the dataset is structured to support various research applications, including agronomy, remote sensing, and machine learning. It is particularly suited for studying disease detection, yield estimation, and vineyard management strategies. The high-resolution and multispectral nature of the data allows for a detailed analysis of vineyard conditions. Potential reuse of the dataset spans multiple disciplines, enabling studies on environmental monitoring, geographic information systems (GIS), and precision agriculture. Its comprehensive nature makes it a valuable resource for developing and testing algorithms for disease classification, yield prediction, and plant phenotyping. For instance, the images of bunches and grape leaves can be used to train object detection algorithms for accurate disease detection and consequent precise spraying. Moreover, yield prediction algorithms can be trained by extracting the phenotypic traits of the grape bunches. The “EscaYard” dataset provides a foundation for advancing research in sustainab

Innovations in precision agriculture enhance complex tasks, reduce environmental impact, and increase food production and cost efficiency. One of the main challenges is ensuring rapid information availability for autonomous vehicles and standardizing processes across platforms to maximize interoperability. The lack of drone technology standardisation, communication barriers, high costs, and post-processing requirements sometimes hinder their widespread use in agriculture. This research introduces a standardized data fusion framework for creating real-time spatial variability maps using images from different Unmanned Aerial Vehicles (UAVs) for Site-Specific Crop Management (SSM). Two spatial interpolation methods were used (Inverse Distance Weight, IDW, and Triangulated Irregular Networks, TIN), selected for their computational efficiency and input flexibility. The proposed framework can use different UAV image sources and offers versatility, speed, and efficiency, consuming up to 98 % less time, energy, and computing requirements than standard photogrammetry techniques, providing rapid field information, allowing edge computing incorporation into the UAV data acquisition phase. Experiments conducted in Spain, Serbia, and Finland in 2022 under the H2020 FlexiGroBots project demonstrated a strong correlation between results from this method and those from standard photogrammetry techniques (up to r = 0.93). In addition, the correlation with Sentinel 2 satellite images was as strong as that obtained with photogrammetry-based orthomosaics (up to r = 0.8). The proposed approach could support irrigation leak detection, soil parameter estimation, weed management, and satellite integration for agriculture.

One of the basic indicators of topsoil properties is the color of the surface horizon. An-thropogenic denudation in moraine plateaus of Northern Poland leads to strong transformation of the soil cover of agricultural areas, which affects strong differences in the color of plowing horizons. This variability is likely visible in low-altitude photogrammetry data, allowing for detailed mapping and analysis of soil conditions. The continued evolution of precision agriculture, driven by innova-tions like drone technology, holds great promise for optimizing resource use, increasing crop yields, and promoting environmental sustainability in the agricultural sector. A modern research approach along with classic laboratory analyzes allows to determine correlation between color of plowing horizons and properties of soil in hummocky and undulating moraine plateaus with varying types of water regime

This study addresses the issue of black-grass, a herbicide-resistant weed that threatens wheat yields in Western Europe, through the use of high- resolution Unmanned Aerial Vehicles (UAVs) and synthetic data augmentation in precision agriculture. We mitigate challenges such as the need for large labeled datasets and environmental variability by employing synthetic data augmentations in training a Mask R-CNN model. Using a minimal dataset of 43 black-grass and 12 wheat field images, we achieved a 37% increase in Area Under the Curve (AUC) over the non-augmented baseline, with scaling as the most effective augmentation. The best model attained a recall of 53% at a precision of 64%, offering a promising approach for future precision agriculture applications. © NLDL 2024. All rights reserved.

Producción Científica, Weeds are unwanted and invasive plants that proliferate and compete for resources such as space, water, nutrients, and sunlight, affecting the quality and productivity of the desired crops. Weed detection is crucial for the application of precision agriculture methods and for this purpose machine learning techniques can be used, specifically convolutional neural networks (CNN). This study focuses on the search for CNN architectures used to detect and identify weeds in different crops; 61 articles applying CNN architectures were analyzed during the last five years (2019–2023). The results show the used of different devices to acquire the images for training, such as digital cameras, smartphones, and drone cameras. Additionally, the YOLO family and algorithms are the most widely adopted architectures, followed by VGG, ResNet, Faster R-CNN, AlexNet, and MobileNet, respectively. This study provides an update on CNNs that will serve as a starting point for researchers wishing to implement these weed detection and identification techniques, Union Europea, Programa Horizonte - (project HORIZON-CL6-2022-FARM2FORK-01)

The research presented in this thesis focuses on an innovative use of digital imaging, and the machine learning techniques to assess the body rumen fill scoring in dairy cows. This study aims to enhance the efficiency of monitoring and managing dairy cow health, which is crucial for the dairy industry's productivity and sustainability. The primary objective was to develop an automated annotation system fore valuating rumen fill status in dairy cows using digital images extracted from recorded videos. This system leverages advanced machine learning algorithms and neural networks, aiming to mimic manual assessments by veterinarians and specialists on farms. To achieve the above objectives, this thesis made use of already existing video records from a Swedish dairy farm hosting mainly the Swedish Redand the Swedish Holstein breeds. A subset of these images were then processed, manually classified using a modified rumen fill scoring system based on visual assessment, and supervised classification algorithms were trained on 277 manually annotated images. The thesis explored various machine learning techniques for classifying these images, including Logistic Regression, Support Vector Machine (SVM), and a Deep Neural Network using the VGG16 architecture. These models were trained, validated, and tested with a dataset that included variations in cow color patterns, aiming to determine the most effective approach for automated rumen fill scoring.The results indicated that while each model had its strengths and weaknesses, the simple logistic model was performing the best in terms of test accuracy and F1 score. This research contributes to the field of precision livestock farming, particularly in the context of dairy farming. By automating the process of rumen fill scoring, the study aims to provide dairy farmers with a reliable, efficient, and cost-effective tool for monitoring cow health. This tool has the potential to enhance dairy cow welfare, improve milk production, an

