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A special issue of Drones (ISSN 2504-446X). This special issue belongs to the section "Drones in Agriculture and Forestry".

Deadline for manuscript submissions: closed (31 December 2023) | Viewed by 24889

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Special Issue Editor

Prof. Dr. Fei Liu

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Guest Editor

College of Biosystems Engineering and Food Science, Zhejiang University, Hangzhou 310058, China
Interests: smart agriculture; UAS; remote sensing; plant phenotype and disease-pest monitoring; crop yield prediction; variable spraying system; deep learning; imaging processing technology
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Special Issue Information

Dear Colleagues,

We are pleased to invite you to submit manuscripts to the MDPI Drones Special Issue on “UAS in Smart Agriculture”.

With the development of emerging information and digital technology, unmanned technology and equipment have become more important for the development of intelligent and sustainable agriculture. UAS is widely used in smart agriculture, including unmanned control systems, remote sensing information collection, and variable operation systems. Unmanned control systems mainly include intelligent control algorithms, communication technology, environmental awareness, and autonomous obstacle avoidance technology, which are aiming to improve the level of intelligent control. Remote sensing technology mainly includes plant phenotype, disease and pest monitoring, yield estimation, 3D information acquisition, multispectral and hyperspectral imaging sensors, and intelligence modeling technology, which are providing more efficient and dynamic data. Variable operation technology mainly includes intelligent decision making, prescription chart technology, and variable spraying and sowing operation, which are providing precise management and operation. Unmanned systems are widely used for field crops, orchards, and unmanned ecological farms. UAS will provide strong support to promote the green, healthy, ecological, and sustainable development of smart agriculture.

In this Special Issue on “UAS in Smart Agriculture”, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following keyword topics.

Prof. Dr. Fei Liu
Guest Editor

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. Drones 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

smart agriculture
digital agriculture
remote sensing
unmanned control and operation system
multiresource image processing technology
deep learning
plant phenotype
plant disease and pest diagnosis
crop and orchard yield monitoring
weed detection
soil monitoring
3D digital technology
spraying and sowing system
variable operation prescription technology

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Related Special Issue

UAS in Smart Agriculture: 2nd Edition in Drones (5 articles)

Published Papers (9 papers)

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Research

16 pages, 5017 KiB

Open AccessArticle

Air-to-Ground Path Loss Model at 3.6 GHz under Agricultural Scenarios Based on Measurements and Artificial Neural Networks

by Hanpeng Li, Kai Mao, Xuchao Ye, Taotao Zhang, Qiuming Zhu, Manxi Wang, Yurao Ge, Hangang Li and Farman Ali

Drones 2023, 7(12), 701; https://doi.org/10.3390/drones7120701 - 11 Dec 2023

Cited by 1 | Viewed by 2097

Abstract

Unmanned aerial vehicles (UAVs) have found expanding utilization in smart agriculture. Path loss (PL) is of significant importance in the link budget of UAV-aided air-to-ground (A2G) communications. This paper proposes a machine-learning-based PL model for A2G communication in agricultural scenarios. On this basis, a double-weight neurons-based artificial neural network (DWN-ANN) is proposed, which can strike a fine equilibrium between the amount of measurement data and the accuracy of predictions by using ray tracing (RT) simulation data for pre-training and measurement data for optimization training. Moreover, an RT pre-correction module is introduced into the DWN-ANN to optimize the impact of varying farmland materials on the accuracy of RT simulation, thereby improving the accuracy of RT simulation data. Finally, channel measurement campaigns are carried out over a farmland area at 3.6 GHz, and the measurement data are used for the training and validation of the proposed DWN-ANN. The prediction results of the proposed PL model demonstrate a fine concordance with the measurement data and are better than the traditional empirical models. Full article

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

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19 pages, 9044 KiB

Open AccessArticle

A Ground Point Fitting Method for Winter Wheat Height Estimation Using UAV-Based SfM Point Cloud Data

by Xiaozhe Zhou, Minfeng Xing, Binbin He, Jinfei Wang, Yang Song, Jiali Shang, Chunhua Liao, Min Xu and Xiliang Ni

