Ali A M, Thind H S, Sharma S, Singh Y. 2015. Site-specific nitrogen management in dry direct-seeded rice using chlorophyll meter and leaf colour chart. Pedosphere, 25, 72–81.

Ballard D H. 1981. Generalizing the Hough transform to detect arbitrary shapes. Pattern Recognition, 13, 111–122.

Baral B R, Pande K R, Gaihre Y K, Baral K R, Sah S K, Thapa Y B, Singh U. 2021. Real-time nitrogen management using decision support-tools increases nitrogen use efficiency of rice. Nutrient Cycling in Agroecosystems, 119, 355–368.

Che S G, Zhao B Q, Li Y T, Yuan L, Lin Z A, Hu S W, Shen B. 2016. Nutrient uptake requirements with increasing grain yield for rice in China. Journal of Integrative Agriculture, 15, 907–917.

Chen L S, Wang K. 2014. Diagnosing of rice nitrogen stress based on static scanning technology and image information extraction. Journal of Soil Science and Plant Nutrition, 14, 382–393.

Chlingaryan A, Sukkarieh S, Whelan B. 2018. Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review. Computers and Electronics in Agriculture, 151, 61–69.

Condori R H M, Romualdo L M, Bruno O M, de Cerqueira Luz P H. 2017. Comparison between traditional texture methods and deep learning descriptors for detection of nitrogen deficiency in maize crops. In: 2017 Workshop of Computer Vision (WVC). Curran Associates, New York, NY. pp. 7–12.

Coskun D, Britto D T, Kronzucker H J. 2017. The nitrogen-potassium intersection: membranes, metabolism, and mechanism. Plant Cell and Environment, 40, 2029–2041.

Dua M, Makhija D, Mishra P Y L, Manasa P. 2020. A CNN–RNN–LSTM based amalgamation for Alzheimer’s disease detection. Journal of Medical and Biological Engineering, 40, 688–706.

Ghosh M, Swain D K, Jha M K, Tewari V K, Bohra A. 2020. Optimizing chlorophyll meter (SPAD) reading to allow efficient nitrogen use in rice and wheat under rice–wheat cropping system in eastern India. Plant Production Science, 23, 270–285.

Gonzalez-Sanchez A, Frausto-Solis J, Ojeda-Bustamante W. 2014. Predictive ability of machine learning methods for massive crop yield prediction. Spanish Journal of Agricultural Research, 12, 313–328.

Goyal K, Singh N, Jindal S, Kaur R, Goyal A, Awasthi R. 2022. Kjeldahl method. Advanced Techniques of Analytical Chemistry, 1, 105.

Gu Q, Deng J S, Lu C, Shi Y Y, Wang K, Shen Z Q. 2012. Diagnosis of rice nitrogen nutrition based on spectral and shape characteristics of scanning leaves. Journal of Agricultural Machinery, 43, 170–174. (in Chinese)

He K M, Zhang X Y, Ren S Q, Sun J. 2016. Deep residual learning for image recognition. In: 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE Computer Society, New York, NY. pp. 770–778.

Hochreiter S, Schmidhuber J. 1997. Long short-term memory. Neural Computation, 9, 1735–1780.

Hou W F, Tränkner M, Lu J W, Yan J Y, Huang S Y, Ren T, Cong R H, Li X K. 2019. Interactive effects of nitrogen and potassium on photosynthesis and photosynthetic nitrogen allocation of rice leaves. BMC Plant Biology, 19, 1–13.

Huang G, Liu Z, Van Der Maaten L, Weinberger K Q. 2017. Densely connected convolutional network. In: 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR). IEEE, Piscataway, NJ. pp. 4700–4708.

Huang Y G, Luo D Q, Jiang X H, Li M, Li L J, Ji G M, Li S X, Xu Y. 2021. Effects of different proportions of nitrogen application on grain yield and quality of Yixiangyou 2115. Hybrid Rice, 36, 94–99. (in Chinese)

Kang Y, Nam J, Kim Y, Lee S, Seong D, Jang S, Ryu C. 2021. Assessment of regression models for predicting rice yield and protein content using unmanned aerial vehicle-based multispectral imagery. Remote Sensing, 13, 1508.

Khaki S, Wang L. 2019. Crop yield prediction using deep neural networks. Frontiers in Plant Science, 10, 621.

Krizhevsky A, Sutskever I, Hinton G E. 2012. Imagenet classification with deep convolutional neural networks. Communications of the ACM, 60, 84–90.

