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

Deadline for manuscript submissions: 15 September 2025 | Viewed by 5767

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Special Issue Editors

Dr. Wei Ma

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Guest Editor

Institute of Urban Agriculture, Chinese Academy of Agriculture Sciences, Chengdu 610213, China
Interests: agricultural robots; intelligent gardening robots
Special Issues, Collections and Topics in MDPI journals

Dr. Zhiwei Tian

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Guest Editor Assistant

Institute of Urban Agriculture, Chinese Academy of Agricultural Sciences, Chengdu 610213, China
Interests: agricultural robotics; grippers; mechanical design; machine vision; simulation

Special Issue Information

Dear Colleagues,

Facility agriculture refers to agricultural production activities carried out at artificial facilities to improve crop yield and quality and achieve year-round production. The management of facility crops should take into account efficiency and precision, striving to continuously improve yield and quality through intelligent technical means. As a global research hotspot, facility agriculture robots have played a very important role in this field, and the future will see the development of more interesting and effective facility production techniques for crops.

The application of facility agriculture robots requires, as a basis, research into agronomic mechanisms such as fruit mechanical characteristics, to avoid mechanical damage, and accurate image recognition, to reduce the number of vegetable flowers. Research into these basic agronomic theories represents the premise behind robots being used for efficient production. In the future, the ultimate goal is to build an unmanned and autonomous facility agricultural food production system to provide humans with healthier food. We hope that more scholars will publish high-quality research results to promote development in this field.

Dr. Wei Ma
Guest Editor

Dr. Zhiwei Tian
Guest Editor Assistant

Manuscript Submission Information

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Keywords

autonomous control robot
intelligent agriculture
laser intelligent fertilization
agricultural sensor

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Published Papers (9 papers)

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Research

19 pages, 3873 KB

Open AccessArticle

Improving Rice Nitrogen Nutrition Index Estimation Using UAV Images Combined with Meteorological and Fertilization Variables

by Zhengchao Qiu, Fei Ma, Jianmin Zhou and Changwen Du

Agronomy 2025, 15(8), 1946; https://doi.org/10.3390/agronomy15081946 - 12 Aug 2025

Viewed by 502

Abstract

Real-time and accurate monitoring of rice nitrogen status is essential for precision nitrogen management. Although unmanned aerial vehicle (UAV)-based spectral sensors have been widely used, existing estimation models that rely solely on crop phenotypes still suffer from limited accuracy and stability. In this study, the UAV vegetation indices (VIs), meteorological parameters (M) and fertilization (F) data were incorporated as input variables to establish rice N nutrition index (NNI) estimation models using three machine learning (ML) algorithms (adaptive boosting (AB), partial least squares (PLSR) and random forest (RF). The results showed that the models’ predictive accuracy ranked as follows based on input variable combinations: VI + M + F > VI + F > VI + M > VI. Among the three ML models, the RF algorithm demonstrated the best performance and achieved validation R² values ranging from 0.94 to 0.95 across all growth stages. Both meteorology and fertilization factors benefited the model, with their incorporation greatly improving model accuracy. This demonstrated the potential to enhance the diagnosis of seasonal rice nitrogen status and provide guidance for in-season site-specific N management through consumer-grade UAV imagery and machine learning. Full article

(This article belongs to the Special Issue Facility Agriculture Robots and Autonomous Unmanned Management for Crops)

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20 pages, 19463 KB

Open AccessArticle

Enhanced Visual Detection and Path Planning for Robotic Arms Using Yolov10n-SSE and Hybrid Algorithms

by Hongjun Wang, Anbang Zhao, Yongqi Zhong, Gengming Zhang, Fengyun Wu and Xiangjun Zou

Agronomy 2025, 15(8), 1924; https://doi.org/10.3390/agronomy15081924 - 9 Aug 2025

Viewed by 438

Abstract

Pineapple harvesting in natural orchard environments faces challenges such as high occlusion rates caused by foliage and the need for complex spatial planning to guide robotic arm movement in cluttered terrains. This study proposes an innovative visual detection model, Yolov10n-SSE, which integrates split convolution (SPConv), squeeze-and-excitation (SE) attention, and efficient multi-scale attention (EMA) modules. These improvements enhance detection accuracy while reducing computational complexity. The proposed model achieves notable performance gains in precision (93.8%), recall (84.9%), and mAP (91.8%). Additionally, a dimensionality-reduction strategy transforms 3D path planning into a more efficient 2D image-space task using point clouds from a depth camera. Combining the artificial potential field (APF) method with an improved RRT* algorithm mitigates randomness, ensures obstacle avoidance, and reduces computation time. Experimental validation demonstrates the superior stability of this approach and its generation of collision-free paths, while robotic arm simulation in ROS confirms real-world feasibility. This integrated approach to detection and path planning provides a scalable technical solution for automated pineapple harvesting, addressing key bottlenecks in agricultural robotics and fostering advancements in fruit-picking automation. Full article

(This article belongs to the Special Issue Facility Agriculture Robots and Autonomous Unmanned Management for Crops)

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23 pages, 5773 KB

Open AccessArticle

Study on Cherry Blossom Detection and Pollination Parameter Optimization Using the SMD-YOLO Model

by Longlong Ren, Yonghui Du, Yuqiang Li, Ang Gao, Wei Ma, Yuepeng Song and Xingchang Han

Agronomy 2025, 15(8), 1915; https://doi.org/10.3390/agronomy15081915 - 8 Aug 2025

Viewed by 398

Abstract

In response to the need for precise blossom identification and optimization of key operational parameters in intelligent cherry spraying pollination, the SMD-YOLO (You Only Look Once with spatial and channel reconstruction convolution, multi-scale channel attention, and dual convolution modules) cherry blossom detection model is proposed, along with a pollination experiment platform for parameter optimization. The SMD-YOLO model, built upon YOLOv11, enhances feature extraction through the ScConvC3k2 (spatial and channel reconstruction convolution C3k2) module, incorporates the MSCA (multi-scale channel attention) attention mechanism, and employs the DualConv module for a lightweight design, ensuring both detection accuracy and operational efficiency. Tested on a self-constructed cherry blossom dataset, the model delivered a precision of 87.6%, a recall rate of 86.1%, and an mAP (mean average precision) reaching 93.1% with a compact size of 4765 KB, 2.28 × 10⁶ parameters, a computational cost of 5.8 G, and a detection speed of 75.76 FPS, demonstrating strong practicality and potential for embedded real-time detection in edge devices, such as cherry pollination robots. To further enhance pollination effectiveness, a dedicated pollination experiment bench was designed, and a second-order orthogonal rotational combination experiment method was employed to systematically optimize three key parameters: spraying distance, spraying time, and liquid flow rate. Experimental results indicate that the optimal pollination effect occurs when the spraying distance is 3.4 cm, spraying time is 1.9 s, and liquid flow rate is 339 mL/min, with a deposition amount of 0.18 g and a coverage rate of 97.25%. This study provides a high-precision image detection algorithm and operational parameter optimization basis for intelligent and precise cherry blossom pollination. Full article

(This article belongs to the Special Issue Facility Agriculture Robots and Autonomous Unmanned Management for Crops)

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26 pages, 10480 KB

Open AccessArticle

Monitoring Chlorophyll Content of Brassica napus L. Based on UAV Multispectral and RGB Feature Fusion

by Yongqi Sun, Jiali Ma, Mengting Lyu, Jianxun Shen, Jianping Ying, Skhawat Ali, Basharat Ali, Wenqiang Lan, Yiwa Hu, Fei Liu, Weijun Zhou and Wenjian Song

Agronomy 2025, 15(8), 1900; https://doi.org/10.3390/agronomy15081900 - 7 Aug 2025

Viewed by 488

Abstract

Accurate prediction of chlorophyll content in Brassica napus L. (rapeseed) is essential for monitoring plant nutritional status and precision agricultural management. The current study focuses on single cultivars, limiting general applicability. This study used unmanned aerial vehicle (UAV)-based RGB and multispectral imagery to evaluate six rapeseed cultivars chlorophyll content across mixed-growth stages, including seedling, bolting, and initial flowering stages. The ExG-ExR threshold segmentation was applied to remove background interference. Subsequently, color and spectral indices were extracted from segmented images and ranked according to their correlations with measured chlorophyll content. Partial Least Squares Regression (PLSR), Multiple Linear Regression (MLR), and Support Vector Regression (SVR) models were independently established using subsets of the top-ranked features. Model performance was assessed by comparing prediction accuracy (R² and RMSE). Results demonstrated significant accuracy improvements following background removal, especially for the SVR model. Compared to data without background removal, accuracy increased notably with background removal by 8.0% (R²p improved from 0.683 to 0.763) for color indices and 3.1% (R²p from 0.835 to 0.866) for spectral indices. Additionally, stepwise fusion of spectral and color indices further improved prediction accuracy. Optimal results were obtained by fusing the top seven color features ranked by correlation with chlorophyll content, achieving an R²p of 0.878 and an RMSE of 52.187 μg/g. These findings highlight the effectiveness of background removal and feature fusion in enhancing chlorophyll prediction accuracy. Full article

(This article belongs to the Special Issue Facility Agriculture Robots and Autonomous Unmanned Management for Crops)

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19 pages, 1661 KB

Open AccessArticle

Evaluation of the Field Performance and Economic Feasibility of Mechanized Onion Production in the Republic of Korea

by Jae-Seo Hwang and Wan-Soo Kim

Agronomy 2025, 15(7), 1721; https://doi.org/10.3390/agronomy15071721 - 17 Jul 2025

Viewed by 632

Abstract

Onion cultivation in the Republic of Korea is increasingly threatened by labor shortages and an aging rural population, underscoring the growing importance of mechanization. This study evaluated the combined and individual performances and economic feasibility of mechanized transplanting, stem cutting, harvesting, and collecting operations using work efficiency; the missing plant, stem cutting, damage, and dropout rates; and foreign matter content as indicators. Mechanized operations achieved up to 358-fold higher work efficiencies than manual labor operations. However, in terms of marketability, performance was inferior due to missing plants, improperly cut stems, damaged bulbs, dropped onions, and foreign matter contamination. The economic analysis indicated that the use of individual machines is advantageous for farms larger than 10.2 ha for transplanting, 1.14 ha for stem cutting, 0 ha for harvesting (i.e., profitable regardless of farm size), and 6.95 ha for collecting. For fully mechanized operations, using machines for all four processes, the break-even area was found to be 3.63 ha, with a payback period of 2.1 years. These findings are expected to serve as a foundational reference for onion growers considering the adoption of mechanization. Full article

(This article belongs to the Special Issue Facility Agriculture Robots and Autonomous Unmanned Management for Crops)

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14 pages, 3779 KB

Open AccessArticle

Technological Parameter Optimization of Double-Press Precision Depth-Control Seeding and Its Application in Rice Production

by Yangjie Shi, Xingye Shen, Xinhui Cheng, Jintao Xu, Jiawang Hong, Lianjie Han, Xiaobo Xi and Ruihong Zhang

Agronomy 2025, 15(7), 1704; https://doi.org/10.3390/agronomy15071704 - 15 Jul 2025

Viewed by 442

Abstract

Current rice cultivation relies on mechanical transplanting, which is costly and complex, and direct seeding, which suffers from poor quality and low efficiency. To address these issues, a double-press precision depth-control seeding method was developed in this study. Discrete element modeling (DEM) was employed to optimize key operational parameters—compaction force, soil covering cutter rotational speed, and penetration depth—using qualified seeding depth and missed seeding rates as performance metrics. Optimal results were achieved at a 60 kPa compaction force, a 300 rpm rotational speed, and a 7 cm penetration depth. A prototype seeder was manufactured and evaluated in three-year field trials against conventional dry direct seeders and mechanical transplanters. The double-press seeder demonstrated significantly superior performance compared to conventional direct seeding. It optimized the crop population structure by maintaining a high tiller number while increasing the productive tiller rate, resulting in stable annual yields exceeding 10.11 t·hm⁻². Although its yield was slightly lower than that of mechanical transplanting, the double-press seeder offers a compelling practical alternative due to its operational convenience and economic benefits. Full article

(This article belongs to the Special Issue Facility Agriculture Robots and Autonomous Unmanned Management for Crops)

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24 pages, 28445 KB

Open AccessArticle

Enhanced Multi-Threshold Otsu Algorithm for Corn Seedling Band Centerline Extraction in Straw Row Grouping

by Yuanyuan Liu, Yuxin Du, Kaipeng Zhang, Hong Yan, Zhiguo Wu, Jiaxin Zhang, Xin Tong, Junhui Chen, Fuxuan Li, Mengqi Liu, Yueyong Wang and Jun Wang

Agronomy 2025, 15(7), 1575; https://doi.org/10.3390/agronomy15071575 - 27 Jun 2025

Viewed by 331

Abstract

Straw row grouping is vital in conservation tillage for precision seeding, and accurate centerline extraction of the seedling bands enhances agricultural spraying efficiency. However, the traditional single-threshold Otsu segmentation struggles with adaptability and accuracy under complex field conditions. To overcome these issues, this study proposes an adaptive multi-threshold Otsu algorithm optimized by a Simulated Annealing-Enhanced Differential Evolution–Whale Optimization Algorithm (SADE-WOA). The method avoids premature convergence and improves population diversity by embedding the crossover mechanism of Differential Evolution (DE) into the Whale Optimization Algorithm (WOA) and introducing a vector disturbance strategy. It adaptively selects thresholds based on straw-covered image features. Combined with least-squares fitting, it suppresses noise and improves centerline continuity. The experimental results show that SADE-WOA accurately separates soil regions while preserving straw texture, achieving higher between-class variance and significantly faster convergence than the other tested algorithms. It runs at just one-tenth of the time of the Grey Wolf Optimizer and one-ninth of that of DE and requires only one-sixth to one-seventh of the time needed by DE-GWO. During centerline fitting, the mean yaw angle error (MEA) ranged from 0.34° to 0.67°, remaining well within the 5° tolerance required for agricultural navigation. The root-mean-square error (RMSE) fell between 0.37° and 0.73°, while the mean relative error (MRE) stayed below 0.2%, effectively reducing the influence of noise and improving both accuracy and robustness. Full article

(This article belongs to the Special Issue Facility Agriculture Robots and Autonomous Unmanned Management for Crops)

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18 pages, 5323 KB

Open AccessArticle

Surface Defect and Malformation Characteristics Detection for Fresh Sweet Cherries Based on YOLOv8-DCPF Method

by Yilin Liu, Xiang Han, Longlong Ren, Wei Ma, Baoyou Liu, Changrong Sheng, Yuepeng Song and Qingda Li

Agronomy 2025, 15(5), 1234; https://doi.org/10.3390/agronomy15051234 - 19 May 2025

Cited by 2 | Viewed by 827

Abstract

The damaged and deformed fruits of fresh berries severely restrict the economic value of produce, and accurate identification and grading methods have become a global research hotspot. To address the challenges of rapid and accurate defect detection in intelligent cherry sorting systems, this study proposes an enhanced YOLOv8n-based framework for sweet cherry defect identification. First, the dilation-wise residual (DWR) module replaces the conventional C2f structure, allowing for the adaptive capture of both local and global features through multi-scale convolution. This enhances the recognition accuracy of subtle surface defects and large-scale damages on cherries. Second, a channel attention feature fusion mechanism (CAFM) is incorporated at the front end of the detection head, which enhances the model’s ability to identify fine defects on the cherry surface. Additionally, to improve bounding box regression accuracy, powerful-IoU (PIoU) replaces the traditional CIoU loss function. Finally, self-distillation technology is introduced to further improve the mode’s generalization capability and detection accuracy through knowledge transfer. Experimental results show that the YOLOv8-DCPF model achieves precision, mAP, recall, and F1 score rates of 92.6%, 91.2%, 89.4%, and 89.0%, respectively, representing improvements of 6.9%, 5.6%, 6.1%, and 5.0% over the original YOLOv8n baseline network. The proposed model demonstrates high accuracy in cherry defect detection, providing an efficient and precise solution for intelligent cherry sorting in agricultural engineering applications. Full article

(This article belongs to the Special Issue Facility Agriculture Robots and Autonomous Unmanned Management for Crops)

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18 pages, 4939 KB

Open AccessArticle

Design and Evaluation of an Innovative Thermoelectric-Based Dehumidifier for Greenhouses

by Xiaobei Han, Tianxiang Liu, Yuliang Cai, Dequn Wang, Xiaoming Wei, Yunrui Hai, Rongchao Shi and Wenzhong Guo

Agronomy 2025, 15(5), 1194; https://doi.org/10.3390/agronomy15051194 - 15 May 2025

Viewed by 936

Abstract

Crops in greenhouses located in cold climates are frequently affected by high relative humidity (RH). This study presents the design, testing, and analysis of a dehumidifier based on thermoelectric cooling. Thermoelectric dehumidifiers (TEDs) are capable of dehumidifying greenhouses in cold regions while recovering heat for indoor air heating. The design of a TED is based on the specific characteristics of thermoelectric coolers (TECs). A TED consists of a cabinet, four heat exchangers, a duct fan, a water pump, and auxiliary components. The TED performance was evaluated in a Chinese solar greenhouse (CSG) with a volume of approximately 160 m³. The input voltage of the TECs, fan airflow rate, and cold-side fin area affected the TED performance, with their influence varying in magnitude. The radar chart results show that the optimal operating parameters are as follows: a fan airflow rate of 300 m³/h, a TEC input voltage of 15 V, and a cold-side fin area of 0.15 m². With the TED running for 120 min under the optimal parameters, the RH in the CSG decreased by 25.5%, while the air temperature increased by 3.4 °C. The installation of the TED at the bottom of the CSG improved the growing environment of the crops, particularly in the vertical range between 0.2 m and 1.5 m height inside the greenhouse. These findings provide a valuable reference for applying thermoelectric cooling technology in the greenhouse field. Full article

(This article belongs to the Special Issue Facility Agriculture Robots and Autonomous Unmanned Management for Crops)

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