Scholarlink

<p>Contemporary orchard agriculture is confronted with insurmountable problems, including workforce shortage, high production and production expenses, poor harvesting of the fragile fruits, and excessive reliance on agrochemicals. These challenges have a&#xa0;considerable impact on productivity, quality of the fruit, and environmental sustainability, especially for crops such as strawberries that are sensitive to these conditions and must be managed with great care and supervision. Traditional fruit harvesting is a&#xa0;laborious and time-consuming task that relies heavily on seasonal workers. Besides, there is excessive usage of pesticides by the conventional technique, causing increased cost and harm to the environment. Currently available robotic harvesting machines tend to be costly and complicated. They are not practical solutions for small and medium-sized farmers. In order to overcome these drawbacks, this paper introduces a&#xa0;low-cost smart orchard robot, dubbed OPTICROP. Some of the features of the proposed system include the use of a&#xa0;vision-based hybrid system using You Only Look Once (YOLO)–OpenCV with an autonomous differential drive robot. The system utilizes the visual processing using OpenCV to identify, categorize, and selectively pick the ripe, unripe, and diseased fruits and allows the localized application of pesticides to reduce the waste in chemicals. Field tests indicate good navigation through undulating orchard land, high level of accuracy in detecting the fruit, and the ability to harvest without much damage. Moreover, there is optimization of the torque and gripping force required for the gentle harvesting of soft fruits such as strawberries. Furthermore, the presence of a&#xa0;localized pesticide spraying mechanism enables a&#xa0;reduction in unnecessary usage of chemicals. The outcomes verify that OPTICROP is very effective compared with the current harvesting systems in reducing labor dependence, enhancing harvesting accuracy, and sustainable orchard management. In general, the proposed system provides a&#xa0;convenient, scalable, and ecologically friendly system of precision agriculture that can be used by small and medium-sized farmers.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

OPTICROP: A Vision-Based Autonomous Robotic System for Precision Fruit Detection and Harvesting in Orchards

  • 期刊论文

  • 发表日期：

    2026年6月19日

查看全文
  • Indira Ghosh Dastidar,
  • Sakshi Patil,
  • Yash Gogawale,
  • Amruta Sawant,
  • Shreya Maid,
  • Vikas Singh Panwar

摘要

Contemporary orchard agriculture is confronted with insurmountable problems, including workforce shortage, high production and production expenses, poor harvesting of the fragile fruits, and excessive reliance on agrochemicals. These challenges have a considerable impact on productivity, quality of the fruit, and environmental sustainability, especially for crops such as strawberries that are sensitive to these conditions and must be managed with great care and supervision. Traditional fruit harvesting is a laborious and time-consuming task that relies heavily on seasonal workers. Besides, there is excessive usage of pesticides by the conventional technique, causing increased cost and harm to the environment. Currently available robotic harvesting machines tend to be costly and complicated. They are not practical solutions for small and medium-sized farmers. In order to overcome these drawbacks, this paper introduces a low-cost smart orchard robot, dubbed OPTICROP. Some of the features of the proposed system include the use of a vision-based hybrid system using You Only Look Once (YOLO)–OpenCV with an autonomous differential drive robot. The system utilizes the visual processing using OpenCV to identify, categorize, and selectively pick the ripe, unripe, and diseased fruits and allows the localized application of pesticides to reduce the waste in chemicals. Field tests indicate good navigation through undulating orchard land, high level of accuracy in detecting the fruit, and the ability to harvest without much damage. Moreover, there is optimization of the torque and gripping force required for the gentle harvesting of soft fruits such as strawberries. Furthermore, the presence of a localized pesticide spraying mechanism enables a reduction in unnecessary usage of chemicals. The outcomes verify that OPTICROP is very effective compared with the current harvesting systems in reducing labor dependence, enhancing harvesting accuracy, and sustainable orchard management. In general, the proposed system provides a convenient, scalable, and ecologically friendly system of precision agriculture that can be used by small and medium-sized farmers.