A Survey on Plants Leaf Diseases Using Machine Learning and Deep Learning Approaches

Agricultural productivity and food security are severely threatened by plant diseases, which affect both yield and quality of crops. The advent of machine learning and deep learning has provided a new avenue for automated detection and diagnosis of these diseases. This review paper delivers an extensive examination of the prevalent trends and prospective developments in employing such technologies. It categorizes various techniques according to the data they utilize, ranging from imagery to sensor data, or a mix of both. The paper reviews renowned datasets prevalent in the research area and assesses the efficacy of various approaches on these datasets. It further discusses the challenges and potential areas for future research, such as the scarcity of data, the interpretability of the algorithms, adaptation to new contexts, and the expansion of these technologies. Furthermore, we highlight potential future directions like explainable AI, active learning, and edge computing, which can contribute to sustainable crop production and effective management of plant health.