Grapes Visual Segmentation for Harvesting Robots Using Local Texture Descriptors

• Published in: Computer Vision Systems Vol. 11754; pp. 98 - 109
• Main Authors: Badeka, Eftichia, Kalabokas, Theofanis, Tziridis, Konstantinos, Nicolaou, Alexander, Vrochidou, Eleni, Mavridou, Efthimia, Papakostas, George A., Pachidis, Theodore
• Format: Book Chapter
• Language: English
• Published: Switzerland Springer International Publishing AG 2019; Springer International Publishing
• Series: Lecture Notes in Computer Science
• Subjects: Computer vision; Grapes detection; Image segmentation; Local binary patterns; Visual computing
• DOI: 10.1007/978-3-030-34995-0_9
• ISBN: 3030349942; 9783030349943
• ISSN: 0302-9743

This paper investigates the performance of Local Binary Patterns variants in grape segmentation for autonomous agricultural robots, namely Agrobots, applied to viniculture and winery. Robust fruit detection is challenging and needs to be accurate to enable the Agrobot to execute demanding tasks of precise farming. Segmentation task is handled by classification with the supervised machine learning model k-Nearest Neighbor ( $$ k $$ -NN), including extracted features from Local Binary Patterns (LBP) and their variants in combination of color components. LBP variants are tested for both varieties of red and white grapes, subject to performance measures of accuracy, recall and precision. The results for red grapes indicate an approximate intended accuracy of 94% of detection, while the results relating to white grapes confirm the concerns of complex indiscreet visual cues providing accuracies of 83%.