Integrating field images and microclimate data to realize multi-day ahead forecasting of maize crop coverage using CNN-LSTM

Xin Wang, Yu Yang, Xin Zhao, Min Huang, Qibing Zhu

Abstract


Crop coverage (CC) is an important parameter to represent crop growth characteristics, and the ahead forecasting of CC is helpful to track crop growth trends and guide agricultural management decisions. In this study, a novel CNN-LSTM model that combined the advantages of convolutional neural network (CNN) in feature extraction and long short-term memory (LSTM) in time series processing was proposed for multi-day ahead forecasting of maize CC. Considering the influence of climate change on maize growth, five microclimatic factors were combined with historical maize CC estimated from field images as the input variables of the forecasting model. The field experimental data of four observation points for more than three years were used to evaluate the performance of CNN-LSTM at the forecasting horizon of three to seven days ahead and compared the forecasting results to CNN and LSTM. The results demonstrated that CNN-LSTM obtained the lowest RMSE and the highest R2 at all forecasting horizons. Subsequently, the performance of CNN-LSTM under univariate (historical maize CC) and multivariate (historical maize CC+microclimatic factors) input was compared, and the results indicated that additional microclimatic factors were effective in improving the forecasting performance. Furthermore, the 3-day ahead forecasting results of CNN-LSTM in different growth stages of maize were also analyzed, and the results showed that the highest forecasting accuracy was obtained in the seven leaves stage. Therefore, CNN-LSTM can be considered a useful tool to forecast maize CC.
Keywords: maize crop coverage, multi-day ahead forecasting, CNN-LSTM, field images, microclimatic factors
DOI: 10.25165/j.ijabe.20231602.7020

Citation: Wang X, Yang Y, Zhao X, Huang M, Zhu Q B. Integrating field images and microclimate data to realize multi-day ahead forecasting of maize crop coverage using CNN-LSTM. Int J Agric & Biol Eng, 2023; 16(2): 199-206.

Keywords


maize crop coverage, multi-day ahead forecasting, CNN-LSTM, field images, microclimatic factors

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References


Maes W H, Steppe K. Perspectives for remote sensing with unmanned aerial vehicles in precision agriculture. Trends in Plant Science, 2019; 24(2): 152-164.

Sui R X, Thomasson J A, Ge Y F. Development of sensor systems for precision agriculture in cotton. Int J Agric & Biol Eng, 2012; 5(4): 1-14.

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

Purevdorj T S, Tateishi R, Ishiyama T, Honda Y. Relationships between percent vegetation cover and vegetation indices. International Journal of Remote Sensing, 1998; 19(18): 3519-3535.

Guevara-Escobar A, Tellez J, Gonzalez-Sosa E. Use of digital photography for analysis of canopy closure. Agroforestry Systems, 2005; 65(3): 175-185.

Tao Z Q, Bagum S A, Ma W, Zhou B Y, Fu J D, Cui R X, et al. Establishment of the crop growth and nitrogen nutrition state model using spectral parameters canopy cover. Spectroscopy and Spectral Analysis, 2016; 36(1): 231-236. (in Chinese)

Purcell L C. Soybean canopy coverage and light interception measurements using digital imagery. Crop Science, 2000; 40(3): 834-837.

Cheng M H, Jiao X Y, Liu Y D, Shao M C, Yu X, Bai Y, et al. Estimation of soil moisture content under high maize canopy coverage from UAV multimodal data and machine learning. Agricultural Water Management, 2022; 264: 107530. doi: 10.1016/j.agwat.2022.107530.

Lee K J, Lee B W. Estimation of rice growth and nitrogen nutrition status using color digital camera image analysis. European Journal of Agronomy, 2013; 48: 57-65.

García-Martínez H, Flores-Magdaleno H, Ascencio-Hernández R, Khalil-Gardezi A, Tijerina-Chávez L, Mancilla-Villa O R, et al. Corn grain yield estimation from vegetation indices, canopy cover, plant density, and a neural network using multispectral and RGB images acquired with unmanned aerial vehicless. Agriculture, 2020; 10(7): 277. doi: 10.3390/agriculture10070277.

De la Casa A, Ovando G, Bressanini L, Martínez J, Díaz G, Miranda C. Soybean crop coverage estimation from NDVI images with different spatial resolution to evaluate yield variability in a plot. ISPRS Journal of Photogrammetry and Remote Sensing, 2018; 146: 531-547.

Liu Y K, Mu X H, Wang H X, Yan G J. A novel method for extracting green fractional vegetation cover from digital images. Journal of Vegetation Science, 2012; 23(3): 406-418.

