An In-Depth Analysis of Deep Learning and Machine Learning Methods for Identifying Rice Leaf Diseases

By training models on massive datasets of rice leaf photos and differentiating between healthy and diseased leaves, machine learning and deep learning approaches are employed to identify rice leaf disease. To correctly identify and categorize rice leaf diseases, these models extract pertinent information from the pictures and use classification methods like Support Vector Machines or Convolutional Neural Networks. The measures adopted to assess the efficacy of the predictive models are accuracy, precision, recall, and F1 score. However, a variety of rice leaf problems can harm the crop. It will result in low-quality harvests and also have an impact on the crop's growth, and it's challenging. without the aid of a specialist, enables farmers to identify illnesses in rice crops at the appropriate time. Therefore, using an automated technique to find rice leaf disease is highly beneficial. Farmers should take safeguards early on to prevent harm to their crops. By automating the identification and classification process, Machine Learning (ML) and Deep Learning (DL) approaches give researchers useful tools for detecting rice leaf disease. With the use of these approaches, massive datasets of rice leaf pictures may be analyzed, facilitating the creation of precise and effective disease detection models. These methods can help researchers better understand rice leaf diseases, aid in early identification, and assist in the creation of efficient disease management procedures.