科研成果

Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery

作者:  來源:  發(fā)布日期:2019-03-11  瀏覽次數(shù):

  論文信息:Su, J.*, Liu, C., Coombes, M., Hu, X.*, Wang, C., Xu, X., Li, Q., Guo, L., Chen, W. Wheat yellow rust monitoring by learning from multispectral UAV aerial imagery. Computers and Electronics in Agriculture, 2018, 155: 157-166.

  JCR分類:Q1區(qū),中科院大類:3區(qū)

  論文摘要:The use of a low-cost five-band multispectral camera (RedEdge, MicaSense, USA) and a low-altitude airborne platform is investigated for the detection of plant stress caused by yellow rust disease in winter wheat for sustainable agriculture. The research is mainly focused on: (i) determining whether or not healthy and yellow rust infected wheat plants can be discriminated; (ii) selecting spectral band and Spectral Vegetation Index (SVI) with a strong discriminating capability; (iii) developing a low-cost yellow rust monitoring system for use at farmland scales. An experiment was carefully designed by infecting winter wheat with different levels of yellow rust inoculum, where aerial multispectral images under different developmental stages of yellow rust were captured by an Unmanned Aerial Vehicle at an altitude of 16–24m with a ground resolution of 1–1.5cm/pixel. An automated yellow rust detection system is developed by learning (via random forest classifier) from labelled UAV aerial multispectral imagery. Experimental results indicate that: (i) good classification performance (with an average Precision, Recall and Accuracy of 89.2%, 89.4% and 89.3%) was achieved by the developed yellow rust monitoring at a diseased stage 45 days after inoculation); (ii) the top three SVIs for separating healthy and yellow rust infected wheat plants are RVI, NDVI and OSAVI; while the top two spectral bands are NIR and Red. The learnt system was also applied to the whole farmland of interest with a promising monitoring result. It is anticipated that this study by seamlessly integrating low-cost multispectral camera, low-altitude UAV platform and machine learning techniques paves the way for yellow rust monitoring at farmland scales.

相關(guān)附件:
編輯:0
終審:0
双桥区| 民县| 甘孜县| 隆昌县| 甘南县| 宁强县| 凤山市| 永济市| 益阳市| 建德市| 沽源县| 东明县| 贺州市| 磐石市| 新龙县| 安阳市| 文化| 当涂县| 高清| 柳州市| 德格县| 中江县| 丽江市| 云龙县| 黔西| 本溪| 密山市| 资阳市| 项城市| 新巴尔虎左旗| 浦城县| 仁化县| 丹江口市| 中方县| 隆化县| 宁国市| 汪清县| 介休市| 盐边县| 蚌埠市| 观塘区|