Image-based and ML-driven analysis for assessing blueberry fruit quality

Heliyon, Год журнала: 2025, Номер unknown, С. e42288 - e42288

Опубликована: Янв. 1, 2025

Язык: Английский

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Prediction of Winter Wheat Parameters with Planet SuperDove Imagery and Explainable Artificial Intelligence

Agronomy, Год журнала: 2025, Номер 15(1), С. 241 - 241

Опубликована: Янв. 19, 2025

This study investigated the application of high-resolution satellite imagery from SuperDove satellites combined with machine learning algorithms to estimate spatiotemporal variability some winter wheat parameters, including relative leaf chlorophyll content (RCC), water (RWC), and aboveground dry matter (DM). The research was carried out within an experimental field in Southern Italy during 2024 growing season. Different (ML) were trained compared using spectral band data calculated vegetation indices (VIs) as predictors. Model performance assessed R2 RMSE. ML models tested random forest (RF), support vector regressor (SVR), extreme gradient boosting (XGB). RF outperformed other prediction RCC when VIs predictors (R2 = 0.81) RWC DM bands 0.71 0.87, respectively). explainability SHAP method. A analysis highlighted that GNDVI, Cl1, NDRE most important for predicting RCC, while yellow red prediction, nir prediction. best model found each target used its seasonal trend produce a map. approach highlights potential integrating remote monitoring wheat, which can sustainable farming practices.

Язык: Английский

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Improving wheat yield prediction through variable selection using Support Vector Regression, Random Forest, and Extreme Gradient Boosting

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 100791 - 100791

Опубликована: Янв. 1, 2025

Язык: Английский

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Enhancing food security: machine learning-based wheat yield prediction using remote sensing and climate data in Pakistan

Theoretical and Applied Climatology, Год журнала: 2025, Номер 156(5)

Опубликована: Апрель 25, 2025

Язык: Английский

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Field based evaluation of wheat cultivars under graded nitrogen levels in North-West India

Frontiers in Agronomy, Год журнала: 2025, Номер 7

Опубликована: Май 22, 2025

Nutrients uptake by plants from the soil depends on fertilizers applied, physical and chemical properties of soil, various environmental biological factors. Each nutrients have a positive or negative interaction with other in terms their availability plants. The purpose this study is to investigate effects successive increases nitrogen (N) macronutrient uptake, system productivity (SP), wheat equivalent yield (WEY) wheat. This was carried out split plot design three distinct N input (N0, N75, N150) main ten cultivars sub-plot over two consecutive years (2020-21 2021-22) New Delhi, India. highest SP 9.85 t/ha -1 , P & K grain (PUG) 21.6 23.8 kg/ha straw (PUS) 13 106.4 total phosphorus (TPU) 34.6 130.4 were obtained ‘HD 3249’ cultivar, followed 3117’. application N75 N150 increased 57.9% 99.2%, WEY 45.2% 61.5%, PUG 105.2% 227%, PUS 94% 182%, TPU 100.5% 208.7%, respectively, N0. findings indicate that fertilization positively influences wheat, 3117’ emerging as efficient candidates for optimizing utilization. These hold significant potential breeding programs aimed at enhancing nutrient while maintaining productivity. Furthermore, incorporating nitrification inhibition traits into these recommended develop climate-smart varieties.

Язык: Английский

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A Data-Driven Approach to Improve Cocoa Crop Establishment in Colombia: Insights and Agricultural Practice Recommendations from an Ensemble Machine Learning Model

AgriEngineering, Год журнала: 2024, Номер 7(1), С. 6 - 6

Опубликована: Дек. 28, 2024

This study addresses the critical challenge of limited understanding environmental factors influencing cocoa cultivation in Colombia, a region with significant production potential but diverse agroecological conditions. The fragmented nature existing agricultural data and lack targeted research hinder efforts to optimize productivity sustainability. To bridge this gap, employs data-driven approach, using advanced machine learning techniques such as supervised, unsupervised, ensemble models, analyze datasets provide actionable recommendations. By integrating from official Colombian sources, well NASA POWER database, geographical APIs, present proposes methodology systematically assess conditions classify regions for optimal cultivation. use an assembled model, combining clustering each cluster, offers more precise scalable establishment under Despite challenges dataset resolution localized climate variability, provides valuable insights comprehensive impacting plantation given location. key findings reveal that temperature, humidity, wind speed are crucial determinants growth, complex interactions affecting regional suitability. results offer guidance implementation adaptive practices resilience strategies, enabling sustainable systems. implementing better practices, countries Colombia can achieve higher market shares growing global demand

Язык: Английский

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Monitoring Wheat Crop Biochemical Responses to Random Rainfall Stress Using Remote Sensing: A Multi-Data Approach

IEEE Access, Год журнала: 2024, Номер 12, С. 174144 - 174157

Опубликована: Янв. 1, 2024

Язык: Английский

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Prediction of copper contamination in soil across EU using spectroscopy and machine learning: handling class imbalance problem

Smart Agricultural Technology, Год журнала: 2024, Номер unknown, С. 100728 - 100728

Опубликована: Дек. 1, 2024

Язык: Английский

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