Sensors and systems for fruit detection and localization: A review

By Aleana Gongal, Suraj Amatya, Manoj Karkee, Qin Zhang, Karen Lewis. Computers and Electronics in Agriculture, 2015

519 citations recorded by the source corpus.

Subfields: Analytical Chemistry, Plant Science

View Paper: https://www.semanticscholar.org/paper/c2eaffc617c57ab5106ba3bc95b7b910be473389

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In Analytical Chemistry

Siva K. Balasundram

In Plant Science

Citations in the corpus

Sensing and Automation in Pruning of Apple Trees: A Review (2018, background): “Various intensive modern tree architecture systems have been developed and tested for production and labor efﬁciency.”
Translating High-Throughput Phenotyping into Genetic Gain (2018, background): “Different selections of frequencies and polarizations that alter SAR signals according to different plant physiological components may even make radar sensors, as still fairly portable active sensors, adaptable to fruit detection for automated fruit counting and even harvesting applications [95,96].”
Research and development in agricultural robotics: a perspective of digital farming. (2018, methodology): “…analysis [163] , combined color distance method and RGB-D data analysis for apples [164] and sweet-peppers [41] , stereo vision for apple detection [165,166] , and the use of convolutional neural networks [167] and deep learning algorithms for fruit detection and obstacle avoidance in extremely…”
Recognition and Localization Methods for Vision-Based Fruit Picking Robots: A Review (2020, background): “Stereo vision technology is a major bottleneck in harvesting robot applications (Zou et al., 2012; Gongal et al., 2015), especially in crop identiﬁcation, localization algorithms, error handling and small object dynamic tracking.”
Detection and counting of flowers on apple trees for better chemical thinning decisions (2019, background): “There are numerous studies related to the problem of object detection in an agricultural environment, with the aim of automating agriculture tasks which are highly labor intensive (Gongal et al. 2015).”