Kwok N M , Wang D, Jia X, Chen S Y, Fang G, Ha Q P. 2011. Gray world based color correction and intensity preservation for image enhancement. In: 2011 4th International Congress on Image and Signal Processing. IEEE, Piscataway, NJ. pp. 994–998.

Latwal A, Saxena S, Dubey S K, Choudhary K, Sehgal S, Ray S S. 2019. Evaluation of pre-harvest production forecasting of mustard crop in major producing states of India, under fasal project. International Archives of the Photogrammetry, Remote Sensing & Spatial Information Sciences, XLII–3/W6, 18–20.

Li H, Hu B, Chu C C. 2017. Nitrogen use efficiency in crops: lessons from Arabidopsis and rice. Journal of Experimental Botany, 68, 2477–2488.

Li L T, Zhang M, Ren T, Li X K, Cong R H, Wu L S, Lu J W. 2015. Diagnosis of N nutrition of rice using digital image processing technique. Journal of Plant Nutrition and Fertilizer, 21, 259–268. (in Chinese)

Li Q, Jin S C, Zang J R, Wang X, Sun Z Z, Li Z Y, Xu S, Ma Q, Su Y J, Guo Q H, Jiang D. 2022. Deciphering the contributions of spectral and structural data to wheat yield estimation from proximal sensing. The Crop Journal, 10, 1334–1345.

Li S X, Feng Z L, Yang B J, Li H, Liao F B, Gao Y F, Liu S H, Tang J, Yao Q. 2022. An intelligent monitoring system of diseases and pests on rice canopy. Frontiers in Plant Science, 13, 972286.

Lin T, Zhong R H, Wang Y D, Xu J F, Jiang H, Xu J L, Ying Y B, Rodriguez L, Ting K C, Li H F. 2020. DeepCropNet: A deep spatial-temporal learning framework for county-level corn yield estimation. Environmental Research Letters, 15, 034016.

Ling Q H, Wang S H, Ding Y F, Li G H. 2017. Re-evaluation of using the color difference between the top 3rd leaf and the 4th leaf as a unified indicator for high-yielding rice. Scientia Agricultura Sinica, 50, 4705–4713. (in Chinese)

Liu H J, Zhang H, Sheng J, Zhang Y F, Guo Z, Zheng J C, Chen J C, Chen L G. 2021. Effects of panicle fertilizer reduction on nitrogen use efficiency of rice under side deep application of basal fertilizer. Chinese Journal of Ecology, 40, 1366–1374. (in Chinese)

Ma P, Yang Z Y, Li Y, Lin D, Sun Y J, Ma J. 2019. Effects of nitrogen reduction in wheat/rape season and nitrogen fertilizer management in rice season on crop yield and nitrogen uptake in crop rotation system. Acta Agriculturae Zhejiangensis, 31, 1769–1778. (in Chinese)

Metropolis N, Ulam S. 1949. The monte carlo method. Journal of the American Statistical Association 44, 335–341.

Panda S S, Ames D P, Panigrahi S. 2010. Application of vegetation indices for agricultural crop yield prediction using neural network techniques. Remote Sensing, 2, 673–696.  

Peng S B, Buresh R J, Huang J L, Zhong X H, Zou Y B, Yang J C, Wang G H, Liu Y Y, Hu R F, Tang Q Y, Cui K H, Zhang F S, Dobermann A. 2010. Improving nitrogen fertilization in rice by sitespecific N management. A review. Agronomy for Sustainable Development, 30, 649–656.

Simonyan K, Zisserman A. 2015. Very deep convolutional networks for large-scale image recognition. In: International Conference on Learning Representations(ICLR). Ithaca, NY. pp.1–14. 

Son H H. 2017. Toward a proposed framework for mood recognition using LSTM Recurrent Neuron Network. Procedia Computer Science, 109, 1028–1034.

Sun G J, Liu S H, Luo H L, Feng Z L, Yang B J, Luo J, Tang J, Yao Q, Xu J J. 2022. Intelligent monitoring system of migratory pests based on searchlight trap and machine vision. Frontiers in Plant Science, 13, 897739.

Sun Y Y, Tong C, He S, Wang K, Chen L S. 2018. Identification of nitrogen, phosphorus, and potassium deficiencies based on temporal dynamics of leaf morphology and color. Sustainability, 10, 762.

