Recent Developments and Open Challenges in Deep Learning for Agricultural Pest Monitoring: A Comprehensive Review
DOI:
https://doi.org/10.70112/ajsat-2026.15.1.4345Keywords:
Deep Learning, Generative Adversarial Networks, Hybrid Models, Pest Detection, Pest Control, Transfer LearningAbstract
An important aspect of agricultural practices is the problem of pest control; that has to do with the emerging need for suitable applications that will help effectively and sustainably do away with the pests that might affect immense crops. Traditional pest control practices are found deficient in terms of scalability and handling ability. Among the latest review trends refers to the practice of deep learning application of the advanced pest detection, identification, and control systems worldwide. The review is set out in order to lead capabilities of deep learning convoluted neural networks, recurrent neural networks and hybrid architectures in the automatic categorization of deep pest control tasks. The paper also includes sophisticated topics related to current advance in transfer learning, synthetic data generation, and fusion of multi- modal data to cover public knowledge needs and improve the robustness of models. On the contrary, researchers found that best model improvement was done by CNNs, and likewise, a hybrid model had a much more sophisticated effect on very complex agricultural environments. In an age where labeled data is still insufficient, transfer learning proves to be an ultimate attempt to impose accuracy within the systems. Synthetic data and the use of generative adversarial networks have been proposed to increase the size of training datasets. Wireless areas in connectivity needs the application of such efforts upon the IoT and drones enabling real-time monitoring and response to pests. This addresses a necessary communication from on-farm sensors of systems of pest identification and need for control. The paper reflects a broad but incisive approach for the future utilizations of advanced learning inside pest management strategies.
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