Machine Learning and Deep Learning Techniques for Pest and Disease Detection in Sustainable Agriculture: A Study

Detecting crop pests and diseases early is crucial for preserving crop yields and ensuring food security. Traditionally, these issues were diagnosed by human experts, but there’s a growing need for innovative and automated approaches to pest detection within agricultural fields. This comprehensive survey explores modern techniques such as Convolutional Neural Networks (CNNs), Long Short-Term Memory networks (LSTM), Deep Convolutional Neural Networks, and Deep Belief Networks for monitoring and identifying pests in three vital crop categories: citrus, rice, and cotton. In citrus crops, the application of deep learning, including CNNs and ensemble classifiers, has significantly improved disease identification. Using smartphone-based intelligent sensors equipped with DenseNet technology has shown remarkable accuracy gains. Integrating environmental variables like weather parameters through Bidirectional Long Short-Term Memory Networks (Bi-LSTM) enables real-time pest and disease prediction in rice crop analysis. Additionally, innovative techniques such as PCR-based sensing of plant DNA in insects have enhanced pest identification in cotton crops. Regression-based techniques, exemplified by Generalized Regression Neural Networks (GRNN), excel in predicting crucial factors like leaf moisture. For wheat pest prediction, various models like Bayesian LASSO and Random Forest Regression are employed, while Multiple Linear Regression is used to predict the occurrence of Deoxynivalenol in wheat. Bayesian techniques are leveraged to create real-time pest prediction systems, demonstrating their effectiveness in agriculture. Deep learning techniques, particularly CNNs, play a pivotal role in plant disease identification, emphasizing visualization and early detection. Diverse architectures and datasets contribute to robust models that address challenges related to limited training data. DeepPestNet, an innovative deep learning model, further enhances pest recognition, and data augmentation techniques are used to bolster model robustness. In conclusion, this research underscores the critical role of early disease detection in safeguarding crop yields and ensuring food security. Machine learning and deep learning have immense potential to revolutionize agriculture by automating disease detection and enabling timely interventions. However, addressing data availability and computational resources challenges will be crucial to harnessing their potential in agriculture fully. Ongoing advancements in agricultural technology are essential for improving crop pest and disease detection methods.