|
|
|
|
|
Junsheng Liu, Guangze Zhao, Shuangxi Liu, Yi Liu, Huawei Yang, Jingwei Sun, Yinfa Yan, Guoqiang Fan, Jinxing Wang and Hongjian Zhang
In the realm of automated apple picking operations, the real-time monitoring of apple maturity and diameter characteristics is of paramount importance. Given the constraints associated with feature detection of apples in automated harvesting, this study ...
ver más
|
|
|
|
|
|
|
|
|
|
|
Xingdong Sun, Yukai Zheng, Delin Wu and Yuhang Sui
The key technology of automated apple harvesting is detecting apples quickly and accurately. The traditional detection methods of apple detection are often slow and inaccurate in unstructured orchards. Therefore, this article proposes an improved YOLOv5s...
ver más
|
|
|
|
|
|
|
|
|
|
|
Guoyu Zhang, Ye Tian, Wenhan Yin and Change Zheng
The use of automation technology in agriculture has become particularly important as global agriculture is challenged by labor shortages and efficiency gains. The automated process for harvesting apples, an important agricultural product, relies on effic...
ver más
|
|
|
|
|
|
|
|
|
|
|
Wolfgang Jarausch, Miriam Runne, Nora Schwind, Barbara Jarausch and Uwe Knauer
Apple proliferation (AP) is an economically important disease in many apple-growing regions caused by ?Candidatus Phytoplasma mali? which is spread by migrating psyllid vectors on a regional scale. As infected trees in orchards are the only inoculum sour...
ver más
|
|
|
|
|
|
|
|
|
|
|
Huishan Li, Lei Shi, Siwen Fang and Fei Yin
Aiming at the problem of accurately locating and identifying multi-scale and differently shaped apple leaf diseases from a complex background in natural scenes, this study proposed an apple leaf disease detection method based on an improved YOLOv5s model...
ver más
|
|
|
|
|
|
|
|
|
|
|
Huaiwen Wang, Jianguo Feng and Honghuan Yin
Images captured using unmanned aerial vehicles (UAVs) often exhibit dense target distribution and indistinct features, which leads to the issues of missed detection and false detection in target detection tasks. To address these problems, an improved met...
ver más
|
|
|
|
|
|
|
|
|
|
|
Haiping Si, Yunpeng Wang, Wenrui Zhao, Ming Wang, Jiazhen Song, Li Wan, Zhengdao Song, Yujie Li, Bacao Fernando and Changxia Sun
Apples are ranked third, after bananas and oranges, in global fruit production. Fresh apples are more likely to be appreciated by consumers during the marketing process. However, apples inevitably suffer mechanical damage during transport, which can affe...
ver más
|
|
|
|
|
|
|
|
|
|
|
Nader Ekramirad, Alfadhl Y. Khaled, Kevin D. Donohue, Raul T. Villanueva and Akinbode A. Adedeji
Codling moth (CM) is a major apple pest. Current manual method of detection is not very effective. The development of nondestructive monitoring and detection methods has the potential to reduce postharvest losses from CM infestation. Previous work from o...
ver más
|
|
|
|
|
|
|
|
|
|
|
Lei Yang, Dexu Mu, Zhen Xu and Kaiyu Huang
Aiming at the problems of uneven light reflectivity on the spherical surface and high similarity between the stems/calyxes and scars that exist in the detection of surface defects in apples, this paper proposed a defect detection method based on image se...
ver más
|
|
|
|
|
|
|
|
|
|
|
Bashar Igried, Shadi AlZu?bi, Darah Aqel, Ala Mughaid, Iyad Ghaith and Laith Abualigah
Plant diseases represent one of the critical issues which lead to a major decrease in the quantity and quality of crops. Therefore, the early detection of plant diseases can avoid any losses or damage to these crops. This paper presents an image processi...
ver más
|
|
|
|
|
|