Integrated phenology and climate in rice yields prediction using machine learning methods

Guo, Yahui; Fu, Yongshuo H.; Hao, Fang; Zhang, Xuan; Wu, Yifan; Jin, Xiuliang; Bryant, Christopher; Senthilnath, J.

doi:10.1016/j.ecolind.2020.106935

Cited by 192 publications

(89 citation statements)

References 85 publications

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“…Besides, our result is a useful supplement to phenological variables. Previous studies [ 14 ] had proven that phenological variables (phenological date) were closely correlated with crop yields. Phenological date variables can, directly and indirectly, influence the photosynthesis and respiration, which will change the accumulation of effective dry matter.…”

Section: Discussionmentioning

confidence: 99%

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Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Pan

Zhu

et al. 2021

Sensors

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Phenology is an indicator of crop growth conditions, and is correlated with crop yields. In this study, a phenological approach based on a remote sensing vegetation index was explored to predict the yield in 314 counties within the US Corn Belt, divided into semi-arid and non-semi-arid regions. The Moderate Resolution Imaging Spectroradiometer (MODIS) data product MOD09Q1 was used to calculate the normalized difference vegetation index (NDVI) time series. According to the NDVI time series, we divided the corn growing season into four growth phases, calculated phenological information metrics (duration and rate) for each growth phase, and obtained the maximum correlation NDVI (Max-R2). Duration and rate represent crop growth days and rate, respectively. Max-R2 is the NDVI value with the most significant correlation with corn yield in the NDVI time series. We built three groups of yield regression models, including univariate models using phenological metrics and Max-R2, and multivariate models using phenological metrics, and multivariate models using phenological metrics combined with Max-R2 in the whole, semi-arid, and non-semi-arid regions, respectively, and compared the performance of these models. The results show that most phenological metrics had a statistically significant (p < 0.05) relationship with corn yield (maximum R2 = 0.44). Models established with phenological metrics realized yield prediction before harvest in the three regions with R2 = 0.64, 0.67, and 0.72. Compared with the univariate Max-R2 models, the accuracy of models built with Max-R2 and phenology metrics improved. Thus, the phenology metrics obtained from MODIS-NDVI accurately reflect the corn characteristics and can be used for large-scale yield prediction. Overall, this study showed that phenology metrics derived from remote sensing vegetation indexes could be used as crop yield prediction variables and provide a reference for data organization and yield prediction with physical crop significance.

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Section: Discussionmentioning

confidence: 99%

“…Recently, some studies have employed remote sensing derived phenological variables to predict crop yields [ 14 , 15 ]. These phenological variables are phenological period date information and belong to the variables that monitor crop growth.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Pan

Zhu

et al. 2021

Sensors

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“…The features that fall under the groundwater characteristics group include transmissivity, water conductivity, aquifer type, rock layer permeability, as well as the number of micronutrients and hydrochemical analysis [42]. Other measurements that also significantly contribute to crop yield prediction are the cropland information [42], crop management data [25], phenology data [43].…”

Section: A Popular Features Used In Crop Yield Predictionmentioning

confidence: 99%

“…In a machine learning framework, learning can be done by using data and minimizing the loss or error (RMSE or MSE) that are experienced by using regression algorithms. The MLR analysis has been used in several applications [43] in which multi-independent variables was proved to be the most efficient and reliable compared to one independent variable [69]. The least-squares method (LSM) is widely used for regression estimation in MLR models.…”

Section: ) Machine Learning Algorithmsmentioning

confidence: 99%

“…Irrigation ratio [46], number of open wells (OW) [26] [41], number of tanks (TK) [ [40], GDD [24], growing degree days [46], killing degree days [46], day length [51] [13] [43], snow water [13], drought index (DI) [ [66], GCVI [50], VOD [46] [62], RVI [63] [39], GNDVI [63] [39], GRVI [63] [39], EVI2 [63], OSAVI [63] [39], WDRVI [63] [39], NDVIre [64], TSAVI [39], IPVI [39], MSAVI [39], GI [39], PVI [39], SAVI [39]], GESAVI [39], GLAI [39], CWSI [39], NDWI [39], GVI [39] LAI [42] [40] [65], FPAR [42], GPP [42], NIRv [56] [47], CDL [42], cropland census [42], satellite images from the landsat thematic mapper (TM) [42], satellite images from advanced wide field sensor (AWIFS) [42], empty-land [45], harrowed land [45], texture conditions [48], PVI [48].…”

Section: Irrigation Informationmentioning

confidence: 99%

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A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

et al. 2021

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Machine learning for major food crops breeding: Applications, challenges, and ways forward

et al. 2023

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Increasing the production of the three major food crops (MFCs), maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum), is essential to fulfilling the food demand for the growing human population. Increasing food production may require the integration of machine learning (ML) into plant breeding programs. However, developing ML tools to improve the production of MFCs is a daunting task due to the lack of quality data and the computation resources needed to process this information. Hence, this review discusses the recent applications of ML for improving MFCs production, including plant phenotyping, yield forecasting, and candidate gene prediction. Based on the challenges reported in recent ML experiments for MFCs, this review prescribes solutions to produce scalable ML models. This review provides valuable insights for future studies and promotes collective efforts among researchers implementing ML to enhance MFCs productivity.

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Integrated phenology and climate in rice yields prediction using machine learning methods

Cited by 192 publications

References 85 publications

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

Prediction of Crop Yield Using Phenological Information Extracted from Remote Sensing Vegetation Index

A Comprehensive Review of Crop Yield Prediction Using Machine Learning Approaches With Special Emphasis on Palm Oil Yield Prediction

Machine learning for major food crops breeding: Applications, challenges, and ways forward

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