Identification of nitrogen, phosphorus, and potassium deficiencies in rice based on static scanning technology and hierarchical identification method.
Identification of nitrogen, phosphorus, and potassium deficiencies in rice based on static scanning technology and hierarchical identification method.
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DOI：
10.1371/journal.pone.0113200
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发表时间：
2014
期刊：
影响因子：
3.7
通讯作者：
Deng J
中科院分区：
文献类型：
--
作者：
Chen L;Lin L;Cai G;Sun Y;Huang T;Wang K;Deng J
建立准确、快速、可操作的作物营养诊断方法对作物养分管理具有重要意义。本研究利用静态扫描技术采集水稻样品完全展开的顶三叶及其相应叶鞘的图像。从这些图像中，32个光谱和形状特征参数提取使用RGB均值函数和使用MATLAB中的Regionprops函数。层次鉴定用于确定氮磷钾缺乏。首先，鉴定正常样品和非正常（NPK缺乏）样品。然后，确定了N缺乏和PK缺乏。最后，确定了缺磷和缺钾。在每个层次的识别中，针对不同的缺陷，采用SVFS有针对性地选择最优特征集，并采用Fisher判别分析建立诊断模型。在第一层次中，选择性状为叶鞘R、叶鞘G、叶鞘B、叶鞘长度、叶尖R、叶尖G、叶面积和叶G。在第二层次中，选择的性状为叶鞘G、叶鞘B、叶鞘白色区、叶B和叶G。在第三层次中，选择的性状为叶长、叶鞘长、叶面积/叶长、叶尖长、第二、第三叶长差、叶鞘长、叶亮度。结果表明，4个生育期氮磷钾营养缺乏综合判别准确率分别为86.15%、87.69%、90.00%和89.23%。利用多年数据进行验证，识别准确率分别为83.08%、83.08%、89.23%和90.77%。
Establishing an accurate, fast, and operable method for diagnosing crop nutrition is very important for crop nutrient management. In this study, static scanning technology was used to collect images of a rice sample's fully expanded top three leaves and corresponding sheathes. From these images, 32 spectral and shape characteristic parameters were extracted using an RGB mean value function and using the Regionprops function in MATLAB. Hierarchical identification was used to identify NPK deficiencies. First, the normal samples and non-normal (NPK deficiencies) samples were identified. Then, N deficiency and PK deficiencies were identified. Finally, P deficiency and K deficiency were identified. In the identification of every hierarchy, SVFS was used to select the optimal characteristic set for different deficiencies in a targeted manner, and Fisher discriminant analysis was used to build the diagnosis model. In the first hierarchy, the selected characteristics were the leaf sheath R, leaf sheath G, leaf sheath B, leaf sheath length, leaf tip R, leaf tip G, leaf area and leaf G. In the second hierarchy, the selected characteristics were the leaf sheath G, leaf sheath B, white region of the leaf sheath, leaf B, and leaf G. In the third hierarchy the selected characteristics were the leaf G, leaf sheath length, leaf area/leaf length, leaf tip G, difference between the 2nd and 3rd leaf lengths, leaf sheath G, and leaf lightness. The results showed that the overall identification accuracies of NPK deficiencies were 86.15, 87.69, 90.00 and 89.23% for the four growth stages. Data from multiple years were used for validation, and the identification accuracies were 83.08, 83.08, 89.23 and 90.77%.
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利用数码相机诊断水稻氮素状态
DOI：
10.3964/j.issn.1000-0593(2009)08-2176-04
发表时间：
2009-08-01
影响因子：
0.7
作者：
Jia Liang-liang;Fan Ming-sheng;Sun Yan-ming
通讯作者：
Sun Yan-ming
DOI：
10.3964/j.issn.1000-0593(2009)09-2467-04
发表时间：
2009-09-01
影响因子：
0.7
作者：
Lin Fen-fang;Ding Xiao-dong;Shen Zhang-quan
通讯作者：
Shen Zhang-quan
影响因子：
13.5
作者：
SHIBAYAMA, M;TAKAHASHI, W;AKIYAMA, T
通讯作者：
AKIYAMA, T
影响因子：
13.5
作者：
MILTON, NM;EISWERTH, BA;AGER, CM
通讯作者：
AGER, CM
影响因子：
13.5
作者：
SHIBAYAMA, M;AKIYAMA, T
通讯作者：
AKIYAMA, T