Published Paper Details:

Rapid and accurate identification of plant diseases is essential for sustainable increases in agricultural productivity. Human experts have traditionally been relied upon to diagnose diseases, pests, nutritional shortages, and severe weather abnormalities in plants. This however is costly, time-consuming, and not practicable in some situations. The study of the use of pictorial methods for plant recognition has become a hot topic to address these challenges. We review the recent studies in the field of identifying pesticides and diseases utilizing imaging and machine learning in this paper. We expect this work to serve as a valuable resource for researchers who use image processing techniques to recognize crop pests and disease. In particular, we concentrate on the use of RGB images due to the low cost and high accessibility of RGB cameras. Deep learning instead of superficial classifications using manufactured characteristics has been at the forefront of recent efforts. The accuracy of the recognition on a specific dataset has been recorded by researchers; in some cases, the performance of these systems has deteriorated significantly when assessed on different datasets or under field conditions. However, it was promising to make progress to date. The experimental findings are present in ten CNN leaf disease recognition architectures, showing the accuracy, memory, precisely, specification, F1 score, training duration, and storage specifications. Recommendations are subsequently provided on the most appropriate architectures to be used in both traditional and mobile computing environments. We also explore some outstanding issues to be tackled to establish realistic systems for recognizing automatic plant diseases in field conditions.