Smart Plant Disease Diagnosis Using Multiple Deep Learning and Web Application Integration DOI Creative Commons

Ahmed M. S. Kheir,

Anis Koubâa,

Vinothkumar Kolluru

et al.

Journal of Agriculture and Food Research, Journal Year: 2025, Volume and Issue: 21, P. 101948 - 101948

Published: April 23, 2025

Language: Английский

Remote sensing-based winter wheat yield estimation integrating machine learning and crop growth multi-scenario simulations DOI Creative Commons
Xin Du, Jiong Zhu, Jingyuan Xu

et al.

International Journal of Digital Earth, Journal Year: 2025, Volume and Issue: 18(1)

Published: Jan. 27, 2025

Language: Английский

Citations

1
Water-cropland resources and agricultural management shape the main interactions with food self-sufficiency goals DOI Creative Commons
Achraf Mamassi, Nicolas Guilpart, Lucile Muneret

et al.

Global Food Security, Journal Year: 2025, Volume and Issue: 44, P. 100841 - 100841

Published: Feb. 24, 2025

Language: Английский

Citations

0
Stacked Ensemble Model for Accurate Crop Yield Prediction Using Machine Learning Techniques DOI Creative Commons

V. Ramesh,

P. Kumaresan

Environmental Research Communications, Journal Year: 2025, Volume and Issue: 7(3), P. 035006 - 035006

Published: Feb. 24, 2025

Abstract Predicting crop yields remains a crucial challenge in agriculture, as these forecasts influence decision-making at global, regional, and individual levels. Historically, such predictions have utilized diverse data sources, including agricultural, land, climatic, atmospheric, other pertinent information. Over the past several years, application of machine learning techniques has emerged valuable analytical approach for estimating agricultural productivity, thereby informing decisions regarding selection management strategies throughout entire growing cycle. Various kinds models been research to forecast yields. Our work proposes stacked ensemble model designed purpose predicting yield. The proposed employs approach, with Decision Tree Regressor functioning meta-model amalgamate from six distinct base learner models: Linear Regression (LR), Elastic Net, XGBoost Regressor, K-Neighbors (KNR), AdaBoost Random Forest (RFR). achieves superior yield prediction performance, evidenced by notable enhancement accuracy significant decrease RMSE, surpassing predictive capabilities traditional models. model’s performance was assessed using metrics, Mean Absolute Error 7.20 tons/hectare, Square 15570.32 tons 2 /hectare , Root 124.78 Coefficient Determination (R Score) 0.98. results demonstrate that outperforms conventional approaches, achieving high R-squared score 98%.

Language: Английский

Citations

0
Hybridization of process-based models, remote sensing, and machine learning for enhanced spatial predictions of wheat yield and quality DOI Creative Commons

Ahmed M. S. Kheir,

Ajit Govind, Vinay Nangia

et al.

Computers and Electronics in Agriculture, Journal Year: 2025, Volume and Issue: 234, P. 110317 - 110317

Published: March 23, 2025

Language: Английский

Citations

0
Remote sensing and TerraClimate datasets for wheat yield prediction using machine learning DOI Creative Commons
Alireza Araghi, André Daccache

Smart Agricultural Technology, Journal Year: 2025, Volume and Issue: unknown, P. 100909 - 100909

Published: March 1, 2025

Language: Английский

Citations

0
Efficient agronomic practices narrow yield gaps and alleviate climate change impacts on winter wheat production in China DOI Creative Commons

Kaiyuan Gong,

Liang-bing RONG,

Yinghua Zhang

et al.

Communications Earth & Environment, Journal Year: 2025, Volume and Issue: 6(1)

Published: April 16, 2025

Language: Английский

Citations

0
Smart Plant Disease Diagnosis Using Multiple Deep Learning and Web Application Integration DOI Creative Commons

Ahmed M. S. Kheir,

Anis Koubâa,

Vinothkumar Kolluru

et al.

Journal of Agriculture and Food Research, Journal Year: 2025, Volume and Issue: 21, P. 101948 - 101948

Published: April 23, 2025

Language: Английский

Citations

0