1 Laboratoire Société Mobilité Environnement, Département de Sociologie, Université Joseph KI-ZERBO (UJKZ), Ouagadougou, Burkina Faso.
2Laboratoire Sciences et Technologie (LaST), Unité de Formation et de Recherche en Sciences et Technique (UFR ST), Université Thomas SANKARA (UTS), 12 BP 417 Ouagadougou 12, Burkina Faso.
3 Graduate School of Agricultural and Life Sciences, The University of Tokyo, 1-1-1 Yayoi, Bunkyo, Tokyo 113-8657, Japan
4 Metropolitan Solar Inc., Washington, DC 20032, USA.
5 Key Laboratory of Crop Water Use and Regulation, Ministry of Agriculture, Farmland Irrigation Research Institute, Chinese Academy of Agricultural Sciences, 380 Hongli road, Xinxiang Henan 453003, P.R. China.
Research Article
World Journal of Advanced Research and Reviews, 2024, 24(02), 058-072
Received on 17 September 2024; revised on 27 October 2024; accepted on 30 October 2024
Water is a vital component for the growth of wheat and the quality of its grains. The measurement of plant water content (PWC) serves as a crucial indicator for assessing the water status of crops, thereby guiding effective irrigation management practices. Recent advancements in technology, particularly the use of multispectral and thermal imaging from unmanned aerial vehicles (UAVs), offer the capability to capture a comprehensive view of PWC variability across agricultural fields. This study aimed to create predictive models for PWC utilizing artificial neural networks (ANN), deep neural networks (DNN), and traditional stepwise regression techniques, all based on the analysis of multispectral and thermal imagery. To effectively evaluate the water content in wheat, we combined high-resolution thermal and multispectral imaging techniques with machine learning approaches. This assessment was carried out through three distinct experiments, which included one conducted in a rainout shelter and two performed under rainfed conditions. The findings demonstrated that UAV-derived multispectral imagery, when coupled with machine learning models, can effectively predict wheat plant water content with remarkable precision. Notably by considering all the dataset, DNN model exhibited superior performance (R2=0.96, ENS=0.98, RMSE=1.37%, MAE=0.98%) compared to both the ANN (R2=0.95, ENS=0.95, RMSE=1.88%, MAE=1.46%,) and the stepwise regression model (SRM) (R2=0.67, ENS=0.51, RMSE=10.79%, MAE=9.03%). Across all machine learning approaches, both the DNN and ANN models significantly outperformed the stepwise regression model in predictive accuracy. The statistical outcomes derived from the calibration phase indicate that both the trained Artificial Neural Networks (ANN) and Deep Neural Networks (DNN) serve as effective instruments for accurate prediction of PWC. Notably, the DNN network demonstrates superior accuracy compared to ANN.
Machine learning; Deep neural network; UAV sensors; Plant water content
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Adama Traore, Mahamadou Konare, Syed Tahir Ata-Ul-Karim, Sunusi Amin Abubakar, Seydou Traore, Aiwang Duan and Ben Zhao. Comparative analysis of deep neural network and artificial neural network for predicting wheat water content using UAV multispectral and thermal imagers. World Journal of Advanced Research and Reviews, 2024, 24(2), 058-072. Article DOI: https://doi.org/10.30574/wjarr.2024.24.2.3281