Precision agriculture relies on understanding crop growth dynamics and plant responses to short-term changes in abiotic factors. In this technical note, we present and discuss a technical approach for cost-effective, non-invasive, time-lapse crop monitoring that automates the process of deriving further plant parameters, such as biomass, from 3D object information obtained via stereo images in the red, green, and blue (RGB) color space. The novelty of our approach lies in the automated workflow, which includes a reliable automated data pipeline for 3D point cloud reconstruction from dynamic scenes of RGB images with high spatio-temporal resolution. The setup is based on a permanent rigid and calibrated stereo camera installation and was tested over an entire growing season of winter barley at the Global Change Experimental Facility (GCEF) in Bad Lauchstädt, Germany. For this study, radiometrically aligned image pairs were captured several times per day from 3 November 2021 to 28 June 2022. We performed image preselection using a random forest (RF) classifier with a prediction accuracy of 94.2% to eliminate unsuitable, e.g., shadowed, images in advance and obtained 3D object information for 86 records of the time series using the 4D processing option of the Agisoft Metashape software package, achieving mean standard deviations (STDs) of 17.3–30.4 mm. Finally, we determined vegetation heights by calculating cloud-to-cloud (C2C) distances between a reference point cloud, computed at the beginning of the time-lapse observation, and the respective point clouds measured in succession with an absolute error of 24.9–35.6 mm in depth direction. The calculated growth rates derived from RGB stereo images match the corresponding reference measurements, demonstrating the adequacy of our method in monitoring geometric plant traits, such as vegetation heights and growth spurts during the stand development using automated workflows.

Este trabajo se ha centrado en mejorar la precisión y la sincronización espaciotemporal en el transplante de tomates, usando una transplantadora de cultivos modificada. La metodología incluye el análisis de visión por ordenador digital en color y de alta velocidad para estudiar el movimiento y la dinámica de las plantas de tomates transplantados. Este análisis condujo al desarrollo de un mecanismo de soporte de planta, que resultó en una reducción significativa, del 25%, de la desviación en la ubicación de las plantas en el suelo, en comparación con métodos convencionales. Esta mejorar es un paso importante hacia un sistema mectrónico capaz de transplatar cultivos en un patrón de cuadrícula a lo largo de la línea de cultivo. Este sistema serí un componente clave en un enfoque de tratamiento individualizado por planta y totalmente automatizado, lo cual es muy relevante para cultivos intensivos como el tomate. Los conjuntos de datos generados en este estudio, que se realizaron entre 2012 y 2014, se basaron en un sistema de control de retroalimentación cerrada para regular la velocidad angular de la rueda de plantación. Esta velocidad se ajustó de acuerdo a la detección de la velocidad angular de una rueda sin motor, empleando para ello un codificador óptico de rotación de eje. El archivo suministrado, un documento de Excel, incluye tres pestañas: "Velocity&Aceleration" (Velocidad y Aceleración), "Height&Diameter" (Altura y Diámetro), y "Supported&Control" (Soporte y Control). Este archivo contiene información numérica detallada que respalda y valida los resultados obtenidos en la investigación.

Met de afschaffing van de derogatie en de daaruit voortvloeiende verlaagde stikstof gebruiksnormen, ligt er een uitdaging voor de veehouder en akkerbouwer om de hectare- opbrengsten te behouden. Dit vraagt onder andere om vernieuwde bodem- en bemestingsstrategieën. Denk bijvoorbeeld aan kunstmestvervangers. Precisiebemesting met de 4 juistheden is meer dan ook van belang. De vier juistheden zijn: juiste plaats, juiste meststof, juiste moment en juiste dosering. Met behulp van deze opdracht leer je hier meer over. Ook maak je kennis met voorbeelden uit de praktijk.

De bemestingsnormen voor mais worden steeds scherper. Precisiebemesting kan helpen om met minder mestgift toch voldoende opbrengst van een perceel te halen en duurzaamheidsdoelen te halen. Mest goed doseren naar gewasbehoefte is essentieel: niet te weinig, maar ook niet te veel.

Precision agriculture is often seen as disembodied and placeless, promised to either bring about a fourth agricultural revolution or as the start of dystopian rural futures, where farmers and their knowledge will be replaced by machines. A growing body of literature shows more nuanced ways of working with precision agriculture. This illustrates the need to investigate how, and under which conditions, precision agriculture is integrated and internalised by farmers. Based on 25 in-depth interviews with male Dutch crop farmers, contractors, researchers, and ag-tech developers, we develop the concept of the cyborg farmer, who embodies the use of precision technology while maintaining an intimate relationship with agro-ecological context. This approach challenges the mind–body dichotomy in that the cognitive is not understood as primary, as emotions and the materiality of the body are taken seriously. This perspective emphasises the importance of embodied knowledge in how farmers interpret precision agriculture data. Our findings highlight the productive tension between data-driven elements of precision agriculture and the embodied and intuitive understanding of agro-ecological context. This productive tension consists of the successful integration of different forms of knowledge by the cyborg farmer, where embodied sense-making and precision agriculture data are integrated into the formation of useable knowledge. The cyborg farmer maintains farmer agency and resists the dominance of algorithmic rationality over other forms of knowledge.

This study addresses the issue of black-grass, a herbicide-resistant weed that threatens wheat yields in Western Europe, through the use of high- resolution Unmanned Aerial Vehicles (UAVs) and synthetic data augmentation in precision agriculture. We mitigate challenges such as the need for large labeled datasets and environmental variability by employing synthetic data augmentations in training a Mask R-CNN model. Using a minimal dataset of 43 black-grass and 12 wheat field images, we achieved a 37% increase in Area Under the Curve (AUC) over the non-augmented baseline, with scaling as the most effective augmentation. The best model attained a recall of 53% at a precision of 64%, offering a promising approach for future precision agriculture applications. © NLDL 2024. All rights reserved.

