Systematic Review of Emerging Deep Learning approaches for Crop Diseases detection

Deep learning, a branch of artificial intelligence, has drawn interest from the academic and corporate realms, especially in areas like speech and image analysis, video processing, and natural language processing. Its use in agricultural plant protection has expanded to include the diagnosis and evaluation of pests and plant diseases. In this situation, there are several benefits to using deep learning. It removes the biases related to manually chosen pathogen properties, improves objectivity when determining disease characteristics, and hastens the advancement of contemporary technology. In order to diagnose agricultural leaf illnesses, this study explores the most recent developments in deep learning methods, including CNN, DENSNET, InceptionV3, VGG16, Resnet50, and MobileNet, in addition to a variety of sensors. This project aims to aid scientists in the field by tackling challenges in merging deep learning and cutting-edge imaging tools to enhance illness identification. It addresses current trends and barriers, shedding light on unresolved issues and paving the way for further research. By clarifying persistent problems and posing new questions, it fosters progress and innovation in the field of medical imaging and diagnosis.