Wheat Crops Monitor: A More Reliable Rust Disease Detector for Wheat Farming
Crop health monitoring, as an intelligent farming activity, has become increasingly difficult and tedious for farmers due to the diversity in plant pathology. The performance of frequentist convolutional neural network (CNN) models is no longer sufficient to detect pertinent features in images and d...
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| Autores principales: | , , |
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| Formato: | Capítulo de libro |
| Lenguaje: | inglés |
| Publicado: |
IntechOpen
2024
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| Materias: | |
| Acceso en línea: | https://www.intechopen.com/chapters/89582 |
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| Sumario: | Crop health monitoring, as an intelligent farming activity, has become increasingly difficult and tedious for farmers due to the diversity in plant pathology. The performance of frequentist convolutional neural network (CNN) models is no longer sufficient to detect pertinent features in images and detect diseases early and efficiently. On the other hand, uncertainty in convolutional network inference is a big concern that can introduce more inexactitude in the predicted classes. In this context, we propose an intelligent farming application that aims to provide farmers with assistance in wheat crop health monitoring. The Bayesian inference with local reparameterization trick has been used to improve the sampling process during the learning phase. Thus, the uncertainty in the model and the output have been modeled to give an idea of the room for improvement. The classification skill of the proposed Bayesian uncertainty-based monitor can distinguish between wheat crops with no diseases and those infected with leaf and stem rust based on leaf and stem super-resolution image processing. The achieved accuracy is 96%, with a big resistance against overfitting/underfitting issues, and more reliability is obtained through the tolerance of the classification concept. The model is also optimized for real-time inference and adapted for resource-constrained devices. |
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| ISBN: | 978-0-85014-279-2 |
| DOI: | 10.5772/intechopen.115211 |