Neuro-Fuzzy Systems Applications in Prediction of Crop Diseases: A Comprehensive Review
Keywords:
Agriculture, Crop disease, Neuro-Fuzzy, Prediction, Precision, SmartAbstract
Crop diseases pose a significant threat to agricultural productivity and food security. Accurate and timely disease prediction is crucial for implementing effective control measures. This review comprehensively explores the application of Neuro-Fuzzy Systems (NFS) in crop disease prediction. NFS, integrating the learning capabilities of neural networks with the interpretability of fuzzy logic, offer a powerful framework for handling the complex and uncertain nature of agricultural data. This study examines various NFS architectures and methodologies employed for predicting diseases in diverse crops. It analyses the strengths and limitations of these systems, focusing on their ability to model non-linear relationships, handle noisy data, and provide interpretable prediction outcomes. The review also evaluates the impact of feature selection, data preprocessing, and parameter optimization on the performance of NFS models. This review also highlights the challenges and future directions in developing robust and scalable NFS-based crop disease prediction systems, emphasizing the potential of these systems to revolutionize precision agriculture.
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