气候变化领域集成服务门户(CCPortal): Accurate Wheat Yield Prediction Using Machine Learning and Climate-NDVI Data Fusion

CCPortal

DOI	10.1109/ACCESS.2024.3376735
	Accurate Wheat Yield Prediction Using Machine Learning and Climate-NDVI Data Fusion
	Ashfaq, Muhammad; Khan, Imran; Alzahrani, Abdulrahman; Tariq, Muhammad Usman; Khan, Humera; Ghani, Anwar
发表日期	2024
ISSN	2169-3536
起始页码	12
卷号	12
英文摘要	Due to exponential population growth, climate change, and an increasing demand for food, there is an unprecedented need for a timely, precise, and dependable assessment of crop yield on a large scale. Wheat, a staple crop worldwide, requires accurate and prompt prediction of its output for global food security. Traditionally, the development of empirical models for crop yield forecasting has relied on climate data, satellite data, or a combination of both. Despite the enhanced performance achieved by integrating satellite and climate data, the contributions from various sources (Climate, Soil, Socioeconomic, and Remote sensing) remain unclear. The lack of well-defined comparisons between the performance of regression-based approaches and different Machine Learning (ML) methods in yield prediction necessitates further investigation. This study addresses the gaps by combining data from multiple sources to forecast wheat yield in the Multan region in the Punjab province of Pakistan. The findings are compared to the benchmark provided by Crop Report Services (CRS) Punjab, with three widely used ML techniques (support vector machine (SVM), Random Forest (RF), and Least Absolute Shrinkage and Selection Operator (LASSO)) by integrating publicly available data within the GEE (Google Earth Engine) platform, including climate, satellite, soil properties, and spatial information data to develop alternative empirical models for yield prediction using data from 2017 to 2022, selecting the best attribute subset related to crop output. The district-level simulated yield data set was analyzed with three ML models (SVM, RF, and LASSO) as a function of seasonal weather, satellite, and soil. The results indicate that combining all datasets using three ML algorithms achieves better yield prediction performance (R-2 : 0.74-0.88). Incorporating spatial information and other properties into benchmark models can improve the prediction from 0.08 to 0.12. Random forest outperformed the competitor models with a Root Mean Square Error (RMSE) of 0.05 q/ha and R-2 of 0.88. Comparative analysis shows that random forest with 97% and SVM with 93% yielded better results in the study area.
英文关键词	Predictive models; RF; LASSO; remote sensing; SVM; CNN; crop yield prediction
语种	英语
WOS研究方向	Computer Science ; Engineering ; Telecommunications
WOS类目	Computer Science, Information Systems ; Engineering, Electrical & Electronic ; Telecommunications
WOS记录号	WOS:001192246900001
来源期刊	IEEE ACCESS
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/304037
作者单位	International Islamic University, Pakistan; International Islamic University, Pakistan; University of Jeddah; Abu Dhabi University; Northern Border University; Jeju National University
推荐引用方式 GB/T 7714	Ashfaq, Muhammad,Khan, Imran,Alzahrani, Abdulrahman,et al. Accurate Wheat Yield Prediction Using Machine Learning and Climate-NDVI Data Fusion[J],2024,12.
APA	Ashfaq, Muhammad,Khan, Imran,Alzahrani, Abdulrahman,Tariq, Muhammad Usman,Khan, Humera,&Ghani, Anwar.(2024).Accurate Wheat Yield Prediction Using Machine Learning and Climate-NDVI Data Fusion.IEEE ACCESS,12.
MLA	Ashfaq, Muhammad,et al."Accurate Wheat Yield Prediction Using Machine Learning and Climate-NDVI Data Fusion".IEEE ACCESS 12(2024).