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Agriculture
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Application of Smart Technologies in Orchard Management
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Application of Smart Technologies in Orchard Management

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".

Deadline for manuscript submissions: closed (28 February 2026) | Viewed by 12658

Special Issue Editors

Dr. Alessandra Vinci

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Guest Editor
Department of Agricultural, Food and Environmental Sciences, University of Perugia, Perugia, Italy
Interests: UAV; precision agriculture; remote sensing; irrigation management; modelling of crop water demand
Special Issues, Collections and Topics in MDPI journals
Dr. Daniela Farinelli

E-Mail Website
Guest Editor
Department of Agricultural, Food, and Environmental Sciences (DSA3), University of Perugia, Via Borgo XX Giugno 74, 06121 Perugia, Italy
Interests: mechanical harvest; breeding and clonal selection of new varieties; abiotic stress; fruit growth; ripening indexes
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

From several years the role of the technology applied to the agriculture led to promising results. In fact, the smart technologies help the optimization of the use of the input such as water and fertilizers for the increasing sustainability of the orchard. In a contest of climate change, it is important to develop and test smart technology solutions for increasing of the production. In addition, IoT-enabled sensor networks and aerial drones generate high-resolution spatial-temporal data, empowering growers with actionable insights for proactive management. 

This Special Issue focuses on the role that smart technology in the production of high-quality food and continuously over time. For this reason, it welcomes highly quality studies includes, but is not limited to the follow scope:

  • Computer vision for fruit detection and yield estimation;
  • AI-driven microclimate and soil health monitoring systems;
  • IoT-based pest and disease monitoring models;
  • AI-optimized irrigation and nutrient delivery systems;
  • scalable digital for orchard management.

Dr. Alessandra Vinci
Dr. Daniela Farinelli
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. Agriculture 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

  • irrigation
  • UAV
  • irrigation systems
  • yield
  • IoT
  • precision agriculture

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Published Papers (9 papers)

