Enhancing Agricultural Diagnostics
Neural Network-Based Plant Disease Identification
DOI:
https://doi.org/10.58445/rars.1772Keywords:
Machine Learning, Computer Vision, Neural Networks, Agriculture, Plant DiseaseAbstract
This study aims to develop a machine-learning model for the automated identification of plant diseases using leaf images. The analysis uses the PlantVillage dataset and utilizes a multi-layer perceptron (MLP) neural network to classify images from the PlantVillage dataset containing various plant species and diseases. Performance was evaluated using accuracy, F1 score, and confusion matrices. The model achieved an accuracy of 88.06% in distinguishing between healthy and diseased plants when optimized with the appropriate hyperparameters. These results highlight the potential of neural networks as tools for disease detection in agriculture, offering a scalable and reliable solution that can significantly enhance crop management practices. Future work could explore incorporating additional diverse datasets and advanced neural network models to further improve model functionality and generalization.
References
Ristaino, J. B., Anderson, P. K., Bebber, D. P., Brauman, K. A., Cunniffe, N. J., Fedoroff, N. V., Finegold, C., Garrett, K. A., Gilligan, C. A., Jones, C. M., Martin, M. D., MacDonald, G. K., Neenan, P., Records, A., Schmale, D. G., Tateosian, L., & Wei, Q. (2021). The persistent threat of emerging plant disease pandemics to global food security. Proceedings of the National Academy of Sciences, 118(23). https://doi.org/10.1073/pnas.2022239118
Shoaib, M., Shah, B., Ei-Sappagh, S., Ali, A., Ullah, A., Alenezi, F., Gechev, T., Hussain, T., & Ali, F. (2023). An advanced deep learning models-based plant disease detection: A review of recent research. Frontiers in Plant Science, 14. https://doi.org/10.3389/fpls.2023.1158933
GeeksforGeeks. (2024, July 9). Backpropagation in neural network. GeeksforGeeks. https://www.geeksforgeeks.org/backpropagation-in-neural-network/
Model:
https://scikit-learn.org/stable/modules/generated/sklearn.neural_network.MLPClassifier.html
https://huggingface.co/openai/clip-vit-base-patch16
https://arxiv.org/abs/2103.00020
Metal Performance Shaders (MPS). (n.d.). https://huggingface.co/docs/diffusers/optimization/mps
Dataset:
https://www.kaggle.com/datasets/emmarex/plantdisease
Yao, Y., Rosasco, L., & Caponnetto, A. (2007). On early stopping in gradient descent learning. Constructive Approximation, 26(2), 289–315. https://doi.org/10.1007/s00365-006-0663-2
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Copyright (c) 2024 Daniel Pan
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