Chemical pesticide delivery is a fundamental aspect of agriculture. However, the extensive use of pesticides severely endangers the ecosystem because they accumulate on crops, in soil, as well as in drinking and groundwater. New frontiers in nano-engineering have opened the door for precision agriculture. We introduced Tobacco mild green mosaic virus (TMGMV) as a viable delivery platform with a high aspect ratio and favorable soil mobility. In this work, we assess the use of TMGMV as a chemical nanocarrier for agriculturally relevant cargo. While plant viruses are usually portrayed as rigid/solid structures, these are "dynamic materials," and they "breathe" in solution in response to careful adjustment of pH or bathing media [e.g., addition of solvent such as dimethyl sulfoxide (DMSO)]. Through this process, coat proteins (CPs) partially dissociate leading to swelling of the nucleoprotein complexes-allowing for the infusion of active ingredients (AI), such as pesticides [e.g., fluopyram (FLP), clothianidin (CTD), rifampicin (RIF), and ivermectin (IVM)] into the macromolecular structure. We developed a "breathing" method that facilitates inter-coat protein cargo loading, resulting in up to ~ 1000 AIs per virion. This is of significance since in the agricultural setting, there is a need to develop nanoparticle delivery strategies where the AI is not chemically altered, consequently avoiding the need for regulatory and registration processes of new compounds. This work highlights the potential of TMGMV as a pesticide nanocarrier in precision farming applications; the developed methods likely would be applicable to other protein-based nanoparticle systems.

The research presented in this thesis focuses on an innovative use of digital imaging, and the machine learning techniques to assess the body rumen fill scoring in dairy cows. This study aims to enhance the efficiency of monitoring and managing dairy cow health, which is crucial for the dairy industry's productivity and sustainability. The primary objective was to develop an automated annotation system fore valuating rumen fill status in dairy cows using digital images extracted from recorded videos. This system leverages advanced machine learning algorithms and neural networks, aiming to mimic manual assessments by veterinarians and specialists on farms. To achieve the above objectives, this thesis made use of already existing video records from a Swedish dairy farm hosting mainly the Swedish Redand the Swedish Holstein breeds. A subset of these images were then processed, manually classified using a modified rumen fill scoring system based on visual assessment, and supervised classification algorithms were trained on 277 manually annotated images. The thesis explored various machine learning techniques for classifying these images, including Logistic Regression, Support Vector Machine (SVM), and a Deep Neural Network using the VGG16 architecture. These models were trained, validated, and tested with a dataset that included variations in cow color patterns, aiming to determine the most effective approach for automated rumen fill scoring.The results indicated that while each model had its strengths and weaknesses, the simple logistic model was performing the best in terms of test accuracy and F1 score. This research contributes to the field of precision livestock farming, particularly in the context of dairy farming. By automating the process of rumen fill scoring, the study aims to provide dairy farmers with a reliable, efficient, and cost-effective tool for monitoring cow health. This tool has the potential to enhance dairy cow welfare, improve milk production, an

Remote sensing (RS) offers great possibilities to acquire data for early determination of crop performance, which is essential for water and nitrogen (N) management. The objective of this study was to assess the nutritional and water status of different wheat genotypes (Triticum aestivum L.) by means of vegetation indices (VIs) derived from ground-level hyperspectral measurements, plant traits retrieved by coupling a radiative transfer (RT) model with a 3-layer neural network, and thermal data. To this end, two field experiments were conducted in central Spain during 2018–2021, using two wheat genotypes (Cellule and Nogal) under three N and two irrigation doses. Pigment and polyphenolic compound measurements were carried out with a Dualex® device, canopy reflectance (400–900 nm) was measured with a handheld spectroradiometer, and thermal indicators were assessed using a thermal camera at different wheat growth stages. The nitrogen nutrition index (NNI) at flowering and the grain yield (GY) and grain N concentration (GNC) at harvest were determined. The spectral reflectance measurements were used to calculate VIs, and the PROSAIL-PRO radiative transfer model was used to retrieve chlorophyll (Chl) content and leaf area index (LAI). In addition, the water deficit index (WDI) and canopy–air temperature difference (Tc-Ta) were used to assess the water status of wheat. Our results show that predicted Chl and VIs built with red-edge bands obtained the best agreement with NNI (R2 ≥ 0.60 at flowering in Cellule and R2 = 0.60 at medium milk in Nogal) and GNC (R2 ≥ 0.45 and 0.38 in Cellule and Nogal respectively). The GY was best assessed with VIs combining near infrared (NIR) / visible bands and NIR / red-edge bands (R2 ≥ 0.74 and RMSE ≤ 903 kg ha-1 at watery ripe in Cellule; R2 ≥ 0.56 and RMSE ≤ 791 kg ha-1 at the end of stem elongation in Nogal) than by the predicted plant traits. The best differentiation between genotype performance was achieved by predicted Chl and LAI re

Neem grondanalyses per perceel, voer deze in en creëer op basis daarvan de precies passende vloeibare ureummeststof voor het volgende groeiseizoen. En dan niet voor hoeveelheden van minimaal 30 ton, maar afgemeten op de benodigde hoeveelheid tegen een passende kostprijs. Het klinkt te mooi om waar te zijn en toch realiseren we dat hier in Leeuwarden vanaf komend voorjaar', zegt Camiel Hoogland.

Een introductie over Proefbedrijf ‘t Kompas te Valthermond in Drenthe. Er wordt onderzoek gedaan naar: optimalisering benutting vocht/mineralen, natuurinclusieve landbouw, rasvergelijkingen, onkruid- en ziektebestrijding, bemesting en optimalisatie, phytophthora en alternaria in aardappelen, vrijlevende aaltjes (vruchtwisseling), precisielandbouw op plantniveau (robotica) en strokenteelt. Presentatie is onderdeel van Groenblauwe diensten als potentieel verdienmodel voor boer en natuur, het thema tijdens de derde Werkplaats Netwerk Platteland op donderdag 30 mei in Wildlands in Emmen.

This study addresses the issue of black-grass, a herbicide-resistant weed that threatens wheat yields in Western Europe, through the use of high- resolution Unmanned Aerial Vehicles (UAVs) and synthetic data augmentation in precision agriculture. We mitigate challenges such as the need for large labeled datasets and environmental variability by employing synthetic data augmentations in training a Mask R-CNN model. Using a minimal dataset of 43 black-grass and 12 wheat field images, we achieved a 37% increase in Area Under the Curve (AUC) over the non-augmented baseline, with scaling as the most effective augmentation. The best model attained a recall of 53% at a precision of 64%, offering a promising approach for future precision agriculture applications. © NLDL 2024. All rights reserved.