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Research

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34 pages, 13959 KB  
Article
Geo-Referenced Factor-Graph SLAM for Orchard-Scale 3D Apple Reconstruction and Yield Estimation
by Dheeraj Bharti, Lilian Nogueira de Faria, Luciano Vieira Koenigkan, Luciano Gebler, Andrea de Rossi and Thiago Teixeira Santos
Agriculture 2026, 16(7), 764; https://doi.org/10.3390/agriculture16070764 - 30 Mar 2026
Viewed by 735
Abstract
Accurate and spatially resolved yield estimation is a critical requirement for precision agriculture and orchard management. This paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental [...] Read more.
Accurate and spatially resolved yield estimation is a critical requirement for precision agriculture and orchard management. This paper presents a geometrically consistent, orchard-scale apple yield estimation framework that integrates GNSS–visual-inertial odometry (VIO) fusion, deep learning-based object detection, multi-frame tracking, three-dimensional triangulation, and incremental factor-graph optimization. Camera poses are obtained using ZED GNSS–VIO fusion and subsequently refined using an iSAM2-based nonlinear smoothing approach that incorporates strong relative-motion constraints and soft global ENU (East-North-Up) translation priors. Apples are detected using a YOLO-based model and associated across frames via CoTracker3, enabling robust multi-view landmark reconstruction. Reprojection factors and landmark priors are incorporated into a unified nonlinear factor graph to jointly optimize camera trajectories and 3D apple positions. The reconstructed apples are spatially aggregated into a grid-based mass map, where individual fruit volumes are estimated assuming spherical geometry and converted to mass using density models. The resulting ENU-referenced yield plot provides a structured representation of orchard production variability. Experimental results demonstrate significant reductions in reprojection error after optimization and improved global consistency of the trajectory, leading to stable and spatially coherent 3D reconstructions. The proposed pipeline bridges perception, geometry, and optimization, providing a scalable solution for orchard-scale yield mapping and decision support in precision agriculture. Full article
(This article belongs to the Special Issue Application of Smart Technologies in Orchard Management)
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23 pages, 9157 KB  
Article
Estimation of Crop Coefficients of a High-Density Hazelnut Orchard Using Traditional Methods vs. UAV-Derived Thermal and Spectral Indices
by Alessandra Vinci, Raffaella Brigante, Silvia Portarena, Laura Marconi, Simona Lucia Facchin, Daniela Farinelli and Chiara Traini
Agriculture 2026, 16(6), 677; https://doi.org/10.3390/agriculture16060677 - 17 Mar 2026
Viewed by 493
Abstract
Evapotranspiration and crop coefficients are key variables for designing efficient irrigation strategies in tree crops, yet standard tabulated coefficients derived for mature, fully covering orchards often fail to represent the water use of young, high-density hazelnut systems. In recent years, updated crop coefficients [...] Read more.
Evapotranspiration and crop coefficients are key variables for designing efficient irrigation strategies in tree crops, yet standard tabulated coefficients derived for mature, fully covering orchards often fail to represent the water use of young, high-density hazelnut systems. In recent years, updated crop coefficients for temperate fruit trees, including hazelnut, and transpiration-based models have been proposed, while several studies have successfully linked Vegetation Indices and thermal metrics to single and basal crop coefficients in vineyards, orchards and field crops. However, no information is available on the use of UAV-derived spectral and thermal indices to estimate crop coefficients in high-density hazelnut orchards. This study compares crop coefficients obtained from traditional approaches (the FAO56 single crop coefficient, a transpiration-based coefficient, and ground cover reduction factors) with coefficients estimated from UAV-derived Normalized Difference Water Index (NDWI) and Crop Water Stress Index (CWSI) in a subsurface-drip-irrigated hazelnut orchard (cv. Tonda Francescana®) with two planting densities (625 and 1250 trees ha−1) in central Italy. Multispectral and thermal UAV surveys carried out between 2021 and 2024 were used to derive canopy geometrical traits, ground cover, NDWI, and CWSI, while a local weather station provided reference evapotranspiration. Empirical relationships were calibrated between crop coefficients and ground cover, NDWI, and CWSI, and mid-season coefficients were applied to estimate daily crop evapotranspiration, which was then compared with the irrigation volumes supplied during the 2024 season. The standard FAO56 crop coefficient (Kc = 0.9) overestimated evapotranspiration, especially at the lower planting density, whereas ground cover-based reduction factors recalibrated for hazelnut and the transpiration-based coefficient provided estimates more consistent with the applied irrigation. UAV-based NDWI- and CWSI-derived crop coefficients produced mid-season values close to those obtained with the transpiration-based method for both planting densities, confirming that spectral and thermal information can effectively capture the combined effects of canopy development and water status. These results indicate that combining traditional methods with UAV-derived indices offers a flexible framework to refine crop coefficients in high-density hazelnut orchards and support more accurate and spatially explicit irrigation scheduling. Full article
(This article belongs to the Special Issue Application of Smart Technologies in Orchard Management)
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28 pages, 4247 KB  
Article
BiMS-Pose: Enhancing Human Pose Estimation in Orchard Spraying Scenarios via Bidirectional Multi-Scale Collaboration
by Yuhang Ren, Zichen Yang, Hanxin Chen, Zhuochao Chen and Daojin Yao
Agriculture 2026, 16(5), 606; https://doi.org/10.3390/agriculture16050606 - 6 Mar 2026
Viewed by 404
Abstract
Most 2D human pose estimation frameworks utilize static designs for multi-scale feature fusion, where information from various scales is integrated using fixed weights. A drawback of these approaches is that they often lead to localization biases in complex scenarios. This paper addresses the [...] Read more.
