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Agronomy
Volume 15
Issue 2
10.3390/agronomy15020356
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This is an early access version, the complete PDF, HTML, and XML versions will be available soon.
Article

Machine Learning-Powered Segmentation of Forage Crops in RGB Imagery Through Artificial Sward Images

by
Hugo Moreno
1,
Christian Rueda-Ayala
2,
Victor Rueda-Ayala
3,
Angela Ribeiro
1,
Carlos Ranz
1 and
Dionisio Andújar
1,*
1
Centre for Automation and Robotics, Consejo Superior Investigaciones Científicas (CSIC), Ctra. de Campo Real km 0.200 La Poveda, 28500 Arganda del Rey, Madrid, Spain
2
Instituto Nacional de Investigación y Tecnología Agraria y Alimentaria (INIA), Consejo Superior Investigaciones Científicas (CSIC), Carretera de la Coruña, km 7, 5, 28040 Madrid, Spain
3
Weed Science in Arable Crops, Agroscope, Route de Duillier 60, 1260 Nyon, Switzerland
*
Author to whom correspondence should be addressed.
Agronomy 2025, 15(2), 356; https://doi.org/10.3390/agronomy15020356
Submission received: 12 November 2024 / Revised: 18 December 2024 / Accepted: 27 January 2025 / Published: 29 January 2025
(This article belongs to the Section Precision and Digital Agriculture)
Versions Notes

Abstract

Accurate assessment of forage quality is essential for ensuring optimal animal nutrition. Key parameters, such as Leaf Area Index (LAI) and grass coverage, are indicators that provide valuable insights into forage health and productivity. Accurate measurement is essential to ensure that livestock obtain the proper nutrition during various phases of plant growth. This study evaluated machine learning (ML) methods for non-invasive assessment of grassland development using RGB imagery, focusing on ryegrass and Timothy (Lolium perenne L. and Phleum pratense L.). ML models were implemented to segment and quantify coverage of live plants, dead material, and bare soil at three pasture growth stages (leaf development, tillering, and beginning of flowering). Unsupervised and supervised ML models, including a hybrid approach combining Gaussian Mixture Model (GMM) and Nearest Centroid Classifier (NCC), were applied for pixel-wise segmentation and classification. The best results were achieved in the tillering stage, with R2 values from 0.72 to 0.97 for Timothy (α = 0.05). For ryegrass, the RGB-based pixel-wise model performed best, particularly during leaf development, with R2 reaching 0.97. However, all models struggled during the beginning of flowering, particularly with dead grass and bare soil coverage.
Keywords: machine learning; RGB imagery; forage crops; image segmentation; Leaf Area Index; grass coverage machine learning; RGB imagery; forage crops; image segmentation; Leaf Area Index; grass coverage

Share and Cite

MDPI and ACS Style

Moreno, H.; Rueda-Ayala, C.; Rueda-Ayala, V.; Ribeiro, A.; Ranz, C.; Andújar, D. Machine Learning-Powered Segmentation of Forage Crops in RGB Imagery Through Artificial Sward Images. Agronomy 2025, 15, 356. https://doi.org/10.3390/agronomy15020356

AMA Style

Moreno H, Rueda-Ayala C, Rueda-Ayala V, Ribeiro A, Ranz C, Andújar D. Machine Learning-Powered Segmentation of Forage Crops in RGB Imagery Through Artificial Sward Images. Agronomy. 2025; 15(2):356. https://doi.org/10.3390/agronomy15020356

Chicago/Turabian Style

Moreno, Hugo, Christian Rueda-Ayala, Victor Rueda-Ayala, Angela Ribeiro, Carlos Ranz, and Dionisio Andújar. 2025. "Machine Learning-Powered Segmentation of Forage Crops in RGB Imagery Through Artificial Sward Images" Agronomy 15, no. 2: 356. https://doi.org/10.3390/agronomy15020356

APA Style

Moreno, H., Rueda-Ayala, C., Rueda-Ayala, V., Ribeiro, A., Ranz, C., & Andújar, D. (2025). Machine Learning-Powered Segmentation of Forage Crops in RGB Imagery Through Artificial Sward Images. Agronomy, 15(2), 356. https://doi.org/10.3390/agronomy15020356

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