A Robust Machine Learning Framework for Plant Disease Emission Forecasting Using Deep Learning Techniques

Abstract

Early crop stress identification and the creation of sustainable agricultural practices depend heavily on the accurate prediction of emission fluctuations linked to plant diseases. Agricultural data that is high-dimensional, nonlinear, and temporal frequently presents challenges for traditional machine learning techniques. This paper suggests a strong deep learning-based framework for predicting disease-induced plant emission characteristics in order to overcome these difficulties. To efficiently capture spatial and temporal patterns in plant physiological and environmental datasets, the framework makes use of sophisticated neural network architectures such as Artificial Neural Networks (ANN), Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM), and hybrid CNN-LSTM models. Using independent test sets and kfold cross-validation, the model's performance was assessed using common metrics including R-squared R2, Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and Mean Absolute Percentage Error (MAPE). Consistent with findings in previous agricultural AI studies experimental results show that deep learning models greatly outperform conventional regression approaches. In line with previous hybrid model research, the hybrid CNN-LSTM architecture outperformed the other models in terms of prediction accuracy and stability across a variety of emission metrics. The robustness of the suggested approach is further confirmed by visualizing prediction errors and real against expected values. The results demonstrate deep learning's promise as a scalable, high-precision method for predicting plant disease emissions in real time, supporting precision agricultural systems and intelligent crop monitoring.