AI- and data-driven crop rotation planning

Article Agriculture, Multidisciplinary

Preseason crop type prediction using crop sequence boundaries

Jonathon Abernethy et al.

Summary: Preseason crop-type prediction is an important tool for agriculture, with various applications such as crop mapping and yield prediction. The United States Department of Agriculture provides valuable data through the Cropland Data Layer, which is used in machine learning models to predict future crop type. The authors propose a novel method using polygon-based boundaries to reduce computational burden and improve accuracy.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2023)

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Article Agronomy

AI- and data-driven pre-crop values and crop rotation matrices

Stefan Fenz et al.

EUROPEAN JOURNAL OF AGRONOMY (2023)

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Article Agriculture, Multidisciplinary

'Fruchtfolge': A crop rotation decision support system for optimizing cropping choices with big data and spatially explicit modeling

C. Pahmeyer et al.

Summary: The web-based, open source decision support system 'Fruchtfolge' provides crop and coarse manure fertilization management recommendations for each field, helping decision makers to make optimal cropping decisions. Applied to a German case study, the system offers a strategy to reduce profit losses in compliance with revised fertilization regulations, emphasizing the benefits of using such support tools for decision-making in a complex environment.

COMPUTERS AND ELECTRONICS IN AGRICULTURE (2021)

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Proceedings Paper Computer Science, Artificial Intelligence

Solving a Many-Objective Crop Rotation Problem with Evolutionary Algorithms

Christian von Lucken et al.

Summary: Crop rotation involves planting different types of crops in a planned sequence to improve profits and environmental outcomes. This study introduces a seven-objective crop rotation problem and proposes an evolutionary algorithm solution, with RVEA achieving the best evaluation metrics for the studied instance.

INTELLIGENT DECISION TECHNOLOGIES, KES-IDT 2021 (2021)

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Article Agriculture, Multidisciplinary