In an effort to reduce pesticide use, agronomists and computer scientists have joined forces to develop site-specific weed detection and classification systems. These systems aim to recognize and locate weed species within a crop field, using precision equipment to apply required herbicides timely and only where needed, with the objective of reducing the sprayable surface required to eliminate the given weed and protect the crop, with both economic and environmental benefits. Yet, with climate change on the rise, common weeds are expected to undergo some changes to adapt to their environment, possibly with new or invasive weeds spreading to areas where they did not exist before. These changes (often morphological) as well as new invasions need to be taken into account by future classifiers and detection algorithms to ensure system robustness and adaptation to new habitats/climate dynamics. This paper proposes a set of experiments evaluating the use of transfer learning and zero-shot learning for weed classification using our novel TomatoWeeds dataset. Residual networks of variable depth, pretrained on the Imagenet and/or DeepWeeds datasets were evaluated. A ResNet50 pretrained on both datasets and fine-tuned on the TomatoWeeds dataset performed best, returning a holdout set accuracy of 77.8%, showing the advantageous use of transfer learning in this domain. Zero-shot learning, using both embeddings of images and morphological and habitat text-based descriptions, is implemented to test the ability of machine learning pipelines of recognizing unseen classes at test time (which may arise e.g. due to changing climate dynamics), a learning task in which the field (and our experiments) are still far from satisfactory results. Further research could benefit from larger weed-specific datasets for transfer learning as well as deeper network architectures to improve model performance. The projection-based ZSL could also benefit from larger datasets and new zero-shot learning

[EN] Efficient invasive weed management is critical to maintain the balance between biodiversity, production and agricultural sustainability. Invasive plants may cause significant yield losses in crops and their control entails a high cost in pesticide products, in addition to the ecological damage they generate. Advances in robotics, digital agriculture, artificial intelligence, etc, allow the inputs’ optimization (e.g. pesticides) through timely and site-specific treatments tailored to the real crop needs, aligned with the principles of precision agriculture. Amaranthus palmeri has been recently detected in Spanish crop fields. It is an extremely competitive species, with a high fertility rate and skilled at developing herbicide-resistant biotypes, which poses a serious threat to Spanish agriculture. Therefore, a digital agriculture tool -called DIGINVASIVE- is being designed for mapping and monitoring of A. palmeri in crops, which consists of an open-source web-based interface system using innovative digital technologies such as drone imagery, Geographic Information Systems, Information and Communication Technologies and powerful Artificial Intelligence algorithms. This interactive tool will allow users to establish suitable prevention and eradication measures to address the control of A. palmeri in agricultural environments., [ES] La gestión eficiente de las malas hierbas invasoras es fundamental para mantener el equilibrio entre biodiversidad, producción y sostenibilidad agrícola. Esto se debe a que las malas hierbas invasoras pueden causar pérdidas significativas de rendimiento en los cultivos y su control supone un elevado coste en productos fitosanitarios, además de los daños ecológicos que generan. Los avances en robótica, digitalización, inteligencia artificial, etc., permiten la optimización de los recursos empleados (e.g. fitosanitarios) mediante la aplicación de tratamientos localizados y ajustados a las necesidades reales del cultivo siguiendo los principios de la agricultura de precisión. Recientemente se ha confirmado la presencia de la planta invasora Amaranthus palmeri en parcelas agrícolas en España. Esta especie es altamente competitiva, presenta una elevada tasa de fecundidad y la capacidad para desarrollar biotipos resistentes a los herbicidas, convirtiéndola en una de las especies exóticas con mayor amenaza para la agricultura española. Para ello, se ha desarrollado una herramienta de agricultura digital -denominada DIGINVASIVE- para el mapeo y seguimiento de A. palmeri en campos agrícolas, consistentes en un servidor de cartografía digital web de código abierto que utiliza tecnologías digitales innovadoras como imágenes de dron, Sistemas de Información Geográfica, Tecnologías de la Información y la Comunicación y potentes algoritmos de Inteligencia Artificial. Esta herramienta interactiva permitirá a los usuarios establecer las medidas de prevención y erradicación necesarias para abordar el control de A. palmeri en escenarios agrícolas.

During the growing season, olives progress through nine different phenological stages, starting with bud development and ending with senescence. During their lifespan, olives undergo changes in their external color and chemical properties. To tackle these properties, we used hyperspectral imaging during the growing season of the olives. The objective of this study was to develop a lightweight model capable of identifying olives in the hyperspectral images using their spectral information. To achieve this goal, we utilized the hyperspectral imaging of olives while they were still on the tree and conducted this process throughout the entire growing season directly in the field without artificial light sources. The images were taken on-site every week from 9:00 to 11:00 a.m. UTC to avoid light saturation and glitters. The data were analyzed using training and testing classifiers, including Decision Tree, Logistic Regression, Random Forest, and Support Vector Machine on labeled datasets. The Logistic Regression model showed the best balance between classification success rate, size, and inference time, achieving a 98% F1-score with less than 1 KB in parameters. A reduction in size was achieved by analyzing the wavelengths that were critical in the decision making, reducing the dimensionality of the hypercube. So, with this novel model, olives in a hyperspectral image can be identified during the season, providing data to enhance a farmer’s decision-making process through further automatic applications.

This study addresses the issue of black-grass, a herbicide-resistant weed that threatens wheat yields in Western Europe, through the use of high- resolution Unmanned Aerial Vehicles (UAVs) and synthetic data augmentation in precision agriculture. We mitigate challenges such as the need for large labeled datasets and environmental variability by employing synthetic data augmentations in training a Mask R-CNN model. Using a minimal dataset of 43 black-grass and 12 wheat field images, we achieved a 37% increase in Area Under the Curve (AUC) over the non-augmented baseline, with scaling as the most effective augmentation. The best model attained a recall of 53% at a precision of 64%, offering a promising approach for future precision agriculture applications. © NLDL 2024. All rights reserved.

This study addresses the issue of black-grass, a herbicide-resistant weed that threatens wheat yields in Western Europe, through the use of high- resolution Unmanned Aerial Vehicles (UAVs) and synthetic data augmentation in precision agriculture. We mitigate challenges such as the need for large labeled datasets and environmental variability by employing synthetic data augmentations in training a Mask R-CNN model. Using a minimal dataset of 43 black-grass and 12 wheat field images, we achieved a 37% increase in Area Under the Curve (AUC) over the non-augmented baseline, with scaling as the most effective augmentation. The best model attained a recall of 53% at a precision of 64%, offering a promising approach for future precision agriculture applications. © NLDL 2024. All rights reserved.

