An Intelligent Deep Learning Framework for Automated Pest Detection in Coconut Trees

Abstract:
Rapid increase of pest associated yield losses in coconut plantations has led to the acute need for the development of scalable, accurate and field-ready diagnosis tools. Traditional manual methods of inspection are labor-intensive, subjective and are often inadequate for early detection of destructive pests such as the rhinoceros beetle larva, red palm weevils and eriophyid mites. To overcome this problem, an intelligent deep learning framework for automatic pest detection in coconut trees is proposed in this study, which takes advantage of a hybrid CNNTransformer architecture, optimized for aerial imaging, taken using drones and aerial-based imaging devices. The model is equipped with advanced image augmentation, multi-scale feature extraction and localization using attention mechanism to make it more robust in complex plantation environment with occlusion, variable lightening conditions, and canopy density. Extensive experiments performed on a newly curated dataset of 14,250 annotated coconut tree pest images show that it can give superior performance (97.8 % detection accuracy, 96.9% precision, 97.3% recall, and 0.98 mAP) as compared to ten state-of-the-art baselines. The results validate the potential of the framework to be used in real-time in precision agriculture systems, allowing early intervention and less pesticide use as well as better crop management. Concluding results show that intelligent pest detection through deep learning can be very beneficial for sustainable coconut farming and decision support capability for large-scale plantation monitoring.