UNCONVENTIONAL TECHNIQUE FOR IMPROVING FARMER YIELDS BY EXPOSING AND MITIGATING FOLIAGE DISEASES IN AN EXTENSIVELY ADAPTABLE DEEP LEARNING AND COMPUTATIONAL MODEL THROUGH MICROBIOLOGICAL VEGETATION ASSESSMENT | PLANT CELL BIOTECHNOLOGY AND MOLECULAR BIOLOGY

Leaf-based diseases suggest the quality of yield for several sectors of agriculture. Banana planting, mango growing, and several others are part of these sectors. In order to increase the quality of production, the identification and prevention of these diseases is extremely important. Thus, several image processing methods have been proposed over the years to effectively detect leaf-based diseases. The present work proposes a new architecture based on a deep neural network that, when identifying leaf imaging diseases, takes into account a variety of imaging aspects. By evaluating the structure, colour, form and border details, the proposed architecture improves accuracy. This article also compares the current system with other state-of-the-art systems, and it is noted that the proposed system improves precision, accuracy and recall by preserving a mild algorithmic complexity.

Please see the link :- https://www.ikprress.org/index.php/PCBMB/article/view/5469