Accurate pest classification plays a pivotal role in modern agriculture for effective pest
management, ensuring crop health and productivity. While Convolutional Neural Networks …

The growth of vision transformer (ViT) methods have been quite enormous since its features
provide efficient outcome in image classification, and identification. Inspired of this …

Crop losses caused by diseases and pests present substantial challenges to global
agriculture, with groundnut crops particularly vulnerable to their detrimental effects. This …

Pests are a major threat to the security of global agricultural production. Therefore, accurate
identification of pests is vital for farmers to increase production and the associated income …

Plant pests have a negative effect on crop yields. If the various insect pests are not identified
and controlled properly, they can spread quickly and cause a significant decline in …

Pest damage is a major factor in reducing crop yield and has negative impacts on the
economy. However, the complex background, diversity of pests, and individual differences …

Insect pests pose a significant and increasing threat to agricultural production worldwide.
However, most existing recognition methods are built upon well-known convolutional neural …

Pests are the main threats to crop growth, and the precision classification of pests is
conducive to formulating effective prevention and governance strategies. In response to the …

Of late, convolutional neural networks have shown significant performance improvement
over the traditional machine learning and dominate the classification tasks in the field of …

Traditional methods for pest recognition have certain limitations in addressing the
challenges posed by diverse pest species, varying sizes, diverse morphologies, and …