Towards site-specific nutrient management strategies: An open database in Senegal

The Science of The Total Environment, Journal Year: 2025, Volume and Issue: 969, P. 178926 - 178926

Published: Feb. 24, 2025

Language: Английский

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A systematic review of open data in agriculture

Computers and Electronics in Agriculture, Journal Year: 2024, Volume and Issue: 219, P. 108775 - 108775

Published: Feb. 28, 2024

In this work, we perform a systematic literature review of Open Data and Public Domain datasets in Agriculture. We use the PRISMA method to analyze existing academic about open data agriculture, concretely 1401 papers from IEEE Xplore Web Science collections, published 2012 2022. Many these articles or make available very different typologies, like sensor data, statistical satellite images, among others kinds. Some talk relevant topics that influence lack for research purposes, barriers adopting sharing privacy concerns, data-sharing recommendations guidelines. addition, with help script created ad-hoc degree compliance within FAIR principles all public domain have been able identify literature, 104 datasets. The can check Gen2 maturity indicators list resources. Using metrics, those might be at risk stopping started "rescue operation". For whose terms permitted it, migrated Zenodo, repository complies availability principles. This will ensure future survival valuable

Language: Английский

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Review of Methods and Models for Potato Yield Prediction

Agriculture, Journal Year: 2025, Volume and Issue: 15(4), P. 367 - 367

Published: Feb. 9, 2025

This article provides a comprehensive overview of the development and application statistical methods, process-based models, machine learning, deep learning techniques in potato yield forecasting. It emphasizes importance integrating diverse data sources, including meteorological, phenotypic, remote sensing data. Advances computer technology have enabled creation more sophisticated such as mixed, geostatistical, Bayesian models. Special attention is given to techniques, particularly convolutional neural networks, which significantly enhance forecast accuracy by analyzing complex patterns. The also discusses effectiveness other algorithms, Random Forest Support Vector Machines, capturing nonlinear relationships affecting yields. According standards adopted agricultural research, Mean Absolute Percentage Error (MAPE) implementation prediction issues should generally not exceed 15%. Contemporary research indicates that, through use advanced accurate value this error can reach levels even less than 10 per cent, increasing efficiency Key challenges field include climatic variability difficulties obtaining on soil properties agronomic practices. Despite these challenges, technological advancements present new opportunities for Future focus leveraging Internet Things (IoT) real-time collection impact biological variables yield. An interdisciplinary approach, insights from ecology meteorology, recommended develop innovative predictive exploration methods has potential advance knowledge forecasting support sustainable

Language: Английский

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Sovereignty by design and human values in agriculture data spaces

Agriculture and Human Values, Journal Year: 2025, Volume and Issue: unknown

Published: Jan. 9, 2025

Language: Английский

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Reinforcement Learning-based Assimilation of the WOFOST Crop Model

Smart Agricultural Technology, Journal Year: 2024, Volume and Issue: 9, P. 100604 - 100604

Published: Oct. 11, 2024

Language: Английский

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