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01-Applied Mathematics & Information Sciences
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Volumes > Volume 17 > No. 5

 
   

Crop Disease Detection Using Deep Learning

PP: 1240-1262
doi:10.18576/amis/170580
Author(s)
R Karthiban, V Alphin Jude, G Dhanush, M T Geethanjaly, S Kavya,
Abstract
Plants are a major source of food for the world population. Plant diseases contribute to production loss, which can be tackled with continuous monitoring. Manual plant disease monitoring is both laborious and error prone. Early detection of plant diseases using computer vision and artificial intelligence (AI) can help to reduce the adverse effects of diseases and helps to overcome the shortcomings of continuous human monitoring. In this study, we have extensively studied the performance of the different state-of-the-art convolutional neural networks (CNNs) classification network architectures i.e., Mobile Net, InceptionV3 and VGG19 on 18,345 plain tomato leaf images to classify tomato diseases. The comparative performance of the models for the binary classification (healthy and unhealthy leaves), and ten-class classification (healthy and various types of unhealthy leaves) are reported. This proposed system has achieved an average accuracy of 94-95% indicating the feasibility of the neural network approach even under unfavourable conditions. It can be concluded that deep architectures performed better at classifying the diseases for these experiments. The performance of each of the experimental studies reported in this work outperforms the existing literature.

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