Most 2D human pose estimation frameworks utilize static designs for multi-scale feature fusion, where information from various scales is integrated using fixed weights. A drawback of these approaches is that they often lead to localization biases in complex scenarios. This paper addresses the issues of multi-scale feature mismatch and joint localization biases in pose estimation. From the perspective of feature processing, multi-scale weights must be adapted to the size and position of joints, while joint predictions should adhere to human anatomical constraints. Existing methods lack effective dynamic adaptation, structural constraints, and bidirectional complementarity between high-level semantics and low-level details. They often experience localization biases in occluded scenarios, and the peaks of their heatmaps demonstrate insufficient consistency with the actual positions of the joints. Through theoretical analysis, we identify the causes of performance gaps and propose directions for narrowing them. We propose Bidirectional Multi-Scale Collaborative Pose Estimation (BiMS-Pose), a framework that introduces dynamic weights to adjust feature proportions, establishes bidirectional topological constraints for joint relationships, and integrates a bidirectional attention flow. The framework filters key information from three dimensions, adjusts filtering strategies in real time, and is enhanced by heatmap optimization to improve localization accuracy. Extensive experiments conducted on COCO, MPII, and our self-built Orchard Spraying Pose Dataset (OSPD) demonstrate the effectiveness of BiMS-Pose. In general scenarios, it achieves a significant 1.2 percentage-point increase in average precision (AP) on the COCO val2017 dataset compared to ViTPose while utilizing the same backbone. In agricultural orchard spraying scenarios, it effectively addresses interference factors such as changes in illumination, occlusion, and varying shooting distances, achieving 75.4% average precision (AP) and 90.7% percent of correct keypoints (PCKh@0.5) on the OSPD dataset. Additionally, it maintains an average frame rate of 18.3 FPS on embedded devices, effectively meeting the requirements for real-time monitoring. This highlights the model’s potential for precise, stable, and practical human pose estimation in both general and agricultural application scenarios. Full article
(This article belongs to the Special Issue Application of Smart Technologies in Orchard Management)
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27 pages, 9877 KB  
Article
An A*-DWA Algorithm Enhanced Laser SLAM System for Orchard Navigation: Design and Performance Analysis
by Hongsen Wang, Xiuhua Zhang, Zheng Huang, Yongwei Yuan, Degang Kong and Shanshan Li
Agriculture 2026, 16(4), 469; https://doi.org/10.3390/agriculture16040469 - 18 Feb 2026
Cited by 1 | Viewed by 628
Abstract
To address the key limitations of existing laser SLAM (Simultaneous Localization and Mapping) navigation systems in orchards—insufficient safety margins, unsmooth trajectories, poor dynamic obstacle adaptability, and high energy consumption—this study proposes an A* (A-Star)-DWA (Dynamic Window Approach) collaborative optimization algorithm integrated into an [...] Read more.
To address the key limitations of existing laser SLAM (Simultaneous Localization and Mapping) navigation systems in orchards—insufficient safety margins, unsmooth trajectories, poor dynamic obstacle adaptability, and high energy consumption—this study proposes an A* (A-Star)-DWA (Dynamic Window Approach) collaborative optimization algorithm integrated into an orchard-specific laser SLAM framework. Three core enhancements were added to the global A* planner: (1) obstacle–vertex adjacency checks (maintaining ~1 m minimum safety distance, meeting 0.8–1.2 m orchard machinery requirements); (2) redundant node elimination (reducing unnecessary turns and energy use); (3) obstacle density metric integrated into the heuristic function (optimizing node expansion efficiency). For the local DWA planner, key parameters (azimuth weight, obstacle distance weight, prediction horizon, etc.) were calibrated to orchard scenarios and tracked robot kinematics, with a lightweight “deviate → avoid → rejoin global path” mechanism for real-time obstacle avoidance. A three-stage path smoothing process (Bresenham verification + cubic spline interpolation + curvature constraint optimization) further improved trajectory quality. The A*-DWA framework synergizes A*’s global optimality (overcoming DWA’s local minima) and DWA’s real-time obstacle avoidance (compensating for A*’s static limitation). Validations via Matlab/Gazebo/Rviz simulations and field tests in the “Xinli No. 7” pear orchard confirmed superior performance: 100% obstacle avoidance success rate (vs. 85.0–92.0% for comparative algorithms), 0.36–0.45 s response time (57.7–71.1% shorter), 1.05–1.15 m safety distance (far exceeding 0.60–0.82 m of existing methods); field tests show 10% lower energy consumption than traditional A*, 0.011 m mean lateral deviation (straight segments), and 65% reduced peak turning deviation (0.14 m). This work resolves multidimensional orchard navigation challenges, enhances agricultural robot efficiency, safety, and adaptability, and provides a practical basis for smart agriculture advancement. Full article
(This article belongs to the Special Issue Application of Smart Technologies in Orchard Management)
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17 pages, 8749 KB  
Article
Farmer-Friendly Approach for Table Grape Bunch Detection Using the Roboflow Platform
by Francesco Vicino, Giovanni Popeo, Francesco Santoro, Simone Pascuzzi and Francesco Paciolla
Agriculture 2026, 16(2), 218; https://doi.org/10.3390/agriculture16020218 - 14 Jan 2026
Viewed by 954
Abstract
Accurate fruit detection and counting are fundamental requirements in the development of reliable computer vision applications for yield estimation. This work was conceived to provide farmers with a farmer-friendly approach for automatic grape bunch detection. This study exploits the free demo version of [...] Read more.
