Abstract
Agriculture is an essential foundation that supports numerous economies, and the longevity of the coffee business is of paramount significance. Controlling and safeguarding coffee farms from harmful pests, including the Coffee Berry Borer, Mealybugs, Scales, and Leaf Miners, which may drastically affect crop productivity and quality. Standard methods for detecting pest diseases sometimes need specialized knowledge or thorough analysis, leading to a substantial commitment of time and effort. To address this challenge, researchers have explored the use of computer vision and deep learning techniques for the automated detection of plant pest diseases. This paper presents a novel strategy for the early detection of coffee crop killers using Hybrid Vision Graph Neural Networks (HV-GNN) in coffee plantations. The model was trained and validated using a curated dataset of 2850 labelled coffee plant images, which included diverse insect infestations. The HV-GNN design allows the model to recognize individual pests within images and capture the complex relationships between them, potentially leading to improved detection accuracy. HV-GNN proficiently detect pests by analyzing their visual characteristics and elucidating the interconnections among pests in images. Experimental findings indicate that HV-GNN attain a detection accuracy of 93.6625%, exceeding that of leading models. The increased accuracy underscores the feasibility of practical implementation, enabling proactive pest control to protect coffee farms and improve agricultural output.