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Agronomy
Special Issues
Advanced Machine Learning in Agriculture—2nd Edition
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Advanced Machine Learning in Agriculture—2nd Edition

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 31 August 2026 | Viewed by 5254

Special Issue Editors

Dr. Paul Kwan

E-Mail Website
Guest Editor
School of Engineering and Technology, CQUniversity Brisbane, 160 Ann St., Brisbane City, QLD 4000, Australia
Interests: artificial intelligence; pattern recognition; computer vision; machine learning; computational science; data science; digital agriculture; agroinformatics
Special Issues, Collections and Topics in MDPI journals
Dr. Jing Zhou

E-Mail Website
Guest Editor
Department of Crop and Soil Science, Oregon State University, Corvallis, OR 97331, USA
Interests: precision agriculture; high-throughput phenotyping; unmanned aerial vehicle; remote sensing; machine learning; image processing
Special Issues, Collections and Topics in MDPI journals
Dr. Beibei Xu

E-Mail Website
Guest Editor
Population Medicine and Diagnostic Sciences, Cornell University, Ithaca, NY 14850, USA
Interests: machine learning; deep learning; precision farming; digital agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Following the success of our first Special Issue of Agronomy, titled “Advanced Machine Learning in Agriculture”, the Editorial Office is pleased to launch a second edition.

Responding to an era marked by the relentless pursuit of innovation and sustainability in farming practices, this collection of articles delves into the transformative potential of artificial intelligence, specifically machine learning and deep learning techniques, in revolutionizing the agricultural landscape.

In today's world, where the global population continues to rise and climate change presents increasingly complex challenges, agriculture stands at a crossroads. It must meet the growing demand for food while mitigating its environmental footprint. The emergence of smart farming and smart agriculture, driven by machine learning, holds immense promise in achieving this delicate balance.

Machine learning, with its ability to process vast datasets and uncover hidden patterns, enables us to make sense of the intricate web of factors that affect agricultural production. Whether predicting crop yields with unprecedented accuracy, identifying and managing pest infestations, optimizing resource allocation, or enhancing the breeding of resilient crops, machine learning empowers us to make informed decisions that drive efficiency, sustainability, and profitability in agriculture.

We are particularly excited about the diverse array of topics covered in this Special Issue. We welcome contributions that encompass smart farming, precision agriculture, and data-driven solutions across the agricultural spectrum. Our contributors include esteemed researchers and practitioners from around the globe, each offering valuable insights into the dynamic field of advanced machine learning in agriculture.

Dr. Paul Kwan
Dr. Jing Zhou
Dr. Beibei Xu
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Agronomy is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • machine learning
  • smart farming
  • smart agriculture
  • precision farming
  • digital technologies
  • artificial intelligence
  • remote sensing

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (4 papers)

