Zobrazeno 1 - 10
of 1 152
pro vyhledávání: '"weed detection"'
Publikováno v:
Artificial Intelligence in Agriculture, Vol 13, Iss , Pp 45-63 (2024)
Machine learning and deep learning are subsets of Artificial Intelligence that have revolutionized object detection and classification in images or videos. This technology plays a crucial role in facilitating the transition from conventional to preci
Externí odkaz:
https://doaj.org/article/5b542458eb684e47b12472596a9e7c84
Publikováno v:
Journal of Agricultural Engineering (2024)
Laser weeding is one of the promising weed control methods for weed management in organic agriculture. However, the complex field environments lead to low weed detection accuracy, which makes it difficult to meet the requirements of high-precision la
Externí odkaz:
https://doaj.org/article/1585841812a0446fa858f11522908c75
Autor:
Lintang Patria, Aceng Sambas, Ibrahim Mohammed Sulaiman, Mohamed Afendee Mohamed, Volodymyr Rusyn, Andrii Samila
Publikováno v:
Informatyka, Automatyka, Pomiary w Gospodarce i Ochronie Środowiska, Vol 14, Iss 3 (2024)
This study proposes a method based on Convolutional Neural Network (CNN) for automated detection of weed in color image format. The image is captured and transmitted to the Internet of Thing (IoT) server following an HTTP request made through the int
Externí odkaz:
https://doaj.org/article/488dfeb1877b452f8dc4eec5de60e3de
Publikováno v:
Smart Agricultural Technology, Vol 9, Iss , Pp 100648- (2024)
Weeds pose a serious production challenge in various agronomic crops by reducing their grain yields. Increasing cases of herbicide-resistant (HR) weed populations further exacerbate the problem. Future weed control tactics require the integration of
Externí odkaz:
https://doaj.org/article/224b22a3ae5c455ab85f83a7c3d094ba
Autor:
Pankaj Malkani, Indra Mani, Pramod Kumar Sahoo, Roaf Ahmad Parray, Sidhartha Sekhar Swain, Asha K．R．, Dharmender, Manojit Chowdhury, Sunil Kumar Rathod, Wahidah H․ Al-Qahtani, Ali Salem, Ahmed Elbeltagi, Abdallah Elshawadfy Elwakeel
Publikováno v:
Smart Agricultural Technology, Vol 9, Iss , Pp 100649- (2024)
A novel self-propelled smart herbicide applicator (1600 mm × 500 mm × 1100 mm) was developed using machine vision to target weeds and apply herbicides variably in wide row-spaced crops. The applicator integrates a weed detection system, smart spray
Externí odkaz:
https://doaj.org/article/1d1dc8f9824a42298217230deb41bdc0
Publikováno v:
Smart Agricultural Technology, Vol 9, Iss , Pp 100552- (2024)
Effective weed control in wheat (Triticum aestivum L.) fields is crucial for optimizing production and ensuring food security in semi-arid regions. The implementation of deep learning for weed detection could enable precise weed management, leading t
Externí odkaz:
https://doaj.org/article/78deb902894643ec85b33681a1e4e48d
Publikováno v:
Journal of Agriculture and Food Research, Vol 18, Iss , Pp 101388- (2024)
Hyperspectral remote sensors are emerging as valuable technology for identifying weeds in field crops. These sensors can acquire high-resolution images, both spatially and spectrally, which are crucial for early weed identification. The objective of
Externí odkaz:
https://doaj.org/article/ea93061eebcd4171a706a4b1aa4ffc30
Autor:
Sunil G C, Arjun Upadhyay, Yu Zhang, Kirk Howatt, Thomas Peters, Michael Ostlie, William Aderholdt, Xin Sun
Publikováno v:
Smart Agricultural Technology, Vol 9, Iss , Pp 100538- (2024)
The implementation of a machine-vision system for real-time precision weed management is a crucial step towards the development of smart spraying robotic vehicles. The intelligent machine-vision system, constructed using deep learning object detectio
Externí odkaz:
https://doaj.org/article/b70ce404338447d3bf1d345240771f06
Publikováno v:
Smart Agricultural Technology, Vol 8, Iss , Pp 100505- (2024)
Accurate weed detection in agricultural images is a crucial challenge for improving crop management practices and reducing chemical usage. In this study, we propose an innovative segmentation model called DWUNet, inspired by popular architectures and
Externí odkaz:
https://doaj.org/article/d6b73b24d0b04e09b7cf12ce15c2c5ff
Autor:
Kaixin Wang, Xihong Hu, Huiwen Zheng, Maoyang Lan, Changjiang Liu, Yihui Liu, Lei Zhong, Hai Li, Suiyan Tan
Publikováno v:
Frontiers in Plant Science, Vol 15 (2024)
IntroductionThe precise detection of weeds in the field is the premise of implementing weed management. However, the similar color, morphology, and occlusion between wheat and weeds pose a challenge to the detection of weeds. In this study, a CSCW-YO
Externí odkaz:
https://doaj.org/article/4221cc134932459c935cfff9e0596b31