Advancements in plant protection - the application of machine learning to the detection of maize infestations

Plant infestations cause significant economic losses in agriculture, necessitating rapid and accurate detection for optimized agrotechnical operations and reduced environmental pollution. This study addresses this challenge by proposing a customized convolutional neural network (CNN) architecture for detecting corn leaf worm infestations in maize. The research focuses on developing unique CNN models through extensive experimentation, systematically adjusting hyperparameters like optimizers, filter numbers, and kernel sizes. The study’s main contributions include the design of an accurate CNN classifier, and its implementation in a user-friendly smartphone application. The research highlights the importance of hyperparameter tuning in CNN performance, demonstrating that optimal configurations lead to high accuracy (up to 95% for accuracy, precision, recall, specificity, and F1-score). While the current model focuses on a single pest, the findings underscore the potential of custom CNN classifiers in vision systems for automated crop inspection, offering a promising solution for minimizing crop losses and the environmental impact of chemical plant protection products.