Revolutionizing Agriculture: Artificial Intelligence Assisted Plant Leaf Disease Detection using Deep Learning Principles

Agriculture is a backbone for every nation. Farmers are facing a lot of problem to deal with crop diseases, because crop diseases are bad for everyone involved farmers, consumers, and the environment—it is essential that farmers regularly monitor their crops for signs of disease and take appropriate measures to eradicate them. Because of variations in rainfall, temperature, and other climatic factors, plants are continually exposed to diseases like viruses, bacteria, and fungus, which can have a devastating effect on crop yields. The specific kind of leaf disease that affects a plant’s leaves is not always easy for the naked eye to discern. Therefore, cutting-edge technological methods including deep learning algorithms, machine learning, and artificial intelligence have been employed to improve the accuracy and recognition rate in order to safeguard plants from leaf diseases and advance the agricultural sector. Image capture, data pre-processing, segmentation, feature extraction, and picture classification are some of the important phases in illness prediction utilizing image processing. This research introduces a novel method; Novel Learning based Agricultural Support System (NLASS), which uses AI and learning logics to intelligently detect plant leaf diseases. The suggested model’s efficacy is assessed by cross-validating this approach with the traditional Support Vector Machine (SVM) model. A supervised classifier neural network and picture content characterization are the building blocks of the suggested decision-making system. This sort of decision analysis uses image processing techniques, which include a preprocessing, feature extraction, and classification step. The processing phase involves resizing the input image and, if necessary, selecting the region of interest.