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Advances in image processing and techniques in artificial intelligence have made it possible for computers to see and learn. This article introduced a technology that has utilised MobilenetV2 Deep Convolution Neural Network architecture to automatically identify and diagnose plant diseases from images. The identification and classification of plant diseases are now carried out by only human experts-crop extension agents, and farmers, expensive labour that is prone to mistakes. This study relies on dataset gathering as a technique of classifying and identifying plant diseases. It is a multistep process involving pre-process data on the raw set, mask green area of the leaf, remove green section, convert to grayscale and then obtain some characteristics, select, and classify with regard to disease management, etc. Two different types of plants, maize and potato, have been taken in consideration to show effectiveness of the outcome of the proposed model. The confusion matrix and classification performance report were used to evaluate the system. The dataset for potato and maize comprised 6228 and 6878 images, respectively, of leaves. Precise, recall, and F1-scores of 95.15%, 94.76%, and 94.93% were recorded as a cumulative performance across the datasets of potato and maize respectively. This translates to its resistance in picking most diseases for these crops, making it a resource that can be used with confidence in agriculture disease detection. The MobileNetV2 model performs well in both crops, especially for potato early blight and maize common rust. Lower performance in recognizing healthy potato leaves suggests that the feature space of healthy and diseased leaves may overlap. The MobileNetV2 model performed a robust ability in general in the detection of most diseases affecting both potato and maize leaves, but some specific areas need to be targeted for further enhancement.
Jain et al. (Sat,) studied this question.