Leveraging Machine Learning for Early Detection of Soybean Crop Pests

Abstract

Background: Soybean cultivation faces challenges from pest infestations, necessitating advanced and proactive pest management strategies. Traditional methods often lag in early detection, resulting in substantial crop losses. This study addresses this gap by employing machine learning, specifically sequential convolutional neural networks (CNNs) and recurrent neural networks (RNNs), to transform early pest detection in soybean fields. The approach leverages a comprehensive dataset comprising high-resolution crop images and environmental variables, offering insights into soybean ecosystems. Methods: Using CNNs for picture processing and RNNs for collecting temporal relationships in environmental factors, a complex deep learning model is created. The dataset contains 1050 images of soybeans including pests such as Anticarsia, Coccinellidae and without pests (healthy). The model is trained for 20 epochs. The model has been carefully validated for accuracy, sensitivity and efficacy in early insect identification. The diversity of the dataset ensures that the model may be adjusted to a range of soybean growing situations. Result: The outcomes demonstrate the model’s unparalleled effectiveness, routinely outperforming conventional techniques with an accuracy rate of 95%. Its exceptional sensitivity reduces financial and environmental expenses, highlighting its versatility in a range of soybean growing environments. In light of the difficult global agricultural landscape, this study offers a novel strategy for proactive and sustainable pest management, which is essential to guaranteeing strong soybean crop yields.