Fertilization Management Zoning Based on Crop Canopy Spectral
Information
CHEN Hao1, WANG Xi1*, ZHANG Wei1, WANG Xin-zhong1, DI Xiao-dong1, WANG Chang2
1. College of Engineering，Heilongjiang Bayi Agricultural University，Daqing 163319，China
2. College of Science，Heilongjiang Bayi Agricultural University，Daqing 163319，China
Abstract：With the continuous development of ground remote sensing technology, more and more crop canopy spectral sensors are applied to agricultural production, among which the Greenseeker plant spectral detector is widely used. Greenseeker can obtain crop canopy spectral information, normalized vegetation index (NDVI) data and divide fertilization management zoning. Targeted variable rate fertilization can be realized according to fertilization management zoning. The fuzzy c-means (FCM) algorithm is common for dividing fertilization management zoning, but the FCM algorithm has certain limitations. In the calculation process, the iterative calculation will be carried out continuously with the increase of data, which will affect the speed of fertilization management zoning. Based on the FCM algorithm, a model-based fuzzy c-means (MFCM) algorithm is proposed. In dividing the fertilization management partition, this algorithm does not have to iteratively calculate all the data every time a group of data is obtained, which can improve the speed of dividing the fertilization management partition. The NDVI data of soybean and maize were obtained through the established crop canopy spectral information collection platform. The fertilization management zoning was divided by the MFCM algorithm, and the division effect was evaluated by evaluation index contour coefficient (SC) and adjusted rand index (ARI). The results show that with the increased NDVI data, the MFCM algorithm can partition fertilization management partition faster than the FCM algorithm. The MFCM algorithm is 0.02～0.15 seconds faster; the MFCM algorithm is 0.07～0.51 seconds faster in dividing maize fertilization management zoning. By calculating the indexes SC and ARI to evaluate the effect of dividing fertilization management zoning, it is found that when dividing different NDVI data, the maximum difference of SC value is 0.022, indicating that the effect of dividing fertilization management zoning by the two algorithms is not different; The ARI value is sensitive to data changes. It can be maintained above 0.7 after the NDVI data volume reaches 6 000, but it will decrease significantly when the NDVI data changes.
Key words：Greenseeker;Management zoning;Fuzzy c-means;Silhouette coefficient;Adjusted rand index
[1] BAO Yi-lin, MENG Xiang-tian, LIU Huan-jun, et al（鲍依临, 孟祥添, 刘焕军, 等）. Chinese Journal of Soil Science（土壤通报）, 2021, 52(04): 988.
[2] ZHU Chang-da, GAO Ming-xiu, WANG Wen-qian, et al（朱昌达, 高明秀, 王文倩, 等）. Acta Ecologica Sinica（生态学报）, 2020, 40(19): 6982.
[3] CHEN Shi-chao, DU Tai-sheng, WANG Su-fen（陈世超, 杜太生, 王素芬）. Transactions of The Chinese Society for Agricultural Machinery（农业机械学报）, 2019, 50(11): 293.
[4] YANG Jun-sen, YANG Gui-jun, XU Bo, et al（杨钧森, 杨贵军, 徐 波, 等）. Transactions of the Chinese Society of Agricultural Engineering（农业工程学报）, 2019, 35(8): 230.
[5] YU Jing, LI Fei, QIN Yong-lin, et al（于 静, 李 斐, 秦永林, 等）. Spectroscopy and Spectral Analysis（光谱学与光谱分析）, 2013, 33(11): 3092.
[6] ZHANG Ning, QI Bo, ZHAO Jin-ming, et al（张 宁, 齐 波, 赵晋铭, 等）. Acta Agronomica Sinica（作物学报）, 2014, 40(4): 657.
[7] LI Xin-wei, YU Bing-feng, LÜ Xin, et al（李新伟, 余炳凤, 吕 新, 等）. Transactions of The Chinese Society for Agricultural Machinery（农业机械学报）, 2014, 45(7): 231.
[8] Jiang R, Wang P, Xu Y, et al. Sensors, 2019, 19(8): 1859.
[9] Azimi R, Ghayekhloo M, Ghofrani M, et al. Expert Systems with Applications, 2017, 76: 59.
[10] Ünlü R, Xanthopoulos P. Expert Systems with Applications, 2019, 125: 33.
[11] Vinh N X, Epps J, Bailey J. The Journal of Machine Learning Research, 2010, 11: 2837.
[12] Steinley D. Psychological Methods, 2004, 9(3): 386.
[13] Dinh D-T, Fujinami T, Huynh V-N. Knowledge and Systems Sciences, 2019. 1.
[14] Hasanzadeh-Mofrad M, Rezvanian A. Journal of Computational Science, 2018, 24: 379.
[15] Duò A, Robinson M D, Soneson C. F1000Research, 2000, 7: 1141.
