Plant diseases affect the availability and safety of plants for human and animal consumption and threaten food safety, thus reducing food availability and access, as well as reducing crop yield and quality. There is a need for novel disease detection methods that can be used to reduce plant losses due to disease. Thus, this study aims to diagnose tomato leaf diseases by classifying healthy and unhealthy tomato leaf images using two pre-trained convolutional neural networks (CNNs): Inception V3 and Inception ResNet V2. The two models were trained using an open-source database (PlantVillage) and field-recorded images with a total of 5225 images. The models were investigated with dropout rates of 5%, 10%, 15%, 20%, 25%, 30%, 40%, and 50%. The most important results showed that the Inception V3 model with a 50% dropout rate and the Inception ResNet V2 model with a 15% dropout rate, as they gave the best performance with an accuracy of 99.22% and a loss of 0.03. The high-performance rate shows the possibility of utilizing CNNs models for diagnosing tomato diseases under field and laboratory conditions. It is also an approach that can be expanded to support an integrated system for diagnosing various plant diseases.

Dates are a common fruit in many Middle Eastern and African nations and have religious and cultural value. one of the key elements in judging the quality of dates is sorting according to their health state. Combining rejected dates with accepted ones causes significant economic losses in both storage and exportation. Despite being a crucial stage for obtaining high-quality dates and reducing losses, this sorting process is still conducted using traditional methods. Thus, this study aims to classify date fruit quality (accepted or rejected) with machine learning technology to reduce cost, time, and improve the quality of final product. In this study, several Convolutional Neural Network architectures (Inception-v3, Inception-ResNet-v2, VGG19) were used to classify three varieties of date fruit (Mejdool, Saiedi, El-Wadi). These varieties were classified into accepted and rejected samples to build the dataset im-ages. An Arduino Automatic mobile camera shutter controller captured the dataset images. In addition to the Kaggle dataset which was added to the accepted images. The total dataset consisted of 5,945 images, comprising 3,142 accepted images and 2,803 rejected images. By comparing the results of different architectures, Inception-ResNet-v2 demonstrated the best performance, achieving an accuracy of 98.99% and a loss of 0.0344. Therefore, it can be concluded that the Inception-ResNet-v2 model could be utilized to develop a suitable computer vision system, thereby enhancing the date sorting process and facilitating the packaging of high-quality dates.

The rapid development of agricultural robotics has positioned fruit harvesting robots as a promising solution to address labor shortages, enhance productivity, and improve fruit quality management. This book chapter provides a comprehensive overview of the key hardware and software components required for the design and operation of harvesting robots. The hardware section discusses sensors and systems for fruit detection and localization, emphasizing the role of advanced imaging technologies, multi-sensor fusion, and environmental adaptability. It further examines the design of robotic arms and the development of efficient end-effectors, including vacuum, cutting, and soft grippers, highlighting their suitability for different fruit crops. The software section focuses on deep learning methods for fruit detection and localization, with attention to convolutional neural networks, multimodal data integration, and recent advances in recognition under complex orchard conditions. Additionally, robotic arm motion planning strategies are explored, including path optimization, obstacle avoidance, and adaptive control approaches that enable efficient harvesting in unstructured environments. By combining these hardware and software perspectives, this chapter presents an integrated framework for developing intelligent harvesting robots. It also outlines the current challenges, including occlusion, clustered fruits, variable lighting, and energy efficiency, while identifying opportunities for future research through reinforcement learning, multimodal perception, and human–robot collaboration. Overall, this chapter aims to provide researchers, engineers, and practitioners with a holistic understanding of the technologies and challenges in robotic fruit harvesting, paving the way for more reliable and commercially viable systems in modern agriculture.