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Agronomy
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Application of Deep and Machine Learning in Crop Monitoring and...
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Application of Deep and Machine Learning in Crop Monitoring and Management—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 July 2026 | Viewed by 936

Special Issue Editors

Dr. Vjekoslav Tadić

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Guest Editor
Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
Interests: agricultural engineering; precise agriculture; farming and cropping systems; machines and devices in plant production; pesticide application equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Dorijan Radočaj

E-Mail Website
Guest Editor
Department of Agricultural Engineering and Renewable Energy Sources, Faculty of Agrobiotehnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
Interests: GIS (geographic information systems); remote sensing; machine learning; predictive modeling and mapping; cropland suitability assessment; digital soil mapping
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Mladen Jurišić

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Guest Editor
Faculty of Agrobiotechnical Sciences Osijek, Josip Juraj Strossmayer University of Osijek, Vladimira Preloga 1, 31000 Osijek, Croatia
Interests: GIS; precision agriculture; multicriteria analysis; farming and cropping systems; inventory of natural resources
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The application of deep and machine learning in crop monitoring and management has become increasingly important in light of the growing demand for sustainable agricultural practices. While traditional methods provide valuable insights into crop management, the integration of deep and machine learning techniques into existing approaches offers a unique opportunity to improve the efficiency and sustainability of agriculture. By incorporating deep and machine learning analytics, various crop-related parameters such as growth patterns, soil composition and fertilization, crop productivity, climate conditions, pest infestations, and many other issues in modern agriculture can be assessed with greater predictive accuracy. This enables the comprehensive monitoring and management of crops in different agricultural landscapes, from small farms to large plantations. Deep learning algorithms recognize complex patterns in large data sets when monitoring crops, thus facilitating informed decision-making processes. By analyzing satellite imagery, sensor data, and historical records, or in situ field research data, deep and machine learning models can predict crop yields, identify areas prone to disease outbreaks, and optimize resource allocation for higher productivity.

This Special Issue aims to expand current knowledge on crop monitoring and management assessment using deep and machine learning methods in various agricultural fields. Contributions should cover a broad range of topics that serve as cornerstones for optimizing crop management, with deep and machine learning serving as the primary analytical approaches. Examples of potential topics include precision agriculture, remote sensing applications, environmental impact assessment, climate change in agriculture, biotic and abiotic factors of agricultural production, and other interdisciplinary areas important to crop monitoring and management. We strongly encourage the submission of original research articles and reviews to showcase the versatility of deep and machine learning in crop monitoring and management and to provide professionals worldwide with insights into refining techniques and evaluating criteria in their respective fields.

It is our great pleasure to invite you to the Special Issue, entitled "Application of Deep and Machine Learning in Crop Monitoring and Management", which aims to bring together the application of state-of-the-art, efficient, and flexible deep and machine learning methods to determine optimal strategies for crop monitoring and management.

We look forward to receiving your contributions!

Dr. Vjekoslav Tadić
Dr. Dorijan Radočaj
Prof. Dr. Mladen Jurišić
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

  • deep and machine learning
  • crop productivity
  • prediction of crop related parameters
  • prediction of biotic and abiotic factors in agricultural production
  • soil composition and fertilization
  • climate change impact of agriculture
  • pest management
  • precision agriculture
  • remote sensing applications
  • convolutional neural networks (CNNs)
  • unmanned aerial vehicles (UAVs)
  • phenotyping
  • data fusion
  • decision support systems

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Published Papers (2 papers)

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Research

26 pages, 9992 KB  
Article
Suitability Maps of Bactrocera Oleae Presence by SDM Based on Pedo-Climatic and Topographic Predictors Data in Sicily
by Giuseppe Antonio Catalano, Giovanni Pirrello, Provvidenza Rita D’Urso and Claudia Arcidiacono
Agronomy 2026, 16(5), 501; https://doi.org/10.3390/agronomy16050501 - 24 Feb 2026
Abstract
Climate change and increasingly restrictive pesticide regulations have created a growing need for new tools to support the integrated pest management (IPM) of the olive fruit fly, Bactrocera oleae, in cultivated areas of the Mediterranean. In this study, the environmental suitability for [...] Read more.
Climate change and increasingly restrictive pesticide regulations have created a growing need for new tools to support the integrated pest management (IPM) of the olive fruit fly, Bactrocera oleae, in cultivated areas of the Mediterranean. In this study, the environmental suitability for this phytophagous insect in eastern Sicily was mapped by using geographic information system (GIS) tools and species distribution models (i.e., Random Forest and MaxEnt). The models were trained on presence data of the fly, obtained from a network of pheromone traps and locations where olive trees were present, combined with climatic, topographic and soil predictors for both current conditions and the future climate scenario (2021–2040). Correlation analysis was utilised to select ten predictors from an initial set of 33 soil and climate variables. Model performance was evaluated by using 10-fold cross-validation based on accuracy measures Area Under the Curve (AUC), True Skill Statistic (TSS), and the difference between the training and testing AUC) to minimise overfitting. Both algorithms demonstrated excellent predictive performance, producing convergent suitability maps, with high values concentrated in the foothills and hills of the Iblean–Calatino area and low values along the coastal plains and at higher altitudes, where extreme temperatures and unfavourable soil textures reduce habitat suitability. Response curves highlighted the combined influence of moderate temperature and precipitation seasonality, balanced topsoil texture, and moderate slopes in defining the species’ ecological niche. The proposed framework provides an operational basis for optimising monitoring networks and targeting IPM measures under current and near-future climate conditions. Full article
(This article belongs to the Special Issue Application of Deep and Machine Learning in Crop Monitoring and Management—2nd Edition)
29 pages, 14822 KB  
Article
Estimation of Cotton Aboveground Biomass Based on UAV Multispectral Images: Multi-Feature Fusion and CNN Model
by Shuhan Huang, Xinjun Wang, Hanyu Cui, Qingfu Liang, Songrui Ning, Haoran Yang, Panfeng Wang and Jiandong Sheng
Agronomy 2026, 16(1), 74; https://doi.org/10.3390/agronomy16010074 - 26 Dec 2025
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Abstract
Precise estimation of cotton aboveground biomass (AGB) plays a crucial role in effectively analyzing growth variations and development of cotton, as well as guiding agricultural management practices. Multispectral (MS) sensors mounted on UAVs offer a practical and accurate approach for estimating the AGB [...] Read more.
Precise estimation of cotton aboveground biomass (AGB) plays a crucial role in effectively analyzing growth variations and development of cotton, as well as guiding agricultural management practices. Multispectral (MS) sensors mounted on UAVs offer a practical and accurate approach for estimating the AGB of cotton. Many previous studies have mainly emphasized the combination of spectral and texture features, as well as canopy height (CH). However, current research overlooks the potential of integrating spectral, textural features, and CH to estimate AGB. In addition, the accumulation of AGB often exhibits synergistic effects rather than a simple additive relationship. Conventional algorithms, including Bayesian Ridge Regression (BRR) and Random Forest Regression (RFR), often fail to accurately capture the nonlinear and intricate correlations between biomass and its relevant variables. Therefore, this research develops a method to estimate cotton AGB by integrating multiple feature information with a deep learning model. Spectral and texture features were derived from MS images. Cotton CH extracted from UAV point cloud data. Variables of multiple features were selected using Spearman’s Correlation (SC) coefficients and the variance inflation factor (VIF). Convolutional neural network (CNN) was chosen to build a model for estimating cotton AGB and contrasted with traditional machine learning models (RFR and BRR). The results indicated that (1) combining spectral, textural features, and CH yielded the highest precision in cotton AGB estimation; (2) compared to traditional ML models (RFR and BRR), the accuracy of applying CNN for estimating cotton AGB is better. CNN has more advanced power to learn complex nonlinear relationships among cotton AGB and multiple features; (3) the most effective strategy in this study involves combining spectral, texture features, and CH, selecting variables using the SC and VIF methods, and employing CNN for estimating AGB of cotton. The R2 of this model is 0.80, with an RMSE of 0.17 kg·m−2 and an MAE of 0.11 kg·m−2. This study develops a framework for evaluating cotton AGB by multiple features fusion with a deep learning model. It provides technical support for monitoring crop growth and improving field management. Full article
(This article belongs to the Special Issue Application of Deep and Machine Learning in Crop Monitoring and Management—2nd Edition)
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