Broad-Leaf Weed Detection in Pasture
Broad-Leaf Weed Detection in Pasture
复制标题
牧场阔叶杂草检测
DOI：
10.1109/icivc.2018.8492831
复制
发表时间：
2018
期刊：
影响因子：
--
通讯作者：
G. Wright
中科院分区：
文献类型：
--
作者：
Wenhao Zhang;M. Hansen;T. Volonakis;Melvyn L. Smith;Lyndon N. Smith;Jim Wilson;Graham Ralston;L. Broadbent;G. Wright
牧场杂草控制是一个具有挑战性的问题，既昂贵又不环保。本文提出了一种新的牧草阔叶杂草识别方法，从而在减少除草剂用量的情况下实现牧草杂草的精确控制。传统的机器学习算法和深度学习方法已经被探索和比较，以在现实环境中实现高检测精度和鲁棒性。捕获牧草/杂草图像数据用于分类器训练和算法验证。所提出的深度学习方法准确率达到96.88%，能够在各种具有代表性的室外光照条件下检测不同牧场的杂草。
Weed control in pasture is a challenging problem that can be expensive and environmentally unfriendly. This paper proposes a novel method for recognition of broad-leaf weeds in pasture such that precision weed control can be achieved with reduced herbicide use. Both conventional machine learning algorithms and deep learning methods have been explored and compared to achieve high detection accuracy and robustness in real-world environments. In-pasture grass/weed image data have been captured for classifier training and algorithm validation. The proposed deep learning method has achieved 96.88 % accuracy and is capable of detecting weeds in different pastures under various representative outdoor lighting conditions.
影响因子：
10
作者：
Hansen, Mark E.;Smith, Melvyn L.;Grieve, Bruce
通讯作者：
Grieve, Bruce