Published Paper Details:

Plant diseases that harm the leaves of the plants halt the growth of the plants themselves. Plant illnesses that are detected early and accurately may lessen the chance that the plant will sustain additional damage. The fascinating strategy required more effort, focus, and expertise. Plant leaf diseases are identified using images of the leaves. Deep learning (DL) research seems to offer a lot of promise for increasing accuracy. The significant progress and expansions in deep learning have made it possible to enhance the accuracy and coordination of the system for recognizing and valuing plant leaf diseases. It is a cutting-edge deep learning method for classifying diseases. An investigation was conducted into the efficacy of diagnosing diseases in plant leaves. The ResNet classifier is used to remove color, texture, and plant leaf arrangement geometry from the given photos. A few efficacy metrics demonstrate that the suggested strategy outperforms current methods with an accuracy rate concert measurements are employed to carry out these procedures. These metrics are used for analysis and to propose a suggested way. The phases of disease detection involve the following steps: image separation, categorization, noise removal, and picture collection.