The potato is a key crop in addressing global hunger, and deep learning is at the core of
smart agriculture. Applying deep learning (eg, YOLO series, ResNet, CNN, LSTM, etc.) in …

Detecting and recognizing pests are paramount for ensuring the healthy growth of crops,
maintaining ecological balance, and enhancing food production. With the advancement of …

Light traps have been widely used for automatic monitoring of pests in the field as an
alternative to time-consuming and labor-intensive manual investigations. However, the scale …

Infestations or parasitism by forestry pests can lead to adverse consequences for tree
growth, development, and overall tree quality, ultimately resulting in ecological degradation …

Traditional chemical pesticide dosage forms and crude application methods have resulted in
low pesticide utilization, increased environmental pollution, and the development of …

Rice planthoppers usually refer to brown planthoppers (Nilaparvata lugens), white-backed
planthoppers (Sogatella furcifera), and small brown planthoppers (Laodelphax striatellus) …

Rice planthoppers are among the most severe migratory pests affecting rice, characterized
by small size and rapid reproduction, leading to sudden and explosive outbreaks. Therefore …

One of the main techniques in smart plant protection is pest detection using deep learning
technology, which is convenient, cost-effective, and responsive. However, existing deep …

In order to rapidly detect pest types in farmland and mitigate their adverse effects on
agricultural production, we proposed an improved Yolox-tiny-based target detection method …

In agriculture and forestry, pest detection is critical for increasing crop yields and reducing
economic losses. However, traditional deep learning models face challenges in resource …