This study addresses the issue of black-grass, a herbicide-resistant weed that threatens wheat yields in Western Europe, through the use of high- resolution Unmanned Aerial Vehicles (UAVs) and synthetic data augmentation in precision agriculture. We mitigate challenges such as the need for large labeled datasets and environmental variability by employing synthetic data augmentations in training a Mask R-CNN model. Using a minimal dataset of 43 black-grass and 12 wheat field images, we achieved a 37% increase in Area Under the Curve (AUC) over the non-augmented baseline, with scaling as the most effective augmentation. The best model attained a recall of 53% at a precision of 64%, offering a promising approach for future precision agriculture applications. © NLDL 2024. All rights reserved.

Object Detection and Tracking have gained importance in recent years because of the great advances in image and video analysis techniques and the accurate results these technologies are producing. Moreover, they have successfully been applied to multiple fields, including the agricultural domain since they offer real-time monitoring of the status of the crops and animals while counting how many are present within a field/barn. This review aims to review the current literature on Object Detection and Tracking within the field of Precision Farming. For that, over 300 research articles were explored, from which 150 articles from the last five years were systematically reviewed and analysed regarding the algorithms they implemented, the domain they belong to, the difficulties they faced, and which limitations should be tackled in the future. Lastly, it examines potential issues that this approach might have, for instance, the lack of open-source datasets with labelled data. The findings of this study indicate that Object Detection and Tracking are critical techniques to enhance Precision Farming and pave the way for robotization for the agricultural sector since they provide accurate results and insights on crop and animal management, and optimize resource allocation. Future work should focus on the optimal acquisition of the datasets prior to Object Detection and Tracking, along with the consideration of the biophysical environment of the farming scenarios.

Site-specific estimation of lime requirement requires high-resolution maps of soil organic carbon (SOC), clay and pH. These maps can be generated with digital soil mapping models fitted on covariates observed by proximal soil sensors. However, the quality of the derived maps depends on the applied methodology. We assessed the effects of (i) training sample size (5–100); (ii) sampling design (simple random sampling (SRS), conditioned Latin hypercube sampling (cLHS) and k-means sampling (KM)); and (iii) prediction model (multiple linear regression (MLR) and random forest (RF)) on the prediction performance for the above mentioned three soil properties. The case study is based on conditional geostatistical simulations using 250 soil samples from a 51 ha field in Eastern Germany. Lin’s concordance correlation coefficient (CCC) and root-mean-square error (RMSE) were used to evaluate model performances. Results show that with increasing training sample sizes, relative improvements of RMSE and CCC decreased exponentially. We found the lowest median RMSE values with 100 training observations i.e., 1.73%, 0.21% and 0.3 for clay, SOC and pH, respectively. However, already with a sample size of 10, models of moderate quality (CCC > 0.65) were obtained for all three soil properties. cLHS and KM performed significantly better than SRS. MLR showed lower median RMSE values than RF for SOC and pH for smaller sample sizes, but RF outperformed MLR if at least 25–30 or 75–100 soil samples were used for SOC or pH, respectively. For clay, the median RMSE was lower with RF, regardless of sample size.

This dataset contains UAV RGB videos (MP4) recorded with a Phantom4 RTK in a vineyard during the harvesting campaign of 2023. It also includes frames and annotations (PNG) to boost Object Detection and Tracking of grape bunches. There are two types of videos: (1) videos capturing the side of the canopy from a frontal point of view only, and (2) videos that collect the data from multiple perspectives to avoid leaf occlusion, common in commercial vineyards. All flights were executed 3 meters above ground level, with a clear sky and wind speed below 0.5 m/s.

In order to provide students with a practical, practice-oriented text that highlights the industry and the potential of microbes for industrial processes in the future, the Industrial Microbiology book brings together the insight and expertise of top researchers and professionals from across the field of industrial microbiology. The twelve most significant applications for microbial technology are discussed after a brief overview of the technology of microbial processes. These include the use of microbes in the leaching of minerals and the treatment of industrial and municipal waste, as well as the production of highly refined biomolecules like enzymes and antibodies. The field of industrial microbiology studies a variety of cutting-edge microbial technology ideas that have been created to maximize the potential of microorganisms. The book looks at products made from microbes that are widely used in a variety of industries, including agriculture, biorefinery, bioremediation, pharmaceutical, and medical.This text offers a comprehensive introduction to the field of industrial microbiology and is aimed at undergraduates studying the applied aspects of biology, specifically those on biotechnology and microbiology courses and students of food science and biochemical engineering.

An effective variable-rate spraying system for greenhouses requires accurate canopy structure parameters of plants to ensure proper pesticide dosage adjustment. While conventional laser systems integrated into spray systems can provide precise point cloud data of plants, they still present a high expense. This study examines the performance of a recently introduced, cost-effective, and high-resolution solid-state LiDAR (Intel RealSense L515) in relation to its potential for greenhouse spray applications. Additionally, a specialized point cloud acquisition algorithm was developed for this solid-state LiDAR to obtain the geometrical parameters of objects. To assess the LiDAR sensor's suitability for greenhouse spray applications, the performance of the LiDAR sensor and the algorithm was evaluated using five different sized regular-shaped cartons and three artificial plants with complex geometry. Various factors were analyzed, such as the horizontal distances between objects and the LiDAR sensor (0.5, 1.0, 1.5, 2.0, 2.5, and 3.0 m), the tilt angle of the LiDAR sensor relative to the ground (45°, 60°, and 75°), the height of the LiDAR sensor from the ground (ranging from 0.3 to 0.8 m with 0.5 m distance intervals), and the forward speed of the LiDAR sensor (0.1, 0.3, 0.6, and 0.9 m s-1). The findings revealed that the optimal detection distance for this LiDAR sensor is 1.0 m. Increasing or decreasing the detection distance of the object relative to the LiDAR sensor diminished the measurement accuracy. The accuracy of the derived geometrical variables was affected by the height and tilt angle of the LiDAR sensor. Nevertheless, the geometrical parameters obtained from the solid-state LiDAR showed a favorable correspondence with the results of manual measurements. The highest root mean square error (RMSE) and coefficient of variation (CV) for the overall test were 14.3 mm and 14.3% in the X (length) direction, 14.3 mm and 14.3% in the Y (width) direction, and 10.8 mm and 1, The authors acknowledge the experimental assistance from Mr. Shuo Yuan and Mr. Liangliang Zhao. The authors are also grateful to the National Natural Science Foundation of China (Grant numbers 52065031, 32301703 and 51605210), the Youth Talent Think Tank Program of the China Association for Science and Technology (Grant num- ber 2022ZZ041876), and the Research Start-up Program for Introduced Talent of Shanghai Institute of Technology (Grant number YJ2023-17)., Postprint (updated version)

