Abstract:The accurate, efficient and nondestructive detection of chlorophyll content in rice leaves using spectral information is of practical importance for diagnosing and optimizing nitrogen nutrition in rice leaves, developing and optimizing nitrogen fertilization systems in rice fields, and monitoring and evaluating rice pests and diseases. This paper addresses the problem of poor model accuracy and stability when machine learning models are used solely to invert the chlorophyll content of rice leaves. Moreover, takes Northeast japonica rice Jijing88 as the research object, obtains leaf phenotypic hyperspectral data and relative chlorophyll content at key fertility stages such as tillering through grid tests, select the kernel limit learning machine (Kernel function extreme learning machine, KELM) in machine learning as the base modeling model, and proposes a new idea of selecting preprocessing methods based on the base KELM modeling effect first, and then optimizing the KELM training process corresponding to the selected preprocessing combination using a bionic optimization algorithm to improve the model prediction accuracy. First, this paper investigates the preprocessing methods of spectral data, and a total of 72 preprocessing combinations are obtained by combining all four types of preprocessing methods. The sequential projection algorithm (Successive Projections Algorithm, SPA) is used to select the characteristic bands for input into the KELM model to filter the better preprocessing combinations. Based on the modeling effect, the test set’s coefficient of determination (R2p) corresponding to KELM for the pretreatment combinations CWT+MMS, CWT+MSC+SG+SS, and CWT+SS was higher, 0.850, 0.835, and 0.828, respectively. Secondly, to make the KELM model perform optimally while ensuring stability and generalization. In this paper, the Harris Hawk Optimization Algorithm (Harris Hawks Optimizer, HHO) is introduced to automatically and optimally adjust the parameters of the above three KELM models by simulating the cooperative behavior and chasing strategy of the hawks during predation, resulting in the HHO-KELM models with R2p of 0.957, 0.867 and 0.858, respectively, and a maximum of 10.7% effectively improves the model accuracy. The feasibility of the HHO algorithm to optimize the machine learning model to invert the chlorophyll content of rice leaves was demonstrated, which provides a strong reference and reference for the determination and assessment of chlorophyll content in northeastern japonica rice.
李晓凯,于海业,于 跃,王洪健,张 蕾,张 昕,隋媛媛. 基于仿生优化算法的水稻叶绿素含量反演模型[J]. 光谱学与光谱分析, 2023, 43(01): 93-99.
LI Xiao-kai, YU Hai-ye, YU Yue, WANG Hong-jian, ZHANG Lei, ZHANG Xin, SUI Yuan-yuan. Inversion Model of Clorophyll Content in Rice Based on a Bonic
Optimization Algorithm. SPECTROSCOPY AND SPECTRAL ANALYSIS, 2023, 43(01): 93-99.
[1] Wakiyama Y. Japan Agricultural Research Quarterly, 2016, 50(4): 329.
[2] GAN Hai-ming,YUE Xue-jun,HONG Tian-sheng,et al(甘海明,岳学军,洪添胜, 等). Journal of South China Agricultural University(华南农业大学学报), 2018, 39(3): 102.
[3] WANG Shu-wen, ZHAO Yue, WANG Li-feng,et al(王树文, 赵 越, 王丽凤, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2016, 32(20): 187.
[4] JIN Zhen-yu, TIAN Qing-jiu, HUI Fang-ming,et al(金震宇, 田庆久, 惠凤鸣, 等). Remote Sensing Technology and Application(遥感技术与应用), 2003, (3): 134.
[5] SUN Xiao-xiang,WANG Fang-dong,GUO Xi,et al(孙小香, 王芳东, 郭 熙, 等). Acta Agriculturae Universitis Jiangxiensis(江西农业大学学报), 2018, 40(3): 444.
[6] CUI Xiao-tao, CHANG Qing-rui, QU Chun-yan,et al(崔小涛, 常庆瑞, 屈春燕, 等). Journal of Northeast Agricultural University(东北农业大学学报),2020, 51(8): 74.
[7] LIU Tan, XU Tong-yu, YU Feng-hua,et al(刘 潭, 许童羽, 于丰华,等). Transactions of the Chinese Society for Agricultural Machinery(农业机械学报), 2020, 51(5): 456.
[8] LI Jun, LI Da-chao(李 军, 李大超). Acta Physica Sinica(物理学报), 2016, 65(13): 39.
[9] FENG Yu, CUI Ning-bo, GONG Dao-zhi,et al(冯 禹, 崔宁博, 龚道枝, 等). Transactions of the Chinese Society of Agricultural Engineering(农业工程学报), 2015, 31(S1): 153.
[10] WANG Ya, ZHOU Meng-ran, YAN Peng-cheng,et al(王 亚, 周孟然, 闫鹏程, 等). Journal of China Coal Society(煤炭学报), 2017, 42(9): 2427.
[11] Heidari A A, Mirjalili S, Faris H, et al. Future Generation Computer Systems, 2019, 97(16): 849.
[12] Hossein Moayedi,Abdolreza Osouli,Hoang Nguyen, et al. Engineering with Computers,2019,37(1): 121.
[13] Pei Du, Jianzhou Wang, Yan Hao, et al. Applied Soft Computing, 2020, 96(1): 106620.
[14] Chen H, Jiao S, Wang M, et al. Journal of Cleaner Production, 2020, 244(1): 118778.
[15] Seyfollahi A, Ghaffari A. Peer-to-Peer Networking and Applications, 2020, 13(6): 1886.
[16] Bacanin N, Vukobrat N, Zivkovic M, et al. International Conference on Intelligent and Fuzzy Systems. Springer, Cham, 2022, 308: 281
[17] Bannari A, Khurshid K S, Staenz K, et al. IEEE Transactions on Geoscience and Remote Sensing, 2007, 45(10): 3063.
[18] Huang G B, Zhu Q Y, Siew C K. IEEE International Joint Conference on Neural Networks,2004, 2: 985.
[19] Huang G B. Cognitive Computation, 2014, 6(3): 376.