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Artificial Intelligence-Enabled Apple Disease Monitoring System for Environmental Protection and Resource Conservation
Abstract
The cultivation of apples is an important segment of the worldwide agricultural economy but is extremely susceptible to numerous diseases that are negatively impactful on yield and quality. To prevent such issues, the current study proposes an Apple Disease Monitoring and Control System from a hybrid AI configuration combining CNN, LSTM, and GAN. The proposed hybrid AI system aims to improve the accuracy of disease identification, optimize disease development prediction, and create synthetic data for generalization. The hybrid AI system uses real-time image data from apple orchards, performs time-series analysis to forecast disease development, and applies GANs to improve training data sets. Experimental results show that our proposed system is more accurate, robust, and minimizes variance compared to conventional machine learning models. The results of this study have the potential to revolutionize precision agriculture through a scalable and automated apple disease monitoring approach, ultimately contributing to increased crop yields and increased economic returns for farmers
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