Chianucci F, Lucibelli A, Dell'Abate M T. Estimation of ground canopy cover in agricultural crops using downward-looking photography. Biosystems Engineering, 2018; 169: 209-216.

Coy A, Rankine D, Taylor M, Nielsen D C, Cohen J. Increasing the accuracy and automation of fractional vegetation cover estimation from digital photographs. Remote Sensing, 2016; 8(7): 474. doi: 10.3390/rs8070474.

Li T Y, Hua M, Wu X. A hybrid CNN-LSTM model for forecasting particulate matter (PM2. 5). IEEE Access, 2020; 8: 26933-26940.

Matsumura K, Gaitan C F, Sugimoto K, Cannon A J, Hsieh W W. Maize yield forecasting by linear regression and artificial neural networks in Jilin, China. The Journal of Agricultural Science, 2015; 153(3): 399-410.

Ferreira L B, da Cunha F F. Multi-step ahead forecasting of daily reference evapotranspiration using deep learning. Computers and Electronics in Agriculture, 2020; 178: 105728. doi: 10.1016/j.compag.2020.105728.

Butler E E, Huybers P. Adaptation of US maize to temperature variations. Nature Climate Change, 2013; 3(1): 68-72.

Mistry P, Bora G. Development of yield forecast model using multiple regression analysis and impact of climatic parameters on spring wheat. Int J Agric & Biol Eng, 2019; 12(4): 110-115.

Long Z H, Qin J M, Zhang T Y, Xu W. Prediction of continuous time series leaf area index based on long short-term memory network: A case study of winter wheat. Spectroscopy and Spectral Analysis, 2020; 40(3): 898-904. (in Chinese)

Sayeed A, Choi Y, Eslami E, Lops Y, Roy A, Jung J. Using a deep convolutional neural network to predict 2017 ozone concentrations, 24 hours in advance. Neural Networks, 2020; 121: 396-408. doi: 10.1016/j.neunet.2019.09.033.

Livieris I E, Pintelas E, Pintelas P. A CNN–LSTM model for gold price time-series forecasting. Neural Computing and Applications, 2020; 32(23): 17351-17360.

Ye M N, Cao Z G, Yu Z H, Bai X D. Crop feature extraction from images with probabilistic superpixel Markov random field. Computers and Electronics in Agriculture, 2015; 114: 247-260.

Wang Q L, Guo Y F, Yu L X, Li P. Earthquake prediction based on spatio-temporal data mining: an LSTM network approach. IEEE Transactions on Emerging Topics in Computing, 2017; 8(1): 148-158.

LeCun Y, Bottou L, Bengio Y, Haffner P. Gradient-based learning applied to document recognition. Proceedings of the IEEE, 1998; 86(11): 2278-2324.

Hochreiter S, Schmidhuber J. Long short-term memory. Neural Computation, 1997; 9(8): 1735-1780.

Zhang X L, Lin T, Xu J F, Luo X, Ying Y B. DeepSpectra: An end-to-end deep learning approach for quantitative spectral analysis. Analytica Chimica Acta, 2019; 1058: 48-57.

Hochreiter S. The vanishing gradient problem during learning recurrent neural nets and problem solutions. International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 1998; 6(2): 107-116.

Kingma D P, Ba J L. Adam: A method for stochastic optimization. In: Proceedings of the International Conference on Learning Representations (ICLR)), 2015; In press. arXiv: 1412.6980.

Zhang J F, Zhu Y, Zhang X P, Ye M, Yang J Z. Developing a Long Short-Term Memory (LSTM) based model for predicting water table depth in agricultural areas. Journal of Hydrology, 2018; 561: 918-929.

Jung D H, Kim H S, Jhin C, Kim H J, Park S H. Time-serial analysis of deep neural network models for prediction of climatic conditions inside a greenhouse. Computers and Electronics in Agriculture, 2020; 173: 105402. doi: 10.1016/j.compag.2020.105402.

Yan R, Liao J, Yang J Q, Sun W, Nong M Y, Li F P. Multi-hour and multi-site air quality index forecasting in Beijing using CNN, LSTM, CNN-LSTM, and spatiotemporal clustering. Expert Systems with Applications, 2021; 169: 114513. doi: 10.1016/j.eswa.2020.114513.

Yue Y, Li J H, Fan L F, Zhang L L, Zhao P F, Zhou Q, et al. Prediction of maize growth stages based on deep learning. Computers and Electronics in Agriculture, 2020; 172: 105351. doi: 10.1016/j.compag.2020.105351.




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