Orchard monitoring is a vital direction of scientific research and practical application for
increasing fruit production in ecological conditions. Recently, due to the development of …

Pest identification is a challenging task in the agricultural sector, as accurate and timely
detection of pests is essential for effective pest control and crop protection. Conventional …

This paper proposes PestNet, a lightweight method for classifying crop pests, which
improves upon MobileNet-V2 to address the high model complexity and low classification …

Accurate recognition of agricultural pests is crucial for effective pest management and
reducing pesticide usage. In recent research, deep learning models based on residual …

The pests captured with imaging devices may be relatively small in size compared to the
entire images, and complex backgrounds have colors and textures similar to those of the …

The recent survey shows at least 1 in 8 people suffer from either malnutrition or hunger. The
world's growing population is raising more concerns about increasing food productivity …

Deep Learning (DL) has recently emerged as a pivotal tool in a wide variety of agricultural
end-level applications. While promising outcomes have been achieved in crop disease …

The task of agricultural image classification has always been a popular topic in agricultural
research. Both traditional and deep learning-based methods have emerged to address this …

Rice pest identification is essential in modern agriculture for the health of rice crops. As
global rice consumption rises, yields and quality must be maintained. Various …