Crop Recognition Method Based On End-effector

With the increasing global population, the demand for agriculture is also on the rise. The crucial stages of agricultural production, namely fruit identification and picking, play a vital role in enhancing product quality and minimizing losses. Traditional manual processing methods, although time-tested, are not only inefficient but also challenging to maintain consistency, making them inadequate to meet the large-scale requirements of modern agricultural production. Consequently, the integration of automation technology has become a necessity. For agricultural robot the machine vision system often need to work in two typical environments, the field environment and the orchard environment. Depending on the varying objectives of operation in diverse environments, crop robots require the ability to rapidly identify fruits within images featuring significant color disparities between crops and two-dimensional field backgrounds. Consequently, a visual servo control system is being investigated. A novel camera attitude search method that employs active visual servo technology to minimize occlusions during orchard search is proposed. The recognition function of the end-effector is exceedingly crucial. The precision of the end effector's identification capabilities directly influences the success rate of automated operations. This is particularly evident in fruit picking, sorting, and other tasks, where the diverse shapes, maturity levels, and colors of fruits present significant challenges to the robotic arm's end effector. The rapid advancement of deep learning technology, however, offers a novel solution for the recognition of fruits by the robotic arm's end effector. By emulating the human visual system, deep learning models can extract the feature representation of fruits from vast amounts of data, enabling accurate identification of fruits in various conditions.