Accurate fruit detection and counting are fundamental requirements in the development of reliable computer vision applications for yield estimation. This work was conceived to provide farmers with a farmer-friendly approach for automatic grape bunch detection. This study exploits the free demo version of the Roboflow 3.0 platform to train five state-of-the-art computer vision models with RGB images of white and red grape bunches, acquired with a smartphone in the field, and compares their performance. The results were evaluated both quantitatively, in terms of precision, recall, and AP@50 calculated on the validation set, and qualitatively on the test set. The models that achieved the best performances, also in the presence of overlapping clusters, were Roboflow 3.0 Object Detection and YOLOv11, reaching precisions of 86.6% and 88%, respectively, for the detection of white bunches, and of 85.7% and 89.9% for red bunches. This study highlights the possibility of developing highly accurate computer vision models for table grape bunch detection using the Roboflow platform, offering an accessible and user-friendly tool for non-expert users, including farmers. Full article
(This article belongs to the Special Issue Application of Smart Technologies in Orchard Management)
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22 pages, 9279 KB  
Article
ORD-YOLO: A Ripeness Recognition Method for Citrus Fruits in Complex Environments
by Zhaobo Huang, Xianhui Li, Shitong Fan, Yang Liu, Huan Zou, Xiangchun He, Shuai Xu, Jianghua Zhao and Wenfeng Li
Agriculture 2025, 15(15), 1711; https://doi.org/10.3390/agriculture15151711 - 7 Aug 2025
Cited by 10 | Viewed by 2477
Abstract
With its unique climate and geographical advantages, Yunnan Province in China has become one of the country’s most important citrus-growing regions. However, the dense foliage and large fruit size of citrus trees often result in significant occlusion, and the fluctuating light intensity further [...] Read more.
With its unique climate and geographical advantages, Yunnan Province in China has become one of the country’s most important citrus-growing regions. However, the dense foliage and large fruit size of citrus trees often result in significant occlusion, and the fluctuating light intensity further complicates accurate assessment of fruit maturity. To address these challenges, this study proposes an improved model based on YOLOv8, named ORD-YOLO, for citrus fruit maturity detection. To enhance the model’s robustness in complex environments, several key improvements have been introduced. First, the standard convolution operations are replaced with Omni-Dimensional Dynamic Convolution (ODConv) to improve feature extraction capabilities. Second, the feature fusion process is optimized and inference speed is increased by integrating a Re-parameterizable Generalized Feature Pyramid Network (RepGFPN). Third, the detection head is redesigned using a Dynamic Head structure that leverages dynamic attention mechanisms to enhance key feature perception. Additionally, the loss function is optimized using InnerDIoU to improve object localization accuracy. Experimental results demonstrate that the enhanced ORD-YOLO model achieves a precision of 93.83%, a recall of 91.62%, and a mean Average Precision (mAP) of 96.92%, representing improvements of 4.66%, 3.3%, and 3%, respectively, over the original YOLOv8 model. ORD-YOLO not only maintains stable and accurate citrus fruit maturity recognition under complex backgrounds, but also significantly reduces misjudgment caused by manual assessments. Furthermore, the model enables real-time, non-destructive detection. When deployed on harvesting robots, it can substantially increase picking efficiency and reduce post-maturity fruit rot due to delayed harvesting. These advancements contribute meaningfully to the quality improvement, efficiency enhancement, and digital transformation of the citrus industry. Full article
(This article belongs to the Special Issue Application of Smart Technologies in Orchard Management)
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28 pages, 15894 KB  
Article
Laser Scanning for Canopy Characterization in Hazelnut Trees: A Preliminary Approach to Define Growth Habitus Descriptor
by Raffaella Brigante, Laura Marconi, Simona Lucia Facchin, Franco Famiani, Marta Sánchez Piñero, Silvia Portarena, Rodrigo José De Vargas, Fabiola Villa, Chiara Traini, Alessandra Vinci, Fabio Radicioni and Daniela Farinelli
Agriculture 2025, 15(12), 1251; https://doi.org/10.3390/agriculture15121251 - 9 Jun 2025
Cited by 5 | Viewed by 1417
Abstract
The accurate definition of tree growth descriptors is a crucial step in enhancing orchard management, allowing cultivar identification within an orchard and in new genotype selection for breeding programs. In apple, almond, and olive orchards, Terrestrial Laser Scanning (TLS) technologies have been already [...] Read more.
The accurate definition of tree growth descriptors is a crucial step in enhancing orchard management, allowing cultivar identification within an orchard and in new genotype selection for breeding programs. In apple, almond, and olive orchards, Terrestrial Laser Scanning (TLS) technologies have been already used to identify different architectural groups, but not in hazelnut yet. This study utilized TLS to investigate the canopy structure of hazelnut trees of four different Italian varieties, with and without leaves. TLS proved to be a sensor capable of collecting three-dimensional data from hazelnut field trials and allowed the definition and selection of hazelnut plant descriptors by morphological traits and morphological indexes. Nineteen descriptors, eight morphologic traits and 11 morphological indexes have been identified as reliable suitable descriptors of hazelnut cultivar and in breeding evaluations, according to Biodiversity, FAO and CIHEAM. Many of the selected descriptors are related to the tree habit, vigour and branching density. Two useful indexes have also been defined: Canopy Uprightness (CU) Index and the Index of Canopy Opening (ICO). The descriptors allowed us to distinguish the four studied hazelnut cultivars based on their growth habit; in particular the cultivar Tonda Gentile delle Langhe showed a growth habit that is a lot different from that of the other ones. Full article
(This article belongs to the Special Issue Application of Smart Technologies in Orchard Management)
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Review

