Deep learning, which has exhibited considerable potential and effectiveness in forest
resource assessment, is vital for comprehending and managing forest resources and …

Fruit detection and localization are essential for future agronomic management of fruit crops
such as yield prediction, yield map** and automated harvesting. However, to perform …

In the complex environment of an orchard, changes in illumination, leaf occlusion, and fruit
overlap make it challenging for mobile picking robots to detect and locate oil-seed camellia …

The apple target recognition algorithm is one of the core technologies of the apple picking
robot. However, most of the existing apple detection algorithms cannot distinguish between …

Precise detection and low-cost deployment are the technological basis of intelligent fruit
picking. This study proposes a lightweight improved YOLOv5s model to detect pitaya fruits in …

Accurate count of fruits is important for producers to make adequate decisions in production
management. Although some algorithms based on machine vision have been developed to …

Orchard pre-harvest yield data is important for fruit growers, which can be used for economic
benefit evaluation, management mode adjustment and so on. However, traditional manual …

High-throughput phenoty** has been widely studied in plant science to monitor plant
growth and analyze the influence of genotypes and environment on plant growth. To meet …

Object detection is a critical technology for apple harvesting robots. For efficient deployment
on edge computing devices with limited processing power, we proposed a new lightweight …

Automatic detection of kiwifruit in the orchard is challenging because illumination varies
through the day and night and because of color similarity between kiwifruit and the complex …