Food and Bioproducts Processing, Год журнала: 2024, Номер 149, С. 353 - 367
Опубликована: Дек. 11, 2024
Язык: Английский
Computers and Electronics in Agriculture, Год журнала: 2024, Номер 222, С. 109032 - 109032
Опубликована: Май 29, 2024
Язык: Английский
Процитировано
19
Deleted Journal, Год журнала: 2025, Номер 67(1)
Опубликована: Янв. 3, 2025
The process of cultivating soil for crop planting and domesticating animals is known as agriculture. A growing agriculture sector indicates an improving economy. Agriculture considered the initial pillar that supports global food safety. Additionally, it controls majority Since we depend on survival, needs to be regularly supervised by us. In this era computerization, humans entirely cyberspace material super-fast takes less time compared humans. Hence, human vision can replicated computer vision. Visual data information are processed analyzed using hardware software. It covers procedures gathering, sending, processing, filtering, storing, comprehending visual data. study computational theory direct research, a variety applications offer solid foundation research platform. use machine has recently increased in response need fast precise ways track production fruit. Machine learning (ML) algorithms make possible swiftly reliably analyze enormous amounts data, regardless complexity. already widely used many domains, such credit analysis, fraud detection, defect sophisticated spam filters, picture recognition patterns, prediction models, inspection product features. But with so options available, critical understand unique qualities each approach optimal situation which apply it. review, have discussed detail artificial intelligence (AI) fruit summarized more than 110 AI technology. As now, review first compilation work application prospects AI-based technology systems. This will provide single-point comprehensive source academics researchers worldwide seeking related technologies system.
Язык: Английский
Процитировано
3
Journal of Field Robotics, Год журнала: 2023, Номер 41(7), С. 2384 - 2400
Опубликована: Ноя. 14, 2023
Abstract Decreased availability and rising cost in labor poses a serious threat to the long‐term profitability sustainability of apple industry United States many other countries. Harvest automation is thus urgently needed. In this paper, we present unified system design field evaluation new harvesting robot. The robot mainly composed specially designed perception component, four‐degree‐of‐freedom manipulator, an improved vacuum‐based soft end‐effector, dropping/catching component receive transport picked fruits. Software algorithms are developed enable synergistic coordination hardware components for efficient, automated apples challenging orchard environments. Specifically, by integrating modified triangulation image processing analysis algorithms, novel strategy achieve robust detection precise localization. Improved planning control guide target positions. performance robotic was evaluated through tests two orchards with different tree architectures foliage conditions. where trees were young well‐pruned, achieved 82.4% successful rate. second, older dense, clustered branches foliage, had 65.2% average cycle time harvest fruit approximately 6 s, which included software algorithm execution. Moreover, in‐depth obtained results, limitations planned future works discussed.
Язык: Английский
Процитировано
39
Computers and Electronics in Agriculture, Год журнала: 2023, Номер 215, С. 108412 - 108412
Опубликована: Ноя. 23, 2023
Язык: Английский
Процитировано
25
Smart Agricultural Technology, Год журнала: 2024, Номер 7, С. 100391 - 100391
Опубликована: Янв. 4, 2024
Recent advancements in deep learning-based approaches have led to remarkable progress fruit detection, enabling robust identification complex environments. However, much less has been made on 3D localization, which is equally crucial for robotic harvesting. Complex shape/orientation, clustering, varying lighting conditions, and occlusions by leaves branches greatly restricted existing sensors from achieving accurate localization the natural orchard environment. In this paper, we report design of a novel technique, called Active Laser-Camera Scanning (ALACS), achieve localization. The ALACS hardware setup comprises red line laser, an RGB color camera, linear motion slide, external RGB-D camera. Leveraging principles dynamic-targeting laser-triangulation, enables precise transformation projected 2D laser surface apples positions. To facilitate pattern acquisitions, Laser Line Extraction (LLE) method proposed high-precision feature extraction apples. Comprehensive evaluations LLE demonstrated its ability extract patterns under variable occlusion conditions. system achieved average apple accuracies 6.9 - 11.2 mm at distances ranging 1.0 m 1.6 m, compared 21.5 commercial RealSense indoor experiment. Orchard that 95% detachment rate versus 71% By overcoming challenges research contributes advancement harvesting technology.
Язык: Английский
Процитировано
7
Horticulturae, Год журнала: 2023, Номер 10(1), С. 40 - 40
Опубликована: Дек. 31, 2023
Robust and effective fruit detection localization is essential for robotic harvesting systems. While extensive research efforts have been devoted to improving detection, less emphasis has placed on the aspect, which a crucial yet challenging task due limited depth accuracy from existing sensor measurements in natural orchard environment with variable lighting conditions foliage/branch occlusions. In this paper, we present system design calibration of an Active LAser-Camera Scanner (ALACS), novel perception module robust high-precision localization. The hardware ALACS mainly consists red line laser, RGB camera, linear motion slide, are seamlessly integrated into active scanning scheme where dynamic-targeting laser-triangulation principle employed. A high-fidelity extrinsic model developed pair laser illumination enabling precise computation when target captured by both sensors. random sample consensus-based then designed calibrate parameters based collected data. Comprehensive evaluations conducted validate scheme. results show that proposed method can detect remove data outliers achieve parameter computation, calibrated able maximum measurement error being than 4 mm at distance ranging 0.6 1.2 m.
Язык: Английский
Процитировано
11
Computers and Electronics in Agriculture, Год журнала: 2023, Номер 212, С. 108156 - 108156
Опубликована: Авг. 19, 2023
Язык: Английский
Процитировано
9
Computers and Electronics in Agriculture, Год журнала: 2024, Номер 227, С. 109586 - 109586
Опубликована: Ноя. 14, 2024
Язык: Английский
Процитировано
3
Computers and Electronics in Agriculture, Год журнала: 2025, Номер 231, С. 109908 - 109908
Опубликована: Янв. 22, 2025
Язык: Английский
Процитировано
0
Computers and Electronics in Agriculture, Год журнала: 2025, Номер 235, С. 110343 - 110343
Опубликована: Апрель 3, 2025
Язык: Английский
Процитировано
0