Introduction: The strawberry crop is seriously affected by different diseases that result in a decrease in production and fruit quality. Among the most important diseases that attack the strawberry crop is Mycosphaerella fragariae, the causal agent of leaf spot, which in advanced stages can lead to the total loss of the crop. Monitoring of this disease is a fundamental tool for its prevention and control. The tools of precision agriculture and agriculture 4.0, such as images obtained by drones, facilitate decision making by producers and optimize crop work such as monitoring. Objetive: This study identified the relationship between the percentage of area affected by leaf spot (Mycosphaerella fragaraiae) and the NDVI and NDRE indices. Metodology: The study was developed in the province of Pamplona in Norte de Santander with high inoculum pressure of Mycosphaerella fragariae. Leaflets were collected from strawberry plants with different degrees of affectation caused by Mycosphaerella fragariae, this plant material was transported guaranteeing its conservation until the laboratory, where measurements were made with the Stellar Net brand EPP2000 portable spectroradiometer, from which the values of the red band, infrared and the red border of each one was taken to perform the calculation of several indices including NDVI and NDRE, these values were averaged for each degree of affectation. The exact affected area was calculated for each leaf using the Compu eye leaf and symptom area software and the correlation coefficients were calculated looking for a mutual linear relationship from simple linear regressions. Results: The NDRE index correlates 82% with the area affected by Mycosphaerella fragariae by up to 70%. Conclusión: The percentage of area affected by leaf freckle (Mycosphaerella fragariae) is directly related to the spectral response in terms of NDVI and NDRE indices., Introducción: El cultivo de fresa se ve seriamente afectado por diferentes enfermedades que resulta en disminución de la producción y de la calidad de la fruta. Dentro de las enfermedades más importantes que atacan al cultivo de fresa se encuentra Mycosphaerella fragariae, agente causal de la peca de la hoja que en estados avanzados puede llevar a la perdida total de los cultivos. El monitoreo de esta enfermedad es una herramienta fundamental para su prevención y control. Las herramientas de agricultura de precisión y agricultura 4.0 como las imágenes obtenidas por drones facilitar la toma de decisiones por parte de los productores y optimizan las labores de cultivo como el monitoreo. Objetivo: En este estudio se identificó la relación entre el porcentaje de área afectada por peca de la hoja (Mycosphaerella fragaraiae) y los índices NDVI y NDRE. Metodología: El estudio se desarrolló en la provincia de Pamplona en Norte de Santander con alta presión de inoculo de Mycosphaerella fragariae. Se colectaron foliolos de plantas de fresa con diferentes grados de afectación causada por Mycosphaerella fragariae, este material vegetal se transportó garantizando su conservación hasta el laboratorio, donde se realizaron mediciones con el espectrorradiómetro portátil EPP2000 de la marca Stellar Net, de las cuales se tomaron los valores de la banda roja, infrarroja y del borde rojo de cada una para realizar el cálculo de varios índices incluyendo el NDVI y el NDRE, estos valores se promediaron para cada grado de afectación. A cada una de las hojas se le calculó el área de afectación exacta a partir del software Compu eye leaf and symptom área y se calcularon los coeficientes de correlación buscando una relación lineal mutua a partir de regresiones lineales simples. Resultados: Se logró identificar que el índice NDRE se correlaciona en un 82% con el área afectada por Mycosphaerella fragariae hasta en un 70 %. Conclusión: El porcentaje de área afectada por peca de la hoja (Mycosphae

The increasing occurrence of stresses in agricultural crops due to pathogens causes a general decline in productivity, becoming a major risk factor for safety and food security. Fusarium Head Blight is a major plant disease characterized by specific pathogenic mechanisms and symptoms, such as spike bleaching, and it represents one of the major biotic threats affecting Mediterranean countries. Remote sensing and field scale optical techniques represent an appealing approach for improving crops monitoring and early detection of diseases. A small-scale controlled field experiment was organized to assess the response of wheat (Triticum aestivum L.) cultivars to Fusarium infections. Hyperspectral images of wheat spikes were acquired in laboratory with a novel visible to near-infrared hyperspectral imaging system with the intent to develop methods capable to automatically identify Fusarium-induced symptoms. We tested the capability of the so-called phasor-based approach to highlight spikes areas affected by Fusarium infection. Outcomes show that infected spikes do not lie completely superimposed to the healthy points cloud in the phasor plane, suggesting that (i) infected grains are characterized by different spectral features, and (ii) Fusarium-related symptoms are not extended throughout the entire spike. This contribution shows the methodology developed and the results obtained from the analysis of hyperspectral images.

