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Agronomy
Special Issues
Artificial Neural Network-Based Methods in Agriculture
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Artificial Neural Network-Based Methods in Agriculture

A Special Issue of Agronomy (ISSN 2073-4395) belonging to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 678

Editors

Dr. Shengyong Xu

E-Mail Website
Guest Editor
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Interests: plant phenomics technology; agricultural robots; AI visual inspection technology
Special Issues, Collections and Topics in MDPI journals
Dr. Chenglong Huang

E-Mail Website
Guest Editor
College of Engineering, Huazhong Agricultural University, Wuhan 430070, China
Interests: plant phenomics technology
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Artificial Neural Networks (ANNs) have revolutionized agricultural research by providing powerful, data-driven solutions to complex, nonlinear problems across the farming value chain. Their adoption signifies a fundamental shift from traditional experience-based practices toward intelligent, precision agriculture. This Special Issue seeks to capture and disseminate cutting-edge advances in ANN theory, development, and real-world implementation within agricultural systems. Its scope encompasses, but is not limited to, plant phenotyping, agricultural target detection, visual navigation, precision crop management, automated disease diagnosis, yield forecasting, resource optimization, and emerging applications of agricultural large language models (LLMs). Current research is rapidly progressing from single-task models toward integrated, scalable systems. Key frontiers include advanced environmental perception and autonomous decision-making, the development of lightweight and domain-specialized large models for edge deployment, and high-precision automated management enabled by multimodal data fusion.We invite high-quality original research and comprehensive review articles that contribute novel ANN architectures, explore cross-technology integration, propose systemic solutions, address sustainability and efficiency, and tackle practical deployment challenges. Submissions should demonstrate both methodological innovation and clear relevance to agricultural applications. 

Dr. Shengyong Xu
Dr. Chenglong Huang
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-anonymized 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

  • artificial neural networks
  • precision agriculture
  • data-driven agriculture
  • intelligent decision-making
  • multimodal data fusion

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Published Papers (2 papers)

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Research

29 pages, 18689 KB  
Article
OccPepSeg-YOLO for Instance Segmentation of Occluded Peppers in Field Images
by Xinran Yu, Mingxi Jiang, Fei Gao, Yize Fan, Yanyan Bai, Zhigang Peng, Qi Lu, Qian Liu and Shengyong Xu
Agronomy 2026, 16(17), 1646; https://doi.org/10.3390/agronomy16171646 - 27 Aug 2026
Abstract
Agricultural operations such as pepper harvesting, fruit counting, and field phenotyping rely on accurate visual recognition and instance segmentation algorithms. However, pepper fruits in complex field environments often exhibit slender and curved shapes, partial occlusion, ambiguous boundaries, and adhesion between adjacent instances. Existing [...] Read more.
Agricultural operations such as pepper harvesting, fruit counting, and field phenotyping rely on accurate visual recognition and instance segmentation algorithms. However, pepper fruits in complex field environments often exhibit slender and curved shapes, partial occlusion, ambiguous boundaries, and adhesion between adjacent instances. Existing object detection and instance segmentation methods therefore struggle to obtain complete fruit masks, which adversely affects subsequent fruit counting, contour measurement, and picking-point localization. To improve the instance segmentation accuracy of occluded peppers in complex field scenes, this study proposes OccPepSeg-YOLO, an improved model based on YOLO11n-seg. First, a P2FreqFusion module is introduced to fuse shallow, high-resolution detail features with deep semantic features, thereby enhancing the representation of fruit edges and tip regions. Second, an ASC module is designed to model the directional and scale-related morphological characteristics of pepper fruits, while a BoundaryGate module strengthens responses at occlusion interfaces and boundaries between adjacent instances. Finally, an OccPepSegment multi-scale prototype segmentation head is constructed, and a BDoU loss function is introduced to improve the boundary consistency of instance masks. Experiments on a self-constructed field-pepper instance segmentation dataset showed that OccPepSeg-YOLO achieved M-P, M-R, M-mAP50, and M-mAP50–95 values of 93.87%, 92.09%, 97.17%, and 82.31%, respectively, representing improvements of 5.59, 3.18, 3.83, and 9.52 percentage points over YOLO11n-seg. Further comparisons with representative YOLO-based instance segmentation models, including YOLOv8n-seg, YOLOv9c-seg, YOLO12n-seg, and YOLOv26n-seg, demonstrated that OccPepSeg-YOLO achieved the best overall segmentation performance. In particular, its M-mAP50–95 exceeded the best competing result obtained by YOLOv9c-seg by 8.35 percentage points. Under a unified repeated-inference protocol on an RTX 3090 GPU using FP32 precision, a batch size of 1, and 640 × 640 inputs, OccPepSeg-YOLO achieved a mean inference latency of 15.801 ± 1.238 ms, a P95 latency of 17.323 ms, and a throughput of 63.29 FPS. These results demonstrate that the proposed model can produce more complete pepper instance masks under leaf occlusion, fruit overlap, and complex background conditions, providing technical support for field-pepper recognition, fruit counting, and visual perception by agricultural robots. Full article
(This article belongs to the Special Issue Artificial Neural Network-Based Methods in Agriculture)
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24 pages, 57641 KB  
Article
Low-Cost and Rapid Construction of 3D Point Clouds for Field-Grown Cotton and Evaluation of Canopy-Level Traits
by Hao Qiu, Xiaoyan Meng, Yunjie Zhao, Yuxiang Wang, Haoyuan Niu, Liang Yu and Shuai Yin
Agronomy 2026, 16(17), 1619; https://doi.org/10.3390/agronomy16171619 - 22 Aug 2026
Viewed by 227
Abstract
Canopy 3D architecture is a critical determinant of light interception, photosynthetic efficiency, and final yield in cotton, yet its rapid and accurate characterisation remains challenging in field conditions. To achieve efficient, non-destructive, and quantitative monitoring of the canopy structure of field-grown cotton, this [...] Read more.
Canopy 3D architecture is a critical determinant of light interception, photosynthetic efficiency, and final yield in cotton, yet its rapid and accurate characterisation remains challenging in field conditions. To achieve efficient, non-destructive, and quantitative monitoring of the canopy structure of field-grown cotton, this study proposes a 3D structure-based technology stack for high-efficiency, low-cost, and high-precision phenotyping extraction. This stack directly addresses the technical bottlenecks of traditional 3D data acquisition, namely high cost, long processing time, and low operational efficiency, which have hindered large-scale application. We developed a pipeline that integrates a fast reconstruction algorithm with a scale-recovery mechanism using ground control points (GCPs), enabling the generation of true-scale 3D point clouds from UAV aerial images in a cost- and time-effective manner. Using only 141 UAV images and with a reconstruction time of approximately 20 min, we efficiently reconstructed high-quality, scale-accurate point clouds of two 5.5 m × 5.5 m cotton plots, significantly outperforming SfM-MVS and Instant-NGP in terms of both reconstruction efficiency and point cloud completeness. This method, whose current validation is confined to a single season, one growth stage, and two experimental plots, not only achieves a breakthrough by using fewer input images with high efficiency, but also ensures point cloud accuracy and completeness, showing strong potential for rapid field monitoring and real-time management. Based on the high-quality reconstructed point clouds, we further quantitatively evaluated canopy characteristics at harvest, analyzing the coefficient of variation of canopy height, porosity distribution, and canopy volume fraction. The core shortcomings and optimization strategies for the existing canopy structure were identified, providing scientific data support and practical technical references for precision cultivation management and mechanization-compatible planting in cotton. Full article
(This article belongs to the Special Issue Artificial Neural Network-Based Methods in Agriculture)
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