Rice yield prediction through integration of biophysical parameters with SAR and optical remote sensing data using machine learning models

Scientific Reports, Год журнала: 2024, Номер 14(1)

Опубликована: Сен. 17, 2024

Abstract In an era marked by growing global population and climate variability, ensuring food security has become a paramount concern. Rice, being staple crop for billions of people, requires accurate timely yield prediction to ensure security. This study was undertaken across two rice seasons in the Udham Singh Nagar district Uttarakhand state predict at 45, 60 90 days after transplanting (DAT) through machine learning (ML) models, utilizing combination optical Synthetic Aperture Radar (SAR) data conjunction with biophysical parameters. Results revealed that ML models were able provide relatively early estimates. For summer rice, eXtreme gradient boosting (XGB) best-performing model all three stages (45, 60, DAT), while kharif DAT XGB, Neural network (NNET), Cubist, respectively. The combined ranking showed accuracy improved as date approaches harvest, best observed both rice. Overall rankings indicate top NNET, Support vector regression, these Random Forest, findings this offer valuable insights into potential use remote sensing parameters using which enhances planning resource management enabling more informed decision-making stakeholders such farmers, policy planners well researchers.

Язык: Английский

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Winter Wheat Yield Prediction Based on the ASTGNN Model Coupled with Multi-Source Data

Agronomy, Год журнала: 2024, Номер 14(10), С. 2262 - 2262

Опубликована: Окт. 1, 2024

Timely and accurate prediction of winter wheat yields, which is crucial for optimizing production management, maintaining supply–demand balance, ensuring food security, depends on interactions among numerous factors, such as climate, surface characteristics, soil quality. Despite the extensive application deep learning models in this field, few studies have analyzed effect large-scale geospatial characteristics neighboring regions crop yields. Therefore, we present an attention-based spatio-temporal Graph Neural Network (ASTGNN) model coupled with multi-source data improved accuracy yield estimation. The datasets used study included multiple types remote sensing, meteorological, soil, yield, planting area Anhui, China, from 2005 to 2020. results showed that led higher performance than single-source data, enabled yields three months prior harvest. Furthermore, ASTGNN provided better two traditional (R2 = 0.70, RMSE 0.21 t/ha, MAE 0.17 t/ha). enhances by incorporating characteristics. This research has implications improving agricultural promoting development digital agriculture, addressing climate change agriculture.

Язык: Английский

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Algorithms for Plant Monitoring Applications: A Comprehensive Review

Algorithms, Год журнала: 2025, Номер 18(2), С. 84 - 84

Опубликована: Фев. 5, 2025

Many sciences exploit algorithms in a large variety of applications. In agronomy, amounts agricultural data are handled by adopting procedures for optimization, clustering, or automatic learning. this particular field, the number scientific papers has significantly increased recent years, triggered scientists using artificial intelligence, comprising deep learning and machine methods bots, to process crop, plant, leaf images. Moreover, many other examples can be found, with different applied plant diseases phenology. This paper reviews publications which have appeared past three analyzing used classifying agronomic aims crops applied. Starting from broad selection 6060 papers, we subsequently refined search, reducing 358 research articles 30 comprehensive reviews. By summarizing advantages applying analyses, propose guide farming practitioners, agronomists, researchers, policymakers regarding best practices, challenges, visions counteract effects climate change, promoting transition towards more sustainable, productive, cost-effective encouraging introduction smart technologies.

Язык: Английский

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Predicting rice productivity for ground data-sparse regions: A transferable framework and its application to North Korea

The Science of The Total Environment, Год журнала: 2024, Номер 946, С. 174227 - 174227

Опубликована: Июнь 25, 2024

Язык: Английский

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Unveiling the impact of mulching film promotion on the food–water–environment–plastic nexus

Resources Conservation and Recycling, Год журнала: 2024, Номер 209, С. 107780 - 107780

Опубликована: Июнь 19, 2024

Язык: Английский

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Accurate estimation of grain number per panicle in winter wheat by synergistic use of UAV imagery and meteorological data

International Journal of Applied Earth Observation and Geoinformation, Год журнала: 2024, Номер 136, С. 104320 - 104320

Опубликована: Дек. 17, 2024

Язык: Английский

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Curating Multimodal Satellite Imagery for Precision Agriculture Datasets with Google Earth Engine

Proceedings of The International Conference on Data Science and Official Statistics, Год журнала: 2023, Номер 2023(1), С. 368 - 381

Опубликована: Дек. 29, 2023

In the era of modern agriculture, satellite imagery has been widely used to monitor crops, one which is paddy. This paper tries describe vegetation indices, climate, and soil index features related paddy plants curates a collection on Google Earth Engine (GEE). reveals how GEE can be collect process multimodal form precision agriculture dataset. The objective this study establish comprehensive dataset by leveraging crops. data collected as originates from 306 locations in Karawang Regency, Indonesia, during 2019-2020 period. first step, we identify relevant essential for crop analysis. Subsequently, carefully select image collections within based these features. Afterward, perform acquisition necessary preprocessing through Colab environment. results showed that Sentinel-2 outperforms Landsat 8 terms spatial temporal resolution. Apart that, generated successfully captures growth patterns plants.

Язык: Английский

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Feasibility of Machine Learning-Based Rice Yield Prediction In India at the District Level Using Climate Reanalysis Data

Опубликована: Янв. 1, 2024

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Язык: Английский

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Accurate Estimation of Grain Number Per Panicle in Winter Wheat by Synergistic Use of Uav Imagery and Meteorological Data

Опубликована: Янв. 1, 2024

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Язык: Английский

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Rice Yield Analysis and Forecasting Using Phenology-Based Time-Series Features

Опубликована: Янв. 1, 2023

Rice yield depends on factors including variety, weather, field management, nutrient and water availability. We analyzed important drivers variability at the scale, developed forecast models for crops in temperate irrigated rice growing region of Australia. fused a time-series Sentinel-1 Sentinel-2 satellite remote sensing imagery, spatial weather data management information, while phenology was predicted using previously reported models. Higher yields were associated with early flowering, higher indices indicating nitrogen status, temperatures around successive cropping same lower yield. which aggregated to phenological periods provided more accurate than aggregating calendar periods. Yield particularly depended reflectances vegetation based red edge short wave infrared bands just before minimum temperature flowering. Final trained 2018-2022 data, applied predicting 1580 fields (43,700 hectares) from an independent season challenging conditions (2023). The accuracy improved as progressed, by 30 days after flowering reaching RMSE=1.6 t/ha Lin's concordance correlation coefficient (LCCC) 0.67 level. Explainability SHAP method, revealing likely causes low individual fields, status during due late sowing. ability predict inter-annual further validated 2006-2017 achieving RMSE=0.62 t/ha, LCCC=0.6 over seasons sowing methods.

Язык: Английский

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