Convolutional neural networks ResNet-50 for weevil detection in corn kernels

Abstract

The article explores the use of convolutional neural networks, specifically ResNet-50, to detect weevils in corn kernels. Weevils are a major pest of stored maize and can cause significant yield and quality losses. The study found that the ResNet-50 model was able to distinguish with high precision between weevil-infested corn kernels and healthy kernels, achieving values ​​of 0.9464 for precision, 0.9310 for sensitivity, 0.9630 for specificity, 0.9469 for quality index, 0.9470 for the area under the curve (AUC) and 0.9474 for the F-score. The model was able to recognize nine out of ten weevil-free corn kernels using a minimal number of training samples. These results demonstrate the efficiency of the model in the accurate detection of weevil infestation in maize grains. The model's ability to accurately identify weevil-affected grains is critical to taking rapid action to control the spread of the pest, which can prevent significant economic losses and preserve the quality of stored corn. Research suggests that the use of ResNet-50 offers an efficient and low-cost solution for the early detection of weevil infestation in corn kernels. These models can quickly process large amounts of imaging data and perform accurate analysis, making it easy to identify affected grains.