Drones 2023, 7(7), 406; https://doi.org/10.3390/drones7070406 - 21 Jun 2023

Cited by 6 | Viewed by 1704

Abstract

Height is a key factor in monitoring the growth status and rate of crops. Compared with large-scale satellite remote sensing images and high-cost LiDAR point cloud, the point cloud generated by the Structure from Motion (SfM) algorithm based on UAV images can quickly estimate crop height in the target area at a lower cost. However, crop leaves gradually start to cover the ground from the beginning of the stem elongation stage, making more and more ground points below the canopy disappear in the data. The terrain undulations and outliers will seriously affect the height estimation accuracy. This paper proposed a ground point fitting method to estimate the height of winter wheat based on the UAV SfM point cloud. A canopy slice filter was designed to reduce the interference of middle canopy points and outliers. Random Sample Consensus (RANSAC) was applied to obtain the ground points from the valid filtered point cloud. Then, the missing ground points were fitted according to the known ground points. Furthermore, we achieved crop height monitoring at the stem elongation stage with an R² of 0.90. The relative root mean squared error (RRMSE) of height estimation was 5.9%, and the relative mean absolute error (RMAE) was 4.6% at the stem elongation stage. This paper proposed the canopy slice filter and fitting missing ground points. It was concluded that the canopy slice filter successfully optimized the extraction of ground points and removed outliers. Fitting the missing ground points simulated the terrain undulations effectively and improved the accuracy. Full article

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

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19 pages, 9762 KiB

Open AccessArticle

Operational Mapping of Salinization Areas in Agricultural Fields Using Machine Learning Models Based on Low-Altitude Multispectral Images

by Ravil Mukhamediev, Yedilkhan Amirgaliyev, Yan Kuchin, Margulan Aubakirov, Alexei Terekhov, Timur Merembayev, Marina Yelis, Elena Zaitseva, Vitaly Levashenko, Yelena Popova, Adilkhan Symagulov and Laila Tabynbayeva

Drones 2023, 7(6), 357; https://doi.org/10.3390/drones7060357 - 29 May 2023

Cited by 7 | Viewed by 2101

Abstract

Salinization of cultivated soil is an important negative factor that reduces crop yields. Obtaining accurate and timely data on the salinity of soil horizons allows for planning the agrotechnical measures to reduce this negative impact. The method of soil salinity mapping of the 0–30 cm layer on irrigated arable land with the help of multispectral data received from the UAV is described in this article. The research was carried out in the south of the Almaty region of Kazakhstan. In May 2022, 80 soil samples were taken from the ground survey, and overflight of two adjacent fields was performed. The flight was carried out using a UAV equipped with a multispectral camera. The data preprocessing method is proposed herein, and several machine learning algorithms are compared (XGBoost, LightGBM, random forest, support vector machines, ridge regression, elastic net, etc.). Machine learning methods provided regression reconstruction to predict the electrical conductivity of the 0–30 cm soil layer based on an optimized list of spectral indices. The XGB regressor model showed the best quality results: the coefficient of determination was 0.701, the mean-squared error was 0.508, and the mean absolute error was 0.514. A comparison with the results obtained based on Landsat 8 data using a similar model was performed. Soil salinity mapping using UAVs provides much better spatial detailing than satellite data and has the possibility of an arbitrary selection of the survey time, less dependence on the conditions of cloud cover, and a comparable degree of accuracy of estimates. Full article

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

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20 pages, 13134 KiB

Open AccessArticle

Feasibility Study of Detection of Ochre Spot on Almonds Aimed at Very Low-Cost Cameras Onboard a Drone

by Juana M. Martínez-Heredia, Ana I. Gálvez, Francisco Colodro, José Luis Mora-Jiménez and Ons E. Sassi

Drones 2023, 7(3), 186; https://doi.org/10.3390/drones7030186 - 8 Mar 2023

Cited by 1 | Viewed by 2053

Abstract

Drones can be very helpful in precision agriculture. Currently, most drone-based solutions for plant disease detection incorporate multispectral, hyperspectral, or thermal cameras, which are expensive. In addition, there is a trend nowadays to apply machine learning techniques to precision agriculture, which are computationally complex and intensive. In this work, we explore the feasibility of detecting ochre spot disease in almond plantations based on conventional techniques of computer vision and images from a very low-cost RGB camera that is placed on board a drone. Such an approach will allow the detection system to be simple and inexpensive. First, we made a study of color on the ochre spot disease. Second, we developed a specific algorithm that was capable of processing and analyzing limited-quality images from a very low-cost camera. In addition, it can estimate the percentage of healthy and unhealthy parts of the plant. Thanks to the GPS on board the drone, the system can provide the location of every sick almond tree. Third, we checked the operation of the algorithm with a variety of photographs of ochre spot disease in almonds. The study demonstrates that the efficiency of the algorithm depends to a great extent on environmental conditions, but, despite the limitations, the results obtained with the analyzed photographs show a maximum discrepancy of 10% between the estimated percentage and the ground truth percentage of the unhealthy area. This approach shows great potential for extension to other crops by making previous studies of color and adaptations. Full article

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

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15 pages, 3707 KiB

Open AccessArticle

Sensitivity of LiDAR Parameters to Aboveground Biomass in Winter Spelt

by Carsten Montzka, Marco Donat, Rahul Raj, Philipp Welter and Jordan Steven Bates