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30 pages, 1653 KB  
Review
Applications and Challenges of Visible-Near-Infrared and Mid-Infrared Spectroscopy in Soil Analysis: Chemometric Approaches and Data Fusion
by Govind Dnyandev Vyavahare, Jin-Ju Yun, Jae-Hyuk Park, Jae-Hong Shim, Seong Heon Kim, Kyeongyeong Kim, Ahnsung Roh, So Hui Kim, Ho Jun Jang, Wartini Ng and Sangho Jeon
Agriculture 2026, 16(1), 135; https://doi.org/10.3390/agriculture16010135 - 5 Jan 2026
Viewed by 1491
Abstract
Infrared (IR) spectroscopy has emerged as a rapid, cost-effective, and reliable alternative to traditional methods, enabling real-time, indirect monitoring of nutrients. Most reviews have discussed visible-near-infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy individually for soil analysis. This review highlights the application of IR spectroscopy, [...] Read more.
Infrared (IR) spectroscopy has emerged as a rapid, cost-effective, and reliable alternative to traditional methods, enabling real-time, indirect monitoring of nutrients. Most reviews have discussed visible-near-infrared (Vis-NIR) and mid-infrared (MIR) spectroscopy individually for soil analysis. This review highlights the application of IR spectroscopy, particularly Vis-NIR, MIR spectroscopy, and their data fusion, coupled with chemometrics and spectral preprocessing for estimating soil attributes. Additionally, the crucial functions of assessing model accuracy and validating model estimates of soil properties are discussed. Partial least squares regression (PLSR) was used in more than 100 studies in 2022. Based on the literature published from 2020 to 2025, the data fusion method predicts soil properties more accurately. This review also sheds light on recent advances in spectroscopic methods, including improvements in speed (e.g., MIR spectroscopy is up to 12 times faster than traditional methods), instrument miniaturization, and integration with portable devices, which can make field analysis more affordable. However, the sensitivity of IR spectroscopy to soil moisture, sample heterogeneity, vegetation cover, and calibration transfer issues remains a significant challenge in certain studies. Therefore, a discussion on the challenges in implementing this technique is included in this review, and future perspectives, such as integration of various sensors and portable devices for real-time soil assessment, are successively discussed. Full article
(This article belongs to the Special Issue Application of Smart Technologies in Orchard Management)
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49 pages, 3978 KB  
Review
A Crawling Review of Fruit Tree Image Segmentation
by Il-Seok Oh and Jin-Seon Lee
Agriculture 2025, 15(21), 2239; https://doi.org/10.3390/agriculture15212239 - 27 Oct 2025
Viewed by 2851
Abstract
Fruit tree image segmentation is an essential problem in automating a variety of agricultural tasks such as phenotyping, harvesting, spraying, and pruning. Many research papers have proposed a diverse spectrum of solutions suitable for specific tasks and environments. The review scope of this [...] Read more.
Fruit tree image segmentation is an essential problem in automating a variety of agricultural tasks such as phenotyping, harvesting, spraying, and pruning. Many research papers have proposed a diverse spectrum of solutions suitable for specific tasks and environments. The review scope of this paper is confined to the front views of fruit trees, and 207 relevant papers proposing tree image segmentation in an orchard environment are collected using a newly designed crawling review method. These papers are systematically reviewed based on a four-tier taxonomy that sequentially considers the method, image, task, and fruit. This taxonomy will assist readers to intuitively grasp the big picture of these research activities. Our review reveals that the most noticeable deficiency of the previous studies was the lack of a versatile dataset and segmentation model that could be applied to a variety of tasks and environments. Six important future research topics, such as building large-scale datasets and constructing foundation models, are suggested, with the expectation that these will pave the way to building a versatile tree segmentation module. Full article
(This article belongs to the Special Issue Application of Smart Technologies in Orchard Management)
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