The utilization of machine vision and its associated algorithms improves the efficiency,
functionality, intelligence, and remote interactivity of harvesting robots in complex …

Continuing progress in machine learning (ML) has led to significant advancements in
agricultural tasks. Due to its strong ability to extract high-dimensional features from fruit …

Apples in modern orchards with vertical-fruiting-wall trees are comparatively easier to
harvest and specifically suitable for robotic picking, where accurate apple detection and …

To perform robust and efficient fruit detection in orchards is challenging since there are a
number of variances in the working environments. Recently, deep-learning have shown a …

Deep learning is known as a promising multifunctional tool for processing images and other
big data. By assimilating large amounts of heterogeneous data, deep-learning technology …

The development of an intelligent jujube fruit harvesting device is a critical step in achieving
the whole mechanization process. Catch‐and‐shake harvesting, as an efficient and stable …

Litchi clusters in fruit groves are randomly scattered and occur irregularly, so it is difficult to
detect and locate the fruit-bearing branches of multiple litchi clusters at one time. This is a …

Trellised fruiting-wall training systems are becoming standard for modern apple orchards
due to their high fruit yield and quality, and their suitability to robotic operations in pruning …

In order to address the challenge of labor shortages, and to reduce costs of apple
harvesting, a targeted shake-and-catch technique is being developed at Washington State …

It is an important step for the precision operation of the litchi picking robot to accurately
detect litchi fruits and fruiting stems in the natural environment. At present, the visual …