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Classification and Detection of Cabbage Leaf Diseases from Images Using Deep Learning Methods

Myna A.N., Manasvi K., Pavan J.K., Rakshith H.S., Yuktha D. Jain


The presented work uses Deep learning methods to detect diseases in cabbage leaves. To avoid a reduction in agricultural product yield, disease diagnosis in plants is crucial. Manual plant disease monitoring is challenging and time-consuming at every stage. Five major types of diseases are considered. Initially, the input images are classified as healthy and diseased. Further, the diseased images are classified into five different varieties. Early and precise biotic stress detection is necessary for effective crop protection. These accomplishments pertain to the creation of non-intrusive, highresolution optical sensors as well as the development of data analysis techniques that can handle the resolution, size, and complexity of the signals from these sensors. The accuracies of 93.5 and 90.5% are achieved for healthy and diseased leaf images. The overall classification accuracy of 92% is attained. The developed methodology is found to provide good classification accuracy.

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