Deep Learning Approaches for Pest Detection and Agricultural Entomology Applications
DOI:
https://doi.org/10.64137/3108088X/IJAES-V2I1P103Keywords:
Deep Learning, Pest Detection, Agricultural Entomology, Precision Agriculture, Computer Vision, Convolutional Neural Networks (CNN), YOLO, Smart Farming, Artificial Intelligence In Agriculture, Image Classification, Object Detection, Sustainable AgricultureAbstract
Agricultural pests are one of the major threats to global food production, causing significant economic losses and reducing crop quality and yield. Conventional pest detection is based mostly on manual inspection and experience, which requires significant time to inspect the complete farmland. Deep Learning (DL) and computer vision technologies have established automated, fast, and accurate pest detection systems that are now redefining agricultural entomology. Abstract: This paper is an extensive review of deep learning techniques used to detect pests at all levels, as well as their relevant use in agricultural entomology applications. We provide an in-depth overview of several modern deep learning architectures such as Convolutional Neural Networks (CNNs), object detection frameworks like YOLO and Faster R-CNN, segmentation models, and transformer-based methods. It also analyzes available datasets, types of image acquisition systems, and evaluation metrics alongside practical applications in smart farming. The study notes the relatively significant obstacles from data scarcity, environmental variability, computation, and model interpretability. Finally, the future research directions towards explainable AI, edge computing applications for the agriculture domain/federated learning, and intelligent pest management systems are discussed. The results suggest that advanced deep learning technologies can provide significant improvements in the precision agriculture scheme and facilitate sustainable pest control practices.
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