Automatic fruit counting systems have garnered interest from farmers and agronomists to monitor fruit production, predict yields in advance, and identify production variability across orchards. However, accurately counting fruits poses challenges, particularly due to occlusions. This study proposes a multi-view sensing approach using continuous motion videos captured by a camera moved along the row of trees, followed by fruit detection in all video frames and application of Multi-Object Tracking (MOT) algorithms to prevent double-counting. Three tracking methods, namely SORT, DeepSORT, and ByteTrack, are compared for fruit counting using the YOLOv5x object detector. The methodology is applied to map fruit production in an experimental apple orchard at two different dates: four weeks and one week before harvest. The results demonstrate that ByteTrack (MOTA=0.682; IDF1=0.837; HOTA=0.689) outperforms SORT and DeepSORT, indicating its superior tracking performance. Computational efficiency analysis reveals similar processing times between SORT and ByteTrack (about 15 ms), while DeepSORT requires significantly more processing time per image (128 ms). Fruit counting evaluation shows reasonably accurate yield predictions on both dates, with reduced errors and improved performance closer to the harvest date (MAPE=7.47 %; R2=0.70). The system proves effective in estimating orchard fruit production using computer vision technology, offering valuable insights for yield forecasting. These findings contribute to optimizing fruit production and supporting precision agriculture practices. The code and the dataset have been made publicly available and a video visualization of results is accessible at http://www.grap.udl.cat/en/publications/video_fruit_counting., This work was partly funded by the Departament de Recerca i Universitats de la Generalitat de Catalunya (grant 2021 LLAV 00088), the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-094222-B-I00 [PAgFRUIT project], PID2021-126648OB-I00 [PAgPROTECT project] and PID2020-117142GB-I00 [DeeLight project] by MCIN/AEI/10.13039/501100011033 and by “ERDF, a way of making Europe”, by the European Union). The work of Jordi Gené Mola was supported by the Spanish Ministry of Universities through a Margarita Salas postdoctoral grant funded by the European Union - NextGenerationEU. The Secretariat of Universities and Research of the Department of Business and Knowledge of the Generalitat de Catalunya and European Social Fund (ESF) are also thanked for financing Juan Carlos Miranda’s predoctoral fellowship (2020 FI_B 00586)., Peer Reviewed, Postprint (author's final draft)

This dataset offers a rich multimodal collection of data from vineyards, designed to enhance agricultural research with a focus on vineyard management and disease monitoring. It includes geotagged smartphone ground images in ".7z" format for detailed plant-level analysis, a ".csv" file detailing plants' phytosanitary status for health assessment, UAV-derived 3D Point Clouds and orthomosaics in ".las" and ".tiff" formats for aerial landscape views, and RTK GNSS data in ".shp" format for precise plant geolocations. This dataset can be combined with other datasets to enable a comprehensive view of the vineyards and improve its value: Ariza-Sentís, Mar, Sergio Vélez, and João Valente. ‘Dataset on UAV RGB Videos Acquired over a Vineyard Including Bunch Labels for Object Detection and Tracking’. Data in Brief 46 (February 2023): 108848. https://doi.org/10.1016/j.dib.2022.108848. Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘VineLiDAR: High-Resolution UAV-LiDAR Vineyard Dataset Acquired over Two Years in Northern Spain.’ Data in Brief, October 2023, 109686. https://doi.org/10.1016/j.dib.2023.109686. Vélez, Sergio, Mar Ariza-Sentís, and João Valente. ‘Dataset on Unmanned Aerial Vehicle Multispectral Images Acquired over a Vineyard Affected by Botrytis Cinerea in Northern Spain’. Data in Brief 46 (February 2023): 108876. https://doi.org/10.1016/j.dib.2022.108876.

Water scarcity in arid and semi-arid areas has led to the development of regulated deficit irrigation (RDI) strategies on most species of fruit trees in order to improve water productivity. For a successful implementation, these strategies require continuous feedback of the soil and crop water status. This feedback is provided by physical indicators from the soil–plant–atmosphere continuum, as is the case of the crop canopy temperature, which can be used for the indirect estimation of crop water stress. Infrared Radiometers (IRs) are considered as the reference tool for temperature-based water status monitoring in crops. Alternatively, in this paper, we assess the performance of a low-cost thermal sensor based on thermographic imaging technology for the same purpose. The thermal sensor was tested in field conditions by performing continuous measurements on pomegranate trees (Punica granatum L. ‘Wonderful’) and was compared with a commercial IR. A strong correlation (R2 = 0.976) between the two sensors was obtained, demonstrating the suitability of the experimental thermal sensor to monitor the crop canopy temperature for irrigation management.

[SPA] En esta comunicación se estudian las vigas inferiores de apoyo del recipiente de cultivo de un lisímetro de pesada transportable para plantaciones hortícolas. El cajón de cultivo alberga el terreno reconstituido donde se desarrolla la plantación y descansa sobre un sistema de pesaje. Para asegurar el correcto funcionamiento del lisímetro el recipiente de cultivo no debe entrar en contacto con otros elementos, evitándose que sus deformaciones interfieran entre sí y modifiquen las mediciones del sistema de pesada. Se han analizado con SolidWorks Simulation los esfuerzos de las vigas y las deformaciones que experimentan. Los resultados obtenidos permiten valorar la supresión de estos elementos para reducir las dimensiones del lisímetro. [ENG] In this communication, the lower beams of the cultivation vessel of a transportable weighing lysimeter for horticultural plantations are studied. The cultivation vessel contains the reconstituted soil where the plantation is developed and rests on a weighing system. To ensure the proper functioning of the lysimeter, the cultivation container have to avoid come into contact with other elements. Its deformations could modify the measurements of the weighing system. The stresses of the beams and the deformations have been analysed with SolidWorks Simulation. The results obtained make it possible to evaluate the elimination of these elements in order to reduce the dimensions of the lysimeter.

Coffee farmers do not have efficient tools to have sufficient and reliable information on the maturation stage of coffee fruits before harvest. In this study, we propose a computer vision system to detect and classify the Coffea arabica (L.) on tree branches in three classes: unripe (green), ripe (cherry), and overripe (dry). Based on deep learning algorithms, the computer vision model YOLO (You Only Look Once), was trained on 387 images taken from coffee branches using a smartphone. The YOLOv3 and YOLOv4, and their smaller versions (tiny), were assessed for fruit detection. The YOLOv4 and YOLOv4-tiny showed better performance when compared to YOLOv3, especially when smaller network sizes are considered. The mean average precision (mAP) for a network size of 800 × 800 pixels was equal to 81 %, 79 %, 78 %, and 77 % for YOLOv4, YOLOv4-tiny, YOLOv3, and YOLOv3-tiny, respectively. Despite the similar performance, the YOLOv4 feature extractor was more robust when images had greater object densities and for the detection of unripe fruits, which are generally more difficult to detect due to the color similarity to leaves in the background, partial occlusion by leaves and fruits, and lighting effects. This study shows the potential of computer vision systems based on deep learning to guide the decision-making of coffee farmers in more objective ways.

