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Unmanned Farms in Smart Agriculture

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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: 25 February 2025 | Viewed by 12246

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Prof. Dr. Zhiyan Zhou

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Guest Editor

College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: onboard sensor design; sensor fusion; signal/image processing; agriculture; controlling system; navigation and position/orientation; autonomous take-off and landing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Lian Hu

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Guest Editor

College of Engineering, South China Agricultural University, Guangzhou 510642, China
Interests: intelligent agricultural machinery and equipment; unmanned farm; agricultural robots; agricultural artificial intelligence

Special Issue Information

Dear Colleagues,

At present, a new round of scientific and technological revolution and industrial change is emerging; information technology, biotechnology, new materials technology and new energy technology have been widely integrated into the field of agriculture, giving rise to a large number of strategic new industries, advanced manufacturing of agricultural equipment, agricultural Internet of Things, agricultural data and agricultural robotics and other technologies gradually applied to various areas of agricultural production. Smart agriculture has also shown a strong momentum of development. Unmanned farm is an important way to achieve wisdom agriculture. Unmanned farms are supported by biotechnology, intelligent farm machinery and information technology. Biotechnology provides varieties and cultivation patterns adapted to mechanized operations for unmanned farms, intelligent farm machinery provides equipment support for automated operations of unmanned farms, and information technology provides support for precise positioning, data transmission and intelligent management of unmanned farms for farm machinery operations.

In this Special Issue, we aim to exchange knowledge on any aspect related to unmanned farms in smart agriculture, thus promoting the rapid development of agricultural mechanization and intelligence, and facilitating the construction of unmanned farms in a period of rapid development.

Prof. Dr. Zhiyan Zhou
Prof. Dr. Lian Hu
Guest Editors

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Keywords

smart agriculture
unmanned farms
intelligent farm machinery
automatic navigation
precision operation
unmanned farm
information technology
agricultural Internet of Things
smart management

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Published Papers (9 papers)

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Research

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20 pages, 9880 KiB

Open AccessArticle

Estimating Rice Leaf Nitrogen Content and Field Distribution Using Machine Learning with Diverse Hyperspectral Features

by Ting Tian, Jianliang Wang, Yueyue Tao, Fangfang Ji, Qiquan He, Chengming Sun and Qing Zhang

Agronomy 2024, 14(12), 2760; https://doi.org/10.3390/agronomy14122760 - 21 Nov 2024

Viewed by 174

Abstract

Leaf nitrogen content (LNC) is a vital agronomic parameter in rice, commonly used to evaluate photosynthetic capacity and diagnose crop nutrient levels. Nitrogen deficiency can significantly reduce yield, underscoring the importance of accurate LNC estimation for practical applications. This study utilizes hyperspectral UAV imagery to acquire rice canopy data, applying various machine learning regression algorithms (MLR) to develop an LNC estimation model and create a nitrogen concentration distribution map, offering valuable guidance for subsequent field nitrogen management. The analysis incorporates four types of spectral data extracted throughout the rice growth cycle: original reflectance bands (OR bands), vegetation indices (VIs), first-derivative spectral bands (FD bands), and hyperspectral variable parameters (HSPs) as model inputs, while measured nitrogen concentration serves as the output. Results demonstrate that the random forest regression (RFR) and gradient boosting decision tree (GBDT) algorithms performed effectively, with the GBDT achieving the highest average R² of 0.76 across different nitrogen treatments. Among the nitrogen estimation models for various rice varieties, RFR exhibited superior accuracy, achieving an R² of 0.95 for the SuXiangJing100 variety, while the GBDT reached 0.93. Meanwhile, the support vector machine regression (SVMR) showed slightly lower accuracy, and partial least-squares regression (PLSR) was the least effective. This study developed an LNC estimation method applicable to the whole growth stage of common rice varieties. The method is suitable for estimating rice LNC across different growth stages, varieties, and nitrogen treatments, and it also provides a reference for nitrogen estimation and fertilization planning at flight altitudes other than the 120 m used in this study. Full article

(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)

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20 pages, 15485 KiB

Open AccessArticle

Integrated Navigation Method for Orchard-Dosing Robot Based on LiDAR/IMU/GNSS

by Wang Wang, Jifeng Qin, Dezhao Huang, Furui Zhang, Zhijie Liu, Zheng Wang and Fuzeng Yang

