Abstract:Progressive application of multidisciplinary research and development pushes the evolution of automation in many subsectors of agriculture to increase productivity, economic growth and environmental preservation with the help of robotics and artificial intelligence. Fruit harvesting robots have been developed mainly to provide support in the field for limited labour resources, to enable selective harvesting, to improve the efficiency and to preserve the quality of fruits. Even a small delay in harvesting can c… Show more
“…Findings suggest that Transfer learning and optimizing weights to train the architecture in fruit detection and related tasks improves performance. Kumar et.al [104] also reviewed the application of deep learning models for fruit detection and localisation to aid in tree crop load estimation. This work further looked at approaches from extrapolation of tree image counts to orchard yield estimation while dealing with occlusion.…”
Section: A Existing Surveys On Automated Detection Of Fruits and Damagesmentioning
Automation improves the quality of fruits through quick and accurate detection of pest and disease infections thus contributing to the country's economic growth and productivity. Although humans can identify the fruit damage caused by pests and diseases, methods used are inconsistent, time-consuming, and variable. The surface features of fruits typically observed by consumers who seek their health benefits, affect their market value. The issue of pest and disease infections further deteriorates fruits' quality, becoming a mounting stressor on farmers as they affect the potential income that could have been realised from production, processing and export. This article reviews various studies on detecting and classifying damages in fruits. Specifically, we review articles where state-of-the-art approaches under segmentation, image processing, machine learning, and deep learning have proved effective in developing automated systems that address hurdles associated with manual methods of assessing damage using visual experiences. This survey reviews 32 Journal and Conference articles spanning 13 years obtained electronically through Google Scholar, Scopus, IEEE, ScienceDirect, and general internet searches. This survey further presents a detailed discussion of related studies done in the past while emphasizing their strengths and limitations and presenting future research directions. It also reveals that much as the use of automated detection and classification of fruit damage has yielded promising results in the horticulture industry, more research is still needed with systems required to fully automate the detection and classification processes, especially those that are mobile phone-based towards addressing occlusion challenges.INDEX TERMS fruit damage detection, classification, deep learning, image analysis and segmentation.
“…Findings suggest that Transfer learning and optimizing weights to train the architecture in fruit detection and related tasks improves performance. Kumar et.al [104] also reviewed the application of deep learning models for fruit detection and localisation to aid in tree crop load estimation. This work further looked at approaches from extrapolation of tree image counts to orchard yield estimation while dealing with occlusion.…”
Section: A Existing Surveys On Automated Detection Of Fruits and Damagesmentioning
Automation improves the quality of fruits through quick and accurate detection of pest and disease infections thus contributing to the country's economic growth and productivity. Although humans can identify the fruit damage caused by pests and diseases, methods used are inconsistent, time-consuming, and variable. The surface features of fruits typically observed by consumers who seek their health benefits, affect their market value. The issue of pest and disease infections further deteriorates fruits' quality, becoming a mounting stressor on farmers as they affect the potential income that could have been realised from production, processing and export. This article reviews various studies on detecting and classifying damages in fruits. Specifically, we review articles where state-of-the-art approaches under segmentation, image processing, machine learning, and deep learning have proved effective in developing automated systems that address hurdles associated with manual methods of assessing damage using visual experiences. This survey reviews 32 Journal and Conference articles spanning 13 years obtained electronically through Google Scholar, Scopus, IEEE, ScienceDirect, and general internet searches. This survey further presents a detailed discussion of related studies done in the past while emphasizing their strengths and limitations and presenting future research directions. It also reveals that much as the use of automated detection and classification of fruit damage has yielded promising results in the horticulture industry, more research is still needed with systems required to fully automate the detection and classification processes, especially those that are mobile phone-based towards addressing occlusion challenges.INDEX TERMS fruit damage detection, classification, deep learning, image analysis and segmentation.
“…Intelligent harvesting system refers to a system that utilizes modern technology to achieve autonomous harvesting by the steps of recognition, decision, control, and grasping during the agricultural harvesting process. The intelligent picking system has the characteristics of efficiency, precision, and reliability, which can greatly improve agricultural production efficiency, reduce the labor burden of fruit farmers, and improve the quality and safety of agricultural production [1][2][3][4][5][6][7][8]. The literature distribution of intelligent harvesting systems for different crops is shown in Figure 1.…”
Smart agricultural harvesting robots’ vision recognition, control decision, and mechanical hand modules all resemble the human eye, brain, and hand, respectively. To enable automatic and precise picking of target fruits and vegetables, the system makes use of cutting-edge sensor technology, machine vision algorithms, and intelligent control and decision methods. This paper provides a comprehensive review of international research advancements in the “eye–brain–hand” harvesting systems within the context of smart agriculture, encompassing aspects of mechanical hand devices, visual recognition systems, and intelligent decision systems. Then, the key technologies used in the current research are reviewed, including image processing, object detection and tracking, machine learning, deep learning, etc. In addition, this paper explores the application of the system to different crops and environmental conditions and analyzes its advantages and challenges. Finally, the challenges and prospects for the research on picking robots in the future are presented, including further optimization of the algorithm and improvement of flexibility and reliability of mechanical devices. To sum up, the “eye–brain–hand” picking system in intelligent agriculture has great potential to improve the efficiency and quality of crop picking and reduce labor pressure, and it is expected to be widely used in agricultural production.
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