Because spectral technology has exhibited benefits in food-related applications, an increasing amount of effort is being dedicated to develop new food-related spectral technologies. In recent years, the use of remote sensing or unmanned aerial vehicles for precision agriculture has increased. As spectral technology continues to improve, portable spectral devices become available in the market, offering the possibility of realising in-field monitoring. This study demonstrates hyperspectral imaging and spectral olive signatures of the Manzanilla and Gordal cultivars analysed throughout the table-olive season from May to September. The data were acquired using an in-field technique and sampled via a non-destructive approach. The olives were monitored periodically during the season using a hyperspectral camera. A white reference was used to normalise the illumination variability in the spectra. The acquired data were saved in files named raw, normalised, and processed data. The normalised data were calculated by the sensor by correcting the white and black levels using the acquired reflectance values. The olive spectral signature of the images is saved in the processed data files. The images were labelled and processed using an algorithm to retrieve the olive spectral signatures. The results were stored as a chart with 204 columns and ‘n’ rows. Each row represents the pixel of an olive in the image, and the columns contain the reflectance information at that specific band. These data provide information about two olive cultivars during the season, which can be used for various research purposes. Statistical and artificial intelligence approaches correlate spectral signatures with olive characteristics such as growth level, organoleptic properties, or even cultivar classification.

The detection and sizing of fruits with computer vision methods is of interest because it provides relevant information to improve the management of orchard farming. However, the presence of partially occluded fruits limits the performance of existing methods, making reliable fruit sizing a challenging task. While previous fruit segmentation works limit segmentation to the visible region of fruits (known as modal segmentation), in this work we propose an amodal segmentation algorithm to predict the complete shape, which includes its visible and occluded regions. To do so, an end-to-end convolutional neural network (CNN) for simultaneous modal and amodal instance segmentation was implemented. The predicted amodal masks were used to estimate the fruit diameters in pixels. Modal masks were used to identify the visible region and measure the distance between the apples and the camera using the depth image. Finally, the fruit diameters in millimetres (mm) were computed by applying the pinhole camera model. The method was developed with a Fuji apple dataset consisting of 3925 RGB-D images acquired at different growth stages with a total of 15,335 annotated apples, and was subsequently tested in a case study to measure the diameter of Elstar apples at different growth stages. Fruit detection results showed an F1-score of 0.86 and the fruit diameter results reported a mean absolute error (MAE) of 4.5 mm and R2 = 0.80 irrespective of fruit visibility. Besides the diameter estimation, modal and amodal masks were used to automatically determine the percentage of visibility of measured apples. This feature was used as a confidence value, improving the diameter estimation to MAE = 2.93 mm and R2 = 0.91 when limiting the size estimation to fruits detected with a visibility higher than 60%. The main advantages of the present methodology are its robustness for measuring partially occluded fruits and the capability to determine the visibility percentage. The main limitation is that, This work was partly funded by the Departament de Recerca i Universitats de la Generalitat de Catalunya (grant 2021 LLAV 00088), the Spanish Ministry of Science, Innovation and Universities (grants RTI2018-094222-B-I00 [PAgFRUIT project], PID2021-126648OB-I00 [PAgPROTECT project] and PID2020-117142GB-I00 [DeeLight project] by MCIN/AEI/10.13039/501100011033 and by “ERDF, a way of making Europe”, by the European Union). The work of Jordi Gené Mola was supported by the Spanish Ministry of Universities through a Margarita Salas postdoctoral grant funded by the European Union - NextGenerationEU., Peer Reviewed, Postprint (published version)

Because spectral technology has exhibited benefits in food-related applications, an increasing amount of effort is being dedicated to develop new food-related spectral technologies. In recent years, the use of remote sensing or unmanned aerial vehicles for precision agriculture has increased. As spectral technology continues to improve, portable spectral devices become available in the market, offering the possibility of realising in-field monitoring. This study demonstrates hyperspectral imaging and spectral olive signatures of the Manzanilla and Gordal cultivars analysed throughout the table-olive season from May to September. The data were acquired using an in-field technique and sampled via a non-destructive approach. The olives were monitored periodically during the season using a hyperspectral camera. A white reference was used to normalise the illumination variability in the spectra. The acquired data were saved in files named raw, normalised, and processed data. The normalised data were calculated by the sensor by correcting the white and black levels using the acquired reflectance values. The olive spectral signature of the images is saved in the processed data files. The images were labelled and processed using an algorithm to retrieve the olive spectral signatures. The results were stored as a chart with 204 columns and ‘n’ rows. Each row represents the pixel of an olive in the image, and the columns contain the reflectance information at that specific band. These data provide information about two olive cultivars during the season, which can be used for various research purposes. Statistical and artificial intelligence approaches correlate spectral signatures with olive characteristics such as growth level, organoleptic properties, or even cultivar classification.

Drones can be very helpful in precision agriculture. Currently, most drone-based solutions for plant disease detection incorporate multispectral, hyperspectral, or thermal cameras, which are expensive. In addition, there is a trend nowadays to apply machine learning techniques to precision agriculture, which are computationally complex and intensive. In this work, we explore the feasibility of detecting ochre spot disease in almond plantations based on conventional techniques of computer vision and images from a very low-cost RGB camera that is placed on board a drone. Such an approach will allow the detection system to be simple and inexpensive. First, we made a study of color on the ochre spot disease. Second, we developed a specific algorithm that was capable of processing and analyzing limited-quality images from a very low-cost camera. In addition, it can estimate the percentage of healthy and unhealthy parts of the plant. Thanks to the GPS on board the drone, the system can provide the location of every sick almond tree. Third, we checked the operation of the algorithm with a variety of photographs of ochre spot disease in almonds. The study demonstrates that the efficiency of the algorithm depends to a great extent on environmental conditions, but, despite the limitations, the results obtained with the analyzed photographs show a maximum discrepancy of 10% between the estimated percentage and the ground truth percentage of the unhealthy area. This approach shows great potential for extension to other crops by making previous studies of color and adaptations.