A survey on the cultivation of citrus fruits, particularly oranges, holds significant economic and nutritional value worldwide

Galat, Bharti B. and Kute, Vivek B. and Chopde, Nitin R. (2025) A survey on the cultivation of citrus fruits, particularly oranges, holds significant economic and nutritional value worldwide. International Journal of Science and Research Archive, 15 (1). pp. 850-855. ISSN 2582-8185

However, orange orchards are frequently threatened by a plethora of diseases that can drastically reduce yield and quality. Early and accurate disease detection is paramount for effective management and mitigation, preventing widespread crop loss and ensuring sustainable agricultural practices. Traditional disease identification methods often rely on visual inspection by experts, which can be time-consuming, subjective, and prone to human error. Moreover, the rapid spread of certain diseases necessitates swift and reliable diagnostic tools. In this context, the application of advanced technologies like deep learning offers a promising avenue for automating and enhancing disease classification in orange fruits. The advent of deep learning has revolutionized various fields, including computer vision, enabling the development of highly accurate image recognition systems. Convolutional Neural Networks (CNNs), a class of deep learning models, have demonstrated exceptional performance in extracting intricate features from images, making them well-suited for tasks like disease classification. In the domain of agricultural disease detection, CNNs have shown remarkable potential in identifying and classifying various plant diseases based on visual symptoms captured in images. . This project will encompass the entire pipeline of developing a robust disease classification system, from dataset acquisition and preprocessing to model training, evaluation, and potential deployment considerations. A comprehensive dataset of orange fruit images, encompassing various disease types and healthy samples, will be assembled. Rigorous preprocessing techniques, including data augmentation, will be employed to enhance dataset diversity and model generalization. The hybrid model will be trained and fine-tuned using appropriate optimization algorithms and evaluated using relevant performance metrics. The ultimate goal is to create a practical and effective tool that can assist farmers and agricultural experts in the early detection and management of orange fruit diseases, contributing to improved crop health and productivity.