The Use of Computer Vision to Combat Losses from Disease in Grapevines

The use of computer vision to support and automate agriculture and viticulture is increasing. Therefore, it is important to continuously test new technologies and equipment. Management of pests and diseases in viticulture is a labour-intensive task. This study aims to investigate current technologies in computer vision that could be applied to disease and pest detection in viticulture and the application of transfer learning on segmentation networks. This study also implements a case study and applies computer vision for disease and pest detection. Observation of limitations in the network's performance on testing images, after training on the limited data set, suggests that careful control is needed over lighting conditions in the image capture environment. Although initial results are positive, a larger training dataset is recommended to achieve a greater level of accuracy. Keywords: Artificial Intelligence, Computer Visions, Viticulture, Sensors DOI: 10.7176/CEIS/14-3-01 Publication date:August 31st 202

Comparative Analysis of Fruit Disease Identification Methods: A Comprehensive Study

The need for accurate and efficient technologies for recognising and controlling fruit diseases has increased due to the rising global demand for high-quality agricultural products. This study focuses on the advantages, disadvantages, and potential practical applications of a range of methods for identifying fecundities. Thanks to developments like improved imaging, machine learning, and data analysis tools, old methods of disease diagnosis have altered as technology has developed. The study compares older methods like visual observation, manual symptom correlation, spectroscopy, and chemical procedures with more contemporary methods like computer vision, autonomous learning algorithms, and sensor-based technologies. Precision, efficiency, cost, scalability, and ease of use are used to describe each method's effectiveness. The article reviews the research examples and practical applications of fruit endocrine disease detection in different cultivars and areas to provide a thorough comparison. This comparison focuses on the variations in disease prevalence and the ways that alternative treatments can be customised to certain situations.It is for this reason that this study offers useful information on how the methods for detecting fruit rot have evolved through time. It emphasises the significance of utilising technological advances to enhance the accuracy, effectiveness, and long-term sustainability of the management of agricultural diseases. Based on the unique requirements of their various agricultural systems, this analysis can assist researchers, practitioners, and policymakers in selecting the most effective methods for identifying fruit diseases

Strawberry Plant Diseases Classification Using CNN Based on MobileNetV3-Large and EfficientNet-B0 Architecture

Strawberry is a plant that has many benefits and a high risk of being attacked by pests and diseases. Diseases in strawberry plants can cause a decrease in the quality of fruit production and can even cause crop failure. Therefore, a method is needed to assist farmers in identifying the types of diseases in strawberry plants. Currently, the most popular method for identifying types of disease in strawberry plants automatically is using Convolutional Neural Network (CNN). This study proposed a system to be able to detect strawberry plant diseases by classifying the disease based on leaf images with high accuracy using CNN. The problem with using CNN in the previous studies is the heavyweight architectures that are not suitable for deployment on restricted resource devices. The research contribution is implementing the method with lightweight architectures. The proposed system is a CNN algorithm using MobileNetV3-Large and EfficientNet-B0 models to train pre-processed four-class classification datasets, such as healthy leaves, spider mites pest leaves, caterpillars pest leaves, and powdery mildew leaves. Using those architectures helps the parameters and model size keep in the small condition. The result of this study shows that the MobileNetV3-Large model outperforms EfficientNet-B0. The results obtained the best accuracy reaching 92.14% using the MobileNetV3-Large architecture with the hyperparameter optimizer RMSProp, epochs 70, and learning rate 0.0001. The percentage of the evaluation model using MobileNetV3-Large for precision, recall, and F1-Score achieved 92.81%, 92.14%, and 92.25%. Overall, it presents fairly good results and is felicitous to be deployed on low-power and low-storage devices. Furthermore, in future work, it needs to obtain higher accuracy by generating more datasets with different lighting conditions, trying other augmentation techniques, such as lighting transformation, and proposing a better model

Sensing and Automation Technologies for Ornamental Nursery Crop Production: Current Status and Future Prospects

The ornamental crop industry is an important contributor to the economy in the United States. The industry has been facing challenges due to continuously increasing labor and agricultural input costs. Sensing and automation technologies have been introduced to reduce labor requirements and to ensure efficient management operations. This article reviews current sensing and automation technologies used for ornamental nursery crop production and highlights prospective technologies that can be applied for future applications. Applications of sensors, computer vision, artificial intelligence (AI), machine learning (ML), Internet-of-Things (IoT), and robotic technologies are reviewed. Some advanced technologies, including 3D cameras, enhanced deep learning models, edge computing, radio-frequency identification (RFID), and integrated robotics used for other cropping systems, are also discussed as potential prospects. This review concludes that advanced sensing, AI and robotic technologies are critically needed for the nursery crop industry. Adapting these current and future innovative technologies will benefit growers working towards sustainable ornamental nursery crop production