Drones 2023, 7(2), 121; https://doi.org/10.3390/drones7020121 - 9 Feb 2023

Cited by 2 | Viewed by 2148

Abstract

Information about the current biomass state of crops is important to evaluate whether the growth conditions are adequate in terms of water and nutrient supply to determine if there is need to react to diseases and to predict the expected yield. Passive optical Unmanned Aerial Vehicle (UAV)-based sensors such as RGB or multispectral cameras are able to sense the canopy surface and record, e.g., chlorophyll-related plant characteristics, which are often indirectly correlated to aboveground biomass. However, direct measurements of the plant structure can be provided by LiDAR systems. In this study, different LiDAR-based parameters are evaluated according to their relationship to aboveground fresh and dry biomass (AGB) for a winter spelt experimental field in Dahmsdorf, Brandenburg, Germany. The parameters crop height, gap fraction, and LiDAR intensity are analyzed according to their individual correlation with AGB, and also a multiparameter analysis using the Ordinary Least Squares Regression (OLS) is performed. Results indicate high absolute correlations of AGB with gap fraction and crop height (−0.82 and 0.77 for wet and −0.70 and 0.66 for dry AGB, respectively), whereas intensity needs further calibration or processing before it can be adequately used to estimate AGB (−0.27 and 0.22 for wet and dry AGB, respectively). An important outcome of this study is that the combined utilization of all LiDAR parameters via an OLS analysis results in less accurate AGB estimation than with gap fraction or crop height alone. Moreover, future AGB states in June and July were able to be estimated from May LiDAR parameters with high accuracy, indicating stable spatial patterns in crop characteristics over time. Full article

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

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23 pages, 10870 KiB

Open AccessFeature PaperArticle

Automated Rice Phenology Stage Mapping Using UAV Images and Deep Learning

by Xiangyu Lu, Jun Zhou, Rui Yang, Zhiyan Yan, Yiyuan Lin, Jie Jiao and Fei Liu

Drones 2023, 7(2), 83; https://doi.org/10.3390/drones7020083 - 25 Jan 2023

Cited by 6 | Viewed by 3632

Abstract

Accurate monitoring of rice phenology is critical for crop management, cultivars breeding, and yield estimating. Previously, research for phenology detection relied on time-series data and orthomosaic and manually plotted regions, which are difficult to automate. This study presented a novel approach for extracting and mapping phenological traits directly from the unmanned aerial vehicle (UAV) photograph sequence. First, a multi-stage rice field segmentation dataset containing four growth stages and 2600 images, namely PaddySeg, was built. Moreover, an efficient Ghost Bilateral Network (GBiNet) was proposed to generate trait masks. To locate the trait of each pixel, we introduced direct geo-locating (DGL) and incremental sparse sampling (ISS) techniques to eliminate redundant computation. According to the results on PaddySeg, the proposed GBiNet with 91.50% mean-Intersection-over-Union (mIoU) and 41 frames-per-second (FPS) speed outperformed the baseline model (90.95%, 36 FPS), while the fastest GBiNet_t reached 62 FPS which was 1.7 times faster than the baseline model, BiSeNetV2. Additionally, the measured average DGL deviation was less than 1% of the relative height. Finally, the mapping of rice phenology was achieved by interpolation on trait value–location pairs. The proposed approach demonstrated great potential for automatic rice phenology stage surveying and mapping. Full article

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

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15 pages, 2408 KiB

Open AccessFeature PaperArticle

Comparison between Field Measured and UAV-Derived Pistachio Tree Crown Characteristics throughout a Growing Season

by Ewelina Jacygrad, Maggi Kelly, Sean Hogan, John E. Preece, Deborah Golino and Richard Michelmore

Drones 2022, 6(11), 343; https://doi.org/10.3390/drones6110343 - 4 Nov 2022

Cited by 3 | Viewed by 2900

Abstract

Monitoring individual tree crown characteristics is an important component of smart agriculture and is crucial for orchard management. We focused on understanding how UAV imagery taken across one growing season can help understand and predict the growth and development of pistachio trees grown from rootstock seedlings. Tree crown characteristics (i.e., height, size, shape, and mean normalized difference vegetation index (NDVI)) were derived using an object-based image analysis method with multispectral Uncrewed Aerial Vehicles (UAV) imagery flown seven times over 472 five-year-old pistachio trees in 2018. These imagery-derived metrics were compared with field-collected tree characteristics (tree height, trunk caliper, crown height, width and volume, and leaf development status) collected over two months in 2018. The UAV method captured seasonal development of tree crowns well. UAV-derived tree characteristics were better correlated with the field tree characteristics when recorded between May and November, with high overall correlations in November. The highest correlation (R² = 0.774) was found between trunk caliper and June UAV crown size. The weakest correlations between UAV and field traits were found in March and December. Spring leaf development stage was most variable, and mean NDVI values were lowest in March, when leaf development starts. Mean NDVI increased orchard-wