Computer Vision Technology for Agricultural Robotics

Purpose - Aims at developing vision-based algorithms to improve efficiency and quality in agricultural applications. Two case studies are analyzed dealing with the harvest of radicchio and the post-harvest of fennel, respectively. Design/methodology/approach - Presents two visual algorithms, which are called the radicchio visual localization (RVL) and fennel visual identification (FVI). The RVL serves as a detection system of radicchio plants in the field for a robotic harvester. The FVI provides information to an automated cutting device to remove the parts of fennel unfit for the market, i.e. root and leaves. Laboratory and field experiments are described to validate our approach and asses the performance of our visual modules. Findings - Both the visual systems presented showed to be effective in experimental trials, computational efficient, accurate, and robust to noises and lighting variations. Computer vision could be successfully adopted in the intelligent and automated production of fresh market vegetables to improve quality and efficiency. Practical implications - Provides guidance in the development of vision-based algorithms for agricultural applications. Originality/value - Describes visual algorithms based on intelligent morphological and color filters which lends themselves very well to agricultural applications and allow robustness and real-time performance.