Agronomy 2024, 14(11), 2541; https://doi.org/10.3390/agronomy14112541 - 28 Oct 2024

Viewed by 467

Abstract

To enhance the localization reliability and obstacle avoidance performance of the dosing robot in complex orchards, this study proposed an integrated navigation method using LiDAR, IMU, and GNSS. Firstly, the tightly coupled LIO-SAM algorithm was used to construct an orchard grid map for path planning and obstacle avoidance. Then, a global localization model based on RTK-GNSS was developed to achieve accurate and efficient initial localization of the robot’s coordinates and heading, and a Kalman filter was applied to integrate GNSS and IMU to improve robustness. Next, an improved A* algorithm was introduced to ensure the global operational path maintained a safe distance from obstacles, while the DWA algorithm handled dynamic obstacle avoidance. Field tests showed that the global localization model achieved an accuracy of 2.215 cm, with a standard deviation of 1 cm, demonstrating stable positioning performance. Moreover, the global path maintained an average safe distance of 50.75 cm from the obstacle map. And the robot exhibited a maximum absolute lateral deviation of 9.82 cm, with an average of 4.16 cm, while maintaining a safe distance of 1 m from dynamic obstacles. Overall, the robot demonstrated smooth and reliable autonomous navigation, successfully completing its tasks. Full article

(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)

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14 pages, 2494 KiB

Open AccessArticle

The Influence of Electrostatic Spraying with Waist-Shaped Charging Devices on the Distribution of Long-Range Air-Assisted Spray in Greenhouses

by Jinlong Lin, Jinping Cai, Jingyi Ouyang, Liping Xiao and Baijing Qiu

Agronomy 2024, 14(10), 2278; https://doi.org/10.3390/agronomy14102278 - 3 Oct 2024

Viewed by 860

Abstract

Electrostatic spraying is considered an effective means to improve the efficacy of pesticide application and reduce pesticide consumption. However, the effectiveness of electrostatic spraying needs further validation in greenhouse environments, especially in long-range air-assisted spraying scenarios. A waist-shaped charging device has been improved to obtain a maximum charge-to-mass ratio of 4.4 mC/kg at an applied voltage of 6 kV in a laboratory setting, representing an increase of approximately 84.9% compared to a commercial circular charging electrode with a fan-shaped nozzle. A comparative air-assisted spray test between electrostatic deactivation (EDAS) and electrostatic activation (EAAS) was conducted on greenhouse tomato crops using a single hanging track autonomous sprayer equipped with a pair of waist-shaped charging devices. The results showed that EAAS yielded an overall average coverage of 28.4%, representing a significant 10.9% improvement over the 25.6% coverage achieved with EDAS. The overall coefficient of variation (CV) for EDAS and EAAS was 62.0% and 48.0%, respectively. Within these, the CV for the average coverage of the sample set reflecting axial distribution uniformity was 33.4% and 31.4%, respectively. Conversely, the CV for the average coverage of the sample group reflecting radial distribution uniformity was 33.7% and 17.9%, respectively. The results indicate that the waist-shaped charging device possesses remarkable charging capabilities, presenting favorable application prospects for long-range air-assisted spraying in greenhouses. The electrostatic application has a positive effect on enhancing the average coverage and improving the overall distribution uniformity. Notably, it significantly improves the radial distribution uniformity of the air-assisted spray at long range, albeit with limited improvement in the axial distribution uniformity. Full article

(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)

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15 pages, 11454 KiB

Open AccessArticle

Accurate Characterization of Soil Moisture in Wheat Fields with an Improved Drought Index from Unmanned Aerial Vehicle Observations

by Minghan Cheng, Xintong Lu, Zhangxin Liu, Guanshuo Yang, Lili Zhang, Binqian Sun, Zhian Wang, Zhengxian Zhang, Ming Shang and Chengming Sun

Agronomy 2024, 14(8), 1783; https://doi.org/10.3390/agronomy14081783 - 14 Aug 2024