LITERATURE REVIEW IOT SOFTWARE ARCHITECTURE ON AGRICULTURE

Context – Internet of Things (IoT) interrelates computing devices, machines, animals, or people and things that use the power of internet usage to utilize data to be much more usable. Food is one of the mandatory human needs to survive, and most of it is produced by agriculture. Using IoT in agriculture needs appropriate software architecture that plays a prominent role in optimizing the gain. Objective and Method – Implementing a solution in a specific field requires a particular condition that belongs to it. The objectives of this research study are to classify the state of the art IoT solution in the software architecture domain perspective. We have used the Evidence- Based Software Engineering (EBSE) and have 24 selected existing studies related to software architecture and IoT solutions to map to the software architecture needed on IoT solutions in agriculture. Result and Implications – The results of this study are the classification of various IoT software architecture solutions in agriculture. The highlighted field, especially in the areas of cloud, big data, integration, and artificial intelligence/machine learning. We mapped the agriculture taxonomy classification with IoT software architecture. For future work, we recommend enhancing the classification and mapping field to the utilization of drones in agriculture since drones can reach a vast area that is very fit for fertilizing, spraying, or even capturing crop images with live cameras to identify leaf disease

Image Analysis and Machine Learning in Agricultural Research

Agricultural research has been a focus for academia and industry to improve human well-being. Given the challenges in water scarcity, global warming, and increased prices of fertilizer, and fossil fuel, improving the efficiency of agricultural research has become even more critical. Data collection by humans presents several challenges including: 1) the subjectiveness and reproducibility when doing the visual evaluation, 2) safety when dealing with high toxicity chemicals or severe weather events, 3) mistakes cannot be avoided, and 4) low efficiency and speed. Image analysis and machine learning are more versatile and advantageous in evaluating different plant characteristics, and this could help with agricultural data collection. In the first chapter, information related to different types of imaging (e.g., RGB, multi/hyperspectral, and thermal imaging) was explored in detail for its advantages in different agriculture applications. The process of image analysis demonstrated how target features were extracted for analysis including shape, edge, texture, and color. After acquiring features information, machine learning can be used to automatically detect or predict features of interest such as disease severity. In the second chapter, case studies of different agricultural applications were demonstrated including: 1) leaf damage symptoms, 2) stress evaluation, 3) plant growth evaluation, 4) stand/insect counting, and 5) evaluation for produce quality. Case studies showed that the use of image analysis is often more advantageous than visual rating. Advantages of image analysis include increased objectivity, speed, and more reproducibly reliable results. In the third chapter, machine learning was explored using romaine lettuce images from RD4AG to automatically grade for bolting and compactness (two of the important parameters for lettuce quality). Although the accuracy is at 68.4 and 66.6% respectively, a much larger data base and many improvements are needed to increase the model accuracy and reliability. With the advancement in cameras, computers with high computing power, and the development of different algorithms, image analysis and machine learning have the potential to replace part of the labor and improve the current data collection procedure in agricultural research. Advisor: Gary L. Hei

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

Proceedings of the European Conference on Agricultural Engineering AgEng2021

This proceedings book results from the AgEng2021 Agricultural Engineering Conference under auspices of the European Society of Agricultural Engineers, held in an online format based on the University of Évora, Portugal, from 4 to 8 July 2021. This book contains the full papers of a selection of abstracts that were the base for the oral presentations and posters presented at the conference. Presentations were distributed in eleven thematic areas: Artificial Intelligence, data processing and management; Automation, robotics and sensor technology; Circular Economy; Education and Rural development; Energy and bioenergy; Integrated and sustainable Farming systems; New application technologies and mechanisation; Post-harvest technologies; Smart farming / Precision agriculture; Soil, land and water engineering; Sustainable production in Farm buildings

Sustainable Agriculture and Advances of Remote Sensing (Volume 2)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publication of the results, among others

Smart greenhouses as the path towards precision agriculture in the food-energy and water nexus: case study of Qatar

Greenhouse farming is essential in increasing domestic crop production in countries with limited resources and a harsh climate like Qatar. Smart greenhouse development is even more important to overcome these limitations and achieve high levels of food security. While the main aim of greenhouses is to offer an appropriate environment for high-yield production while protecting crops from adverse climate conditions, smart greenhouses provide precise regulation and control of the microclimate variables by utilizing the latest control techniques, advanced metering and communication infrastructures, and smart management systems thus providing the optimal environment for crop development. However, due to the development of information technology, greenhouses are undergoing a big transformation. In fact, the new generation of greenhouses has gone from simple constructions to sophisticated factories that drive agricultural production at the minimum possible cost. The main objective of this paper is to present a comprehensive understanding framework of the actual greenhouse development in Qatar, so as to be able to support the transition to sustainable precision agriculture. Qatar’s greenhouse market is a dynamic sector, and it is expected to mark double-digit growth by 2025. Thus, this study may offer effective supporting information to decision and policy makers, professionals, and end-users in introducing new technologies and taking advantage of monitoring techniques, artificial intelligence, and communication infrastructure in the agriculture sector by adopting smart greenhouses, consequently enhancing the Food-Energy-Water Nexus resilience and sustainable development. Furthermore, an analysis of the actual agriculture situation in Qatar is provided by examining its potential development regarding the existing drivers and barriers. Finally, the study presents the policy measures already implemented in Qatar and analyses the future development of the local greenhouse sector in terms of sustainability and resource-saving perspective and its penetration into Qatar’s economy.Open Access funding provided by the Qatar National Library. The authors are grateful to Qatar National Research Fund (QNRF) for funding and supporting the M-NEX Project (Grant No. BFSUGI01-1120-170005) in Qatar. The M-NEX is a project of the Collaborative Research Area Belmont Forum (Grant No. 11314551)