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Research

18 pages, 1267 KB  
Article
GrapePPI: A Deep Learning Framework for Grape Protein–Protein Interaction Prediction Using ESM Embeddings
by Chenghui Li, Mengyao Li and Aisheng Xiong
Agronomy 2026, 16(6), 626; https://doi.org/10.3390/agronomy16060626 - 15 Mar 2026
Viewed by 457
Abstract
Protein–protein interactions (PPIs) are fundamental to biological processes, yet experimental identification of PPIs remains time-consuming and costly, particularly for crop species with limited data. Grape (Vitis vinifera) is a globally important fruit crop that would benefit from improved computational tools for [...] Read more.
Protein–protein interactions (PPIs) are fundamental to biological processes, yet experimental identification of PPIs remains time-consuming and costly, particularly for crop species with limited data. Grape (Vitis vinifera) is a globally important fruit crop that would benefit from improved computational tools for PPI prediction to support functional genomics and molecular breeding. Here, we present GrapePPI, a deep learning framework specifically designed for grape PPI prediction that leverages pre-trained ESM (Evolutionary Scale Modeling) protein embeddings. GrapePPI employs a four-component architecture: ESM embedding extraction, sequence encoding, feature combination, and multi-layer interaction prediction. We evaluated GrapePPI on grape-specific datasets with balanced and imbalanced class distributions, as well as benchmark datasets from yeast and Arabidopsis. On grape data, GrapePPI significantly outperformed state-of-the-art methods including DeepFE-PPI, PIPR, and ESMAraPPI, achieving F1 scores of 89.34% and 85.43% on balanced and imbalanced datasets, respectively, with PR AUC values of 95.29% and 90.87%. GrapePPI also demonstrated strong cross-species generalization, outperforming competing methods on yeast datasets and achieving performance comparable to specialized plant models on Arabidopsis data. Our results establish GrapePPI as an effective and robust tool for grape PPI prediction, with practical applications in functional genomics research and crop improvement programs. Full article
(This article belongs to the Special Issue Advanced Machine Learning in Agriculture—2nd Edition)
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31 pages, 4400 KB  
Article
Regional-Scale Mapping of Gully Network in Mediterranean Olive Landscapes Using Machine Learning Algorithms: The Guadalquivir Basin
by Paula González-Garrido, Adolfo Peña-Acevedo, Francisco-Javier Mesas-Carrascosa and Juan Julca-Torres
Agronomy 2026, 16(6), 622; https://doi.org/10.3390/agronomy16060622 - 14 Mar 2026
Viewed by 488
Abstract
Gully erosion is a significant threat to the sustainability of soil in Mediterranean basins. Despite its impact, there is a lack of research providing accurate regional-scale cartography of complete gully networks. This study aims to automatically map the gully network in the olive-growing [...] Read more.
Gully erosion is a significant threat to the sustainability of soil in Mediterranean basins. Despite its impact, there is a lack of research providing accurate regional-scale cartography of complete gully networks. This study aims to automatically map the gully network in the olive-growing landscapes of the Guadalquivir basin (Spain) using Machine Learning (ML) algorithms: Random Forest (RF), Support Vector Machine (SVM), Decision Tree (DT), and Logistic Regression (LR). We integrated these models with 17 predictive variables (including hydrotopographic, climatic, and edaphic factors) and the Gully Head Initiation (GHI) index. RF was the most suitable model, achieving an Area Under the Curve (AUC) of 0.91 and an F1-score of 0.83, and enabled the delineation of a gully network totalling 8439.05 km. Variable importance analysis revealed that flow accumulation (17.33%) and the GHI index (nearly 30%) were the primary predictors, with the Rainy Day Normal (RDN)-based formulation outperforming the maximum daily precipitation (Pmax)-based one. Spatially, countryside hill landscapes exhibited the highest gully densities (42.50 m/ha). The results demonstrate the effectiveness of combining ML with physically based indices to generate high-resolution gully cartography for soil conservation planning in Mediterranean olive groves. Full article
(This article belongs to the Special Issue Advanced Machine Learning in Agriculture—2nd Edition)
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32 pages, 7698 KB  
Article
Delineating Soybean Mega-Environments Across State Lines: A Statistical Learning Approach to Multi-State Official Variety Trial Analysis
by Isaac Mirahki, Richard Bond, Ryan Heiniger, David Moseley and Virginia R. Sykes
Agronomy 2026, 16(3), 376; https://doi.org/10.3390/agronomy16030376 - 4 Feb 2026
Viewed by 406
Abstract
The current state-centric analysis of Official Variety Trials (OVTs) restricts the identification of stable performance zones across political boundaries. This study employed multivariate statistical learning techniques to delineate soybean (Glycine max L.) “mega-environments” using yield data from 2269 varieties collected across seven [...] Read more.
The current state-centric analysis of Official Variety Trials (OVTs) restricts the identification of stable performance zones across political boundaries. This study employed multivariate statistical learning techniques to delineate soybean (Glycine max L.) “mega-environments” using yield data from 2269 varieties collected across seven U.S. states (2019–2022). Utilizing Quadratic Discriminant Analysis (QDA), Principal Component Analysis (PCA), and Agglomerative Hierarchical Clustering (AHC), we examined the edaphoclimatic factors influencing yield stability. QDA classified over 79% of environments into distinct temporal categories, highlighting significant inter-annual climatic variability driven by Growing Degree Days (GDD) and latitude. PCA distinguished broad climatic drivers (PC1) from localized soil texture constraints (PC2). AHC identified optimal production clusters that frequently diverged from geographic proximity, indicating that distant sites often share more critical yield-determining factors than neighboring counties. By operationalizing these latent environmental patterns, this study provides a data-driven framework for cross-state environmental zoning that can support more precise variety placement once genotype performance has been evaluated within these zones. Full article
(This article belongs to the Special Issue Advanced Machine Learning in Agriculture—2nd Edition)
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20 pages, 7085 KB  
Article
A Lightweight Citrus Ripeness Detection Algorithm Based on Visual Saliency Priors and Improved RT-DETR
by Yutong Huang, Xianyao Wang, Xinyao Liu, Liping Cai, Xuefei Feng and Xiaoyan Chen
Agronomy 2025, 15(5), 1173; https://doi.org/10.3390/agronomy15051173 - 12 May 2025
Cited by 8 | Viewed by 2839
Abstract
As one of the world’s economically valuable fruit crops, citrus has its quality and productivity closely tied to the degree of fruit ripeness. However, accurately and efficiently detecting citrus ripeness in complex orchard environments for selective robotic harvesting remains a challenge. To address [...] Read more.
As one of the world’s economically valuable fruit crops, citrus has its quality and productivity closely tied to the degree of fruit ripeness. However, accurately and efficiently detecting citrus ripeness in complex orchard environments for selective robotic harvesting remains a challenge. To address this, we constructed a citrus ripeness detection dataset under complex orchard conditions, proposed a lightweight algorithm based on visual saliency priors and the RT-DETR model, and named it LightSal-RTDETR. To reduce computational overhead, we designed the E-CSPPC module, which efficiently combines cross-stage partial networks with gated and partial convolutions, combined with cascaded group attention (CGA) and inverted residual mobile block (iRMB), which minimizes model complexity and computational demand and simultaneously strengthens the model’s capacity for feature representation. Additionally, the Inner-SIoU loss function was employed for bounding box regression, while a weight initialization method based on visual saliency maps was proposed. Experiments on our dataset show that LightSal-RTDETR achieves a mAP@50 of 81%, improving by 1.9% over the original model while reducing parameters by 28.1% and computational cost by 26.5%. Therefore, LightSal-RTDETR effectively solves the citrus ripeness detection problem in orchard scenes with high complexity, offering an efficient solution for smart agriculture applications. Full article
(This article belongs to the Special Issue Advanced Machine Learning in Agriculture—2nd Edition)
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