Sun Z Z, Li Q, Jin S C, Song Y L, Xu S, Wang X, Cai J, Zhou Q, Ge Y, Zhang R N, Zhang J R, Jiang D. 2022. Simultaneous prediction of wheat yield and grain protein content using multitask deep learning from time-series proximal sensing. Plant Phenomics, 2022, 13.

Szegedy C, Liu W, Jia Y Q, Sermanet P, Reed S, Anguelov D, Erhan D, Vanhoucke V, Rabinovich A. 2015. Going deeper with convolutions. In: 2015 IEEEConference on Computer Vision and Pattern Recognition (CVPR). IEEE, Piscataway, NJ. pp. 1–9.

Taghavi Namin S, Esmaeilzadeh M, Najafi M, Brown T B, Borevitz J O. 2018. Deep phenotyping: deep learning for temporal phenotype/genotype classification. Plant Methods, 14, 1–14. 

Tran T T, Choi J W, Le T T H, Kim J W. 2019. A comparative study of deep CNN in forecasting and classifying the macronutrient deficiencies on development of tomato plant. Applied Sciences, 9, 1601.

Vitousek P M, Naylor R, Crews T, David M B, Drinkwater L E, Holland E, Johnes P J, Katzenberger J, Martinelli L A, Matson P A, Nziguheba G, Ojima D, Palm C A, Robertson G P, Sanchez P A, Townsend A R, Zhang F S. 2009. Nutrient imbalances in agricultural development. Science, 324, 1519–1520.

Wang L, Chen S S, Li D, Wang C Y, Jiang H, Zheng Q, Peng Z P. 2021. Estimation of paddy rice nitrogen content and accumulation both at leaf and plant levels from UAV hyperspectral imagery. Remote Sensing, 13, 2956.

Wang S W, Cheng X, Zhao Q H, Li X W, Zhang Y, Mani A, Wang R T. 2018. Estimating canopy nitrogen content of rice using hyperspectral reflectance combined with SG-FD-CARS-ELM in cold region. Systems Engineering, 3, 25–34.

Wang Y, Fu L D, Li X, Sui X, Ren H, Lv X H. 2013. Effect of nitrogen application amount on growth development and yield of rice. North Rice, 43, 14–17. (in Chinese)

Weiss K, Khoshgoftaar T M, Wang D D. 2016. A survey of transfer learning. Journal of Big Data, 3, 1–40.

Wood E M, Pidgeon A M, Radeloff V C, Keuler N S. 2012. Image texture as a remotely sensed measure of vegetation structure. Remote Sensing of Environment, 121, 516–526.

Wu G, Peng Y Q , Zhou G Q, Li X L, Zheng Y J, Yan H J. 2020. Recognition method for corn nutrient based on multispectral image and convolutional neural network. Smart Agriculture, 2, 111. (in Chinese)

Xiong J T, Dai S X, Ou J H, Lin X Y, Huang Q H, Yang Z G. 2020. Leaf deficiency symptoms detection method of soybean based on deep learning. Journal of Agricultural Machinery, 51, 195–202. (in Chinese)

Yao Q, Feng J, Tang J, Xu W G, Zhu X H, Yang B J, Lv J, Xie Y Z, Yao B, Wu S Z, Kuai N Y, Wang L J. 2020. Development of an automatic monitoring system for rice light-trap pests based on machine vision. Journal of Integrative Agriculture, 19, 2500–2513.

Yao Q, Li C, Li B, Yi J, Jing T T, Lv B. 2021. Preliminary study on the application of deep learning method in nitrogen nutrition diagnosis of rice. South China Agriculture, 15, 125–129. (in Chinese) 

Zhang Z S, Xia J Q, Alfatih A, Song Y, Huang Y J, Sun L Q,Wan G Y, Wang S M, Wang Y P, Hu B H, Zhang G H, Qin P, Li S G, Yu L H, Wu J, Xiang C B. 2022. Rice NIN-LIKE PROTEIN 3 modulates nitrogen use efficiency and grain yield under nitrate-sufficient conditions. Plant Cell and Environment, 45, 1520–1536.

Zheng H B, Zhou M, Zhu Y, Cheng T. 2019. Exploiting the textural information of UAV multispectral imagery to monitor nitrogen status in rice. In: 2019 IEEE International Geoscience and Remote Sensing Symposium. IEEE, Piscataway, NJ. pp. 7251–7253.