AI & Environment Resource HubRichetti et al. · 2023-01-13
This article offers practical guidelines for applying deep learning to tabular agricultural data, using a cereal yield forecasting case study to illustrate best practices in model selection, data preprocessing, and cross-validation for new users tackling agronomic problems.
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Richetti et al.. 2023-01-13. A Methods Guideline for Deep Learning for Tabular Data in Agriculture with a Case Study to Forecast Cereal Yield. https://www.sciencedirect.com/science/article/pii/S0168169923000303?via%3Dihub (AI & Environment Resource Hub; record paper-334; collection snapshot 2026-10-04).
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Source sheet: Scientific Paper · Row 338 · Original ID: paper-334.
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Shared topics: Agriculture and food
This study develops a machine vision and deep learning framework, optimized with EfficientNetB7 and the Lion optimizer, that accurately identifies consumed food products across 32 categories, achieving up to 100% classification accuracy and supporting sustainable food consumption goals.
Shared topics: Agriculture and food
AI enhances precision agriculture through data-driven crop management, irrigation optimization, and pest control, but gaps remain in nutrient management and sensor integration.
Shared topics: Agriculture and food
Abeya is providing chocolate companies with cacao sourced directly from cacao cooperatives transitioning to regenerative practices. We are scaling regenerative agriculture by supporting tropical farmers with new technology and better prices. For farmers, the planet, and the companies who produce the products we love.
Shared topics: Agriculture and food
We are a data analytics company using aerial imagery and machine learning algorithms to optimize crop performance for farmers around the world. We make it possible for farmers to interact with this data through our mobile and web applications.