Viewed by 1062

Abstract

Soil moisture content is a crucial indicator for understanding the water requirements of crops. The effective monitoring of soil moisture content can provide support for irrigation decision-making and agricultural water management. Traditional ground-based measurement methods are time-consuming and labor-intensive, and point-scale monitoring cannot effectively represent the heterogeneity of soil moisture in the field. Unmanned aerial vehicle (UAV) remote sensing technology offers an efficient and convenient way to monitor soil moisture content in large fields, but airborne multispectral data are prone to spectral saturation effects, which can further affect the accuracy of monitoring soil moisture content. Therefore, we aim to construct effective drought indices for the accurate characterization of soil moisture content in winter wheat fields by utilizing unmanned aerial vehicles (UAVs) equipped with LiDAR, thermal infrared, and multispectral sensors. Initially, we estimated wheat plant height using airborne LiDAR sensors and improved traditional spectral indices in a structured manner based on crop height. Subsequently, we constructed the normalized land surface temperature–structured normalized difference vegetation index (NLST-SNDVI) space by combining the SNDVI with land surface temperature and calculated the improved Temperature–Vegetation Drought Index (iTVDI). The results are summarized as follows: (1) the structured spectral indices exhibit better resistance to spectral saturation, making the NLST-SNDVI space closer to expectations than the NLST-NDVI space, with higher fitting accuracy for wet and dry edges; (2) the iTVDI calculated based on the NLST-SNDVI space can effectively characterize soil moisture content, showing a significant correlation with measured surface soil moisture content; (3) the global Moran’s I calculated based on iTVDI deviations ranges between 0.18 and 0.30, all reaching significant levels, indicating that iTVDI has good spatial applicability. In conclusion, this study proved the effectiveness of the drought index based on a structured vegetation index, and the results can provide support for crop moisture monitoring and irrigation decision-making in the field. Full article

(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)

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17 pages, 6291 KiB

Open AccessArticle

Influence of Shaped Hole and Seed Disturbance on the Precision of Bunch Planting with the Double-Hole Rice Vacuum Seed Meter

by Cheng Qian, Siyu He, Wei Qin, Youcong Jiang, Zishun Huang, Meilin Zhang, Minghua Zhang, Wenwu Yang and Ying Zang

Agronomy 2024, 14(4), 768; https://doi.org/10.3390/agronomy14040768 - 8 Apr 2024

Viewed by 896

Abstract

The double-hole rice vacuum seed meter is critical equipment for the planting precision of rice direct seeding. The effects of shaped holes and seed disturbance on the precision of rice bunch planting were investigated to improve the precision of bunch planting with the double-hole rice vacuum seed meter. A test bench with the rice vacuum seed meter was set up to analyze the trends in the quality of feed index, miss index, and multiple index of seed meters with different shaped holes at different speeds and vacuum pressures. Based on the optimal hole structure, different seed disturbance structures were designed to investigate the influence of the seed disturbance structure on the precision of bunch planting. A multiple linear regression model was established for the relationship between the disturbance structure, vacuum pressure, rotational speed, and the precision of bunch planting. Discrete element numerical simulation experiments were carried out to analyze the effect of disturbance structures on seeds. The planting precision of the seed meter with the shaped hole was significantly higher than that of the seed meter without the shaped hole while the shaped hole B was the optimum structure. Disturbance structure affects the quality of feed index, multiple index rate, and miss index. The planting precision of the seed disturbance structure II was better than the other structures. At a speed of 60 rpm and vacuum pressures of 2.0 kPa, 2.4 kPa, and 2.8 kPa, the qualities of feed index of seed disturbance structure II were 90%, 91.11%, and 89.17%, respectively, and the miss indexes were 2.96%, 1.94%, and 1.57%, respectively. At high rotational speeds, the precision of rice bunch planting with the seed disturbance structure is better than that without the seed disturbance structure. In the simulation test, the seed velocity and total force magnitude of the meter without disturbance structures were less than those with the disturbed structure. Simulation experiments showed that the seed disturbance structure breaks up the stacked state of seeds. Research has shown that the shaped hole holds the seed in a stable suction posture, which helps to increase the seed-filling rate. Seed disturbance improves seed mobility, thereby enhancing the precision of bunch planting. Full article

(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)

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25 pages, 9132 KiB

Open AccessArticle

A UAV-Borne Six-Vessel Negative-Pressure Enrichment Device with Filters Designed to Collect Infectious Fungal Spores in Rice Fields

by Xiaoyan Guo, Yuanzhen Ou, Konghong Deng, Xiaolong Fan, Rui Jiang and Zhiyan Zhou

Agronomy 2024, 14(4), 716; https://doi.org/10.3390/agronomy14040716 - 29 Mar 2024

Viewed by 917

Abstract

Fungal spores that cause infectious fungal diseases in rice are mainly transmitted through air. The existing fixed, portable or vehicle-mounted fungal spore collection devices used for rice infectious diseases have several disadvantages, such as low efficiency, large volume, low precision and incomplete information. In this study, a mobile fungal spore collection device is designed, consisting of six filters called “Capture-A”, which can collect spores and other airborne particles onto a filter located on a rotating disc of six filters that can be rotated to a position allowing for the capture of six individual samples. They are captured one at a time and designed and validated by capturing spores above the rice field, and the parameters of the key components of the collector are optimized through fluid simulation and verification experiments. The parameter combination of the “Capturer-A” in the best working state is as follows: sampling vessel filter screen with aperture size of 0.150 mm, bent air duct with inner diameter of 20 mm, negative pressure fan with 1500 Pa and spore sampling of cylindrical shape. In the field test, the self-developed “Capturer-A” was compared with the existing “YFBZ3” (mobile spore collection device made by Yunfei Co., Ltd., Zhengzhou, China). The two devices were experimented on at 15 sampling points in three diseased rice fields, and the samples were examined and counted under a microscope in the laboratory. It was found that the spores of rice blast disease and rice flax spot disease of rice were contained in the samples; the number of samples collected by a single sampling vessel of “Capturer-A” was about twice that of the device “YFBZ3”in the test. Full article

(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)

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18 pages, 10857 KiB

Open AccessArticle

Method and Experiments for Acquiring High Spatial Resolution Images of Abnormal Rice Canopy by Autonomous Unmanned Aerial Vehicle Field Inspection

by Qiangzhi Zhang, Xiwen Luo, Lian Hu, Chuqi Liang, Jie He, Pei Wang and Runmao Zhao

Agronomy 2023, 13(11), 2731; https://doi.org/10.3390/agronomy13112731 - 29 Oct 2023

Viewed by 1530

Abstract

The yield and quality of rice are closely related to field management. The automatic identification of field abnormalities, such as diseases and pests, based on computer vision currently mainly relies on high spatial resolution (HSR) images obtained through manual field inspection. In order to achieve automatic and efficient acquisition of HSR images, based on the capability of high-throughput field inspection of UAV remote sensing and combining the advantages of high-flying efficiency and low-flying resolution, this paper proposes a method of “far-view and close-look” autonomous field inspection by unmanned aerial vehicle (UAV) to acquire HSR images of abnormal areas in the rice canopy. First, the UAV equipped with a multispectral camera flies high to scan the whole field efficiently and obtain multispectral images. Secondly, abnormal areas (namely areas with poor growth) are identified from the multispectral images, and then the geographical locations of identified areas are positioned with a single-image method instead of the most used method of reconstruction, sacrificing part of positioning accuracy for efficiency. Finally, the optimal path for traversing abnormal areas is planned through the nearest-neighbor algorithm, and then the UAV equipped with a visible light camera flies low to capture HSR images of abnormal areas along the planned path, thereby acquiring the “close-look” features of the rice canopy. The experimental results demonstrate that the proposed method can identify abnormal areas, including diseases and pests, lack of seedlings, lodging, etc. The average absolute error (AAE) of single-image positioning is 13.2 cm, which can meet the accuracy requirements of the application in this paper. Additionally, the efficiency is greatly improved compared to reconstruction positioning. The ground sampling distance (GSD) of the acquired HSR image can reach 0.027 cm/pixel, or even smaller, which can meet the resolution requirements of even leaf-scale deep-learning classification. The HSR image can provide high-quality data for subsequent automatic identification of field abnormalities such as diseases and pests, thereby offering technical support for the realization of the UAV-based automatic rice field inspection system. The proposed method can also provide references for the automatic field management of other crops, such as wheat. Full article

(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)

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19 pages, 11965 KiB

Open AccessArticle

Method and Experiment for Quantifying Local Features of Hard Bottom Contours When Driving Intelligent Farm Machinery in Paddy Fields

by Tuanpeng Tu, Lian Hu, Xiwen Luo, Jie He, Pei Wang, Li Tian, Gaolong Chen, Zhongxian Man, Dawen Feng, Weirui Cen, Mingjin Li, Yuxuan Liu, Kang Hou, Le Zi, Mengdong Yue and Yuqin Li

Agronomy 2023, 13(7), 1949; https://doi.org/10.3390/agronomy13071949 - 23 Jul 2023

Cited by 2 | Viewed by 1365

Abstract

The hard bottom layer of a paddy field has a great influence on the driving stability and the operation quality and efficiency of intelligent farm machinery. For paddy field machinery, continuous improvements in the accuracy and operation efficiency of unmanned precision operations are needed to realize unmanned rice farming. In the context of unmanned farm machinery operation, the complicated hard bottom layer situation makes it difficult to quantify the local characteristics of paddy fields. In this paper, an unmanned direct rice seeding machine chassis is used to maneuver the operation field and collect the hard bottom layer information simultaneously. This information is used to design a data processing method that automatically calibrates the sensor installation error and performs abnormal value rejection and 3D sample curve denoising of the contour trajectory. A hard bottom layer surface profile evaluation method based on the local sliding surface roughness is also proposed. The local characteristics of the hard bottom layer were quantified, and the results from the test plots showed that the mean value of the local roughness was 0.0065, where 68.27% of the plots were distributed in the variation range of 0.0042~0.0087 and 99.73% were distributed in the variation range of 0~0.0133. Using the test field data, the surface roughness features were verified to describe the variability in representative working conditions, such as the transport, downfield, operation, and trapping of unmanned intelligent farm machinery. When driving intelligent farm machinery, the proposed method for quantifying local features of the hard bottom layer can provide feedback on the local environmental features at any given position of the machinery. The method also provides a reference for the design optimization of unmanned systems, which can help to realize speed adaption and improve the local path tracking control accuracy of smart farming machines. Full article

(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)

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Review

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32 pages, 11260 KiB

Open AccessEditor’s ChoiceReview

Comparative Analysis of Different UAV Swarm Control Methods on Unmanned Farms

by Rui Ming, Rui Jiang, Haibo Luo, Taotao Lai, Ente Guo and Zhiyan Zhou

Agronomy 2023, 13(10), 2499; https://doi.org/10.3390/agronomy13102499 - 28 Sep 2023

Cited by 16 | Viewed by 3831

Abstract

Unmanned farms employ a variety of sensors, automated systems, and data analysis techniques to enable fully automated and intelligent management. This not only heightens agricultural production efficiency but also reduces the costs associated with human resources. As integral components of unmanned farms’ automation systems, agricultural UAVs have been widely adopted across various operational stages due to their precision, high efficiency, environmental sustainability, and simplicity of operation. However, present-day technological advancement levels and relevant policy regulations pose significant restrictions on UAVs in terms of payload and endurance, leading to diminished task efficiency when a single UAV is deployed over large areas. Accordingly, this paper aggregates and analyzes research pertaining to UAV swarms from databases such as Google Scholar, ScienceDirect, Scopus, IEEE Xplorer, and Wiley over the past decade. An initial overview presents the current control methods for UAV swarms, incorporating a summary and analysis of the features, merits, and drawbacks of diverse control techniques. Subsequently, drawing from the four main stages of agricultural production (cultivation, planting, management, and harvesting), we evaluate the application of UAV swarms in each stage and provide an overview of the most advanced UAV swarm technologies utilized therein. Finally, we scrutinize and analyze the challenges and concerns associated with UAV swarm applications on unmanned farms and provide forward-looking insights into the future developmental trajectory of UAV swarm technology in unmanned farming, with the objective of bolstering swarm performance, scalability, and adoption rates in such settings. Full article

(This article belongs to the Special Issue Unmanned Farms in Smart Agriculture)

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