Abstract
In today’s world, plant diseases are a major threat to agriculture crops and their production rate. These are difficult to spot in early stages and it’s not feasible to inspect every leaf manually. We tested different convolutional neural networks on their ability to classify plant diseases. The best model reaches an accuracy of 99.70%, made with a deep training method. We also developed a hybrid training method, reaching a 98.70% accuracy with faster training times, reducing the gap between accuracy and training time. This was made possible due to the freezing of layers at a predefined step. In general, detecting plant diseases using deep learning models is an excellent approach and much more practical than detection with the human eye.
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Ghesquiere, M., Ngxande, M. (2021). Deep Learning for Plant Disease Detection. In: Arabnia, H.R., Deligiannidis, L., Shouno, H., Tinetti, F.G., Tran, QN. (eds) Advances in Computer Vision and Computational Biology. Transactions on Computational Science and Computational Intelligence. Springer, Cham. https://doi.org/10.1007/978-3-030-71051-4_5
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