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Agronomy
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Artificial Intelligence as a Support for Forecasting in Sustainable Agriculture
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Artificial Intelligence as a Support for Forecasting in Sustainable Agriculture

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

Deadline for manuscript submissions: 30 September 2025 | Viewed by 205

Special Issue Editors

Prof. Dr. Gniewko Niedbała

E-Mail Website
Guest Editor
Department of Biosystems Engineering, Faculty of Environmental and Mechanical Engineering, Poznań University of Life Sciences, Wojska Polskiego 50, 60-627 Poznań, Poland
Interests: artificial neural networks; artificial intelligence; machine learning; yield modelling; predictions; forecasting; crop production; agritourism development
Special Issues, Collections and Topics in MDPI journals
Dr. Magdalena Piekutowska

E-Mail Website
Guest Editor
Department of Geoecology and Geoinformation, Institute of Biology and Earth Sciences, Pomeranian University in Słupsk, 27 Partyzantów St., 76-200 Słupsk, Poland
Interests: artificial neural networks; artificial intelligence; machine learning; yield modelling; predictions; potato production; plant breeding; soil science; plant growth analysis
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We would like to invite you to contribute to the Special Issue entitled “Artificial Intelligence as a Support for Forecasting in Sustainable Agriculture”. With a growing population and the increasing challenges of climate change, the agricultural infrastructure needs modern solutions to adapt to the new reality. Artificial intelligence and machine learning are promising tools that can be used to improve agricultural forecasting, supporting agricultural producers to make better decisions about crops, resource management, and adaptation to changing environmental conditions.

AI-enabled forecasting can cover various aspects, including crop quantity and quality. By analyzing historical data, current weather conditions, and applied agronomic practices, it is possible to estimate future crop yields. In addition, these technologies enable the forecasting of crop quality traits, such as nutrient content or disease susceptibility, which is key to ensuring healthy food.

Pest and disease management is also an important part of forecasting. The early detection of threats facilitates a more effective implementation of crop protection strategies. Analyzing data on soil conditions, such as pH, moisture, and nutrients, makes it possible to forecast optimal locations for planting and harvesting, while forecasting crop needs for water, fertilizer, and pesticides contributes to the sustainable management of natural resources.

We encourage researchers, practitioners and industry professionals to share their findings and experiences. Together, we can build a platform of leading innovations that will aid the development of agricultural practices that not only increase productivity but also promote sustainability. Do not miss this opportunity to contribute to shaping the future of agriculture. We look forward to receiving your applications.

Prof. Dr. Gniewko Niedbała
Dr. Magdalena Piekutowska
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 100 words) can be sent to the Editorial Office for announcement on this website.

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 monthly 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
  • yield forecasting
  • artificial intelligence
  • sustainable agriculture
  • resource management
  • crop quality

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Published Papers (2 papers)

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Research

29 pages, 8507 KiB  
Article
ASE-YOLOv8n: A Method for Cherry Tomato Ripening Detection
by Xuemei Liang, Haojie Jia, Hao Wang, Lijuan Zhang, Dongming Li, Zhanchen Wei, Haohai You, Xiaoru Wan, Ruixin Li, Wei Li and Minglai Yang
Agronomy 2025, 15(5), 1088; https://doi.org/10.3390/agronomy15051088 - 29 Apr 2025
Abstract
To enhance the efficiency of automatic cherry tomato harvesting in precision agriculture, an improved YOLOv8n algorithm was proposed for fast and accurate recognition in natural environments. The improvements are as follows: first, the ADown down-sampling module replaces part of the original network backbone’s [...] Read more.
To enhance the efficiency of automatic cherry tomato harvesting in precision agriculture, an improved YOLOv8n algorithm was proposed for fast and accurate recognition in natural environments. The improvements are as follows: first, the ADown down-sampling module replaces part of the original network backbone’s standard convolution, enabling the model to capture higher-level image features for more accurate target detection, while also reducing model complexity by cutting the number of parameters. Secondly, the model’s neck adopts a Slim-Neck (GSConv+VoV-GSCSP) instead of traditional convolution with C2f. It replaces this combination with the more efficient CSConv and swaps the C2f module for VoV-GSCSP. Finally, the model also introduces the EMA attention mechanism, implemented at the P5 layer, which enhances the feature representation capability, enabling the network to extract detailed target features more accurately. This study trained the object-detection algorithm on a self-built cherry tomato dataset before and after improvement and compared it with early deep learning models and YOLO series algorithms. The experimental results show that the improved model increases accuracy by 3.18%, recall by 1.43%, the F1 score by 2.30%, mAP50 by 1.57%, and mAP50-95 by 1.37%. Additionally, the number of parameters is reduced to 2.52 M, and the model size is reduced to 5.08 MB, which outperforms other related models compared to the previous version. The experiment demonstrates the technology’s broad potential for embedded systems and mobile devices. The improved model offers efficient, accurate support for automated cherry tomato harvesting. Full article
(This article belongs to the Special Issue Artificial Intelligence as a Support for Forecasting in Sustainable Agriculture)
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17 pages, 5308 KiB  
Article
A Lightweight Algorithm for Detection and Grading ofOlive Ripeness Based on Improved YOLOv11n
by Fengwu Zhu, Suyu Wang, Min Liu, Weijie Wang and Weizhi Feng
Agronomy 2025, 15(5), 1030; https://doi.org/10.3390/agronomy15051030 - 25 Apr 2025
Viewed by 51
Abstract
Olives are a crucial woody oil crop, the harvesting of which predominantly relies on manual labor, which is characterized by high costs, low efficiency, and challenges in ensuring optimal harvesting timing. The development of an automated ripeness-detection system with high recognition accuracy is [...] Read more.
Olives are a crucial woody oil crop, the harvesting of which predominantly relies on manual labor, which is characterized by high costs, low efficiency, and challenges in ensuring optimal harvesting timing. The development of an automated ripeness-detection system with high recognition accuracy is of paramount importance for advancing automated olive-harvesting technologies. Traditional detection methods are constrained by susceptibility to environmental interference, limited robustness, and inadequate generalization capabilities. Concurrently, existing deep learning-based detection models face issues such as insufficient feature extraction for small targets and difficulties in deployment due to their need for large numbers of parameters. To address these limitations, this study proposes a lightweight algorithm for detection and grading of olive ripeness based on an Improved YOLOv11n framework. The proposed approach employs YOLOv11n as the baseline model, replaces its backbone network with EfficientNet-B0, and integrates the Large-Scale Kernel Attention (LSKA) mechanism and the Bidirectional Feature Pyramid Network (BiFPN). Experimental validation demonstrated that the enhanced model achieved detection accuracy comparable to that of the original model, attaining a mean average precision (mAP) of 0.918. Furthermore, the model size was reduced to 3.7 MB, a 39.3% reduction, while the computational complexity (GFLOPs) was decreased by 2.4 and the inference time per image was reduced by 0.2 ms. The proposed model exhibits significant advantages in terms of lightweight design and improved detection efficiency, demonstrating substantial potential for practical deployment. This study provides a valuable reference for the development of automated olive-harvesting technologies. Full article
(This article belongs to the Special Issue Artificial Intelligence as a Support for Forecasting in Sustainable Agriculture)
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