Revolutionizing Plant Disease Detection: A Comprehensive Review - Shruthi T.V.; Suresh M.B. (International Journal of Advance in Molecular Engineering, 2024)
Rise in population demands more food production but the diseases in plants contribute to loss. The advancement in agricultural field has a remarkable effect in detecting plant diseases. These diseases will have a major impact on the quality of plant and yield and hence can destroy the entire plant if they are not controlled on time. To reduce disease-related losses, it is necessary to identify different types of diseases and control the diseases in the early stages. Subjective audit by farmers or agricultural experts across vast plant fields consumes much time and is impractical thus minimize crop production. Therefore, many agricultural procedures and practices are adapted to control plant diseases. Existing systems have adapted various image processing techniques, computer vision, machine learning techniques, deep learning techniques and deep transfer learning technique for diseases detection. Artificial Intelligence (AI) plays a crucial role in addressing many challenges faced. It is integrated with other technologies for efficient farming. This review helps the researcher to know the existing methods applied for the detection of plant diseases and also the research gaps and future exploration in the field of deep learning to improve the model accuracy for better detection.
Keywords: Deep Learning models; Deep Transfer Learning models; Precision agriculture; Pretrained models; Quantitative Metrics; machine learning models