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The human population and domestic animals rely heavily on agriculture for their food and livelihood. Agriculture is an important contributor to the national economy of many countries. Plant diseases lead to a significant reduction in agricultural yield, posing a threat to global food security. It is crucial to detect plant diseases in a timely manner to prevent economic losses. Expert diagnosis and pathogen analysis are widely used for the detection of diseases in plants. However, both expert diagnosis and pathogen analysis rely on the real-time investigation experience of experts, which is prone to errors. In this work, an image analysis-based method is proposed for detecting and classifying plant diseases using an involution neural network and self-attention-based model. This method uses digital images of plant leaves and identifies diseases on the basis of image features. Different diseases affect leaf characteristics in different ways; therefore, their visual patterns are highly useful in disease recognition. For rigorous evaluation of the method, leaf images of different crops, including apple, grape, peach, cherry, corn, pepper, potato, and strawberry, are taken from a publicly available PlantVillage dataset to train the developed model. The experiments are not performed separately for different crops; instead, the model is trained to work for multiple crops. The experimental results demonstrate that the proposed method performed well, with an average classification accuracy of approximately 98.73% (κ = 98.04) for 8 different crops with 23 classes. The results are also compared with those of several existing methods, and it is found that the proposed method outperforms the other methods considered in this work.


Convolutional neural network Image classification Involution Neural Network Plant disease Pre-Trained Network Transfer Learning

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How to Cite
Pradhan, P., Kumar, B., Kumar, K., & Bhutiani, R. (2024). Plant disease detection using leaf images and an involutional neural network . Environment Conservation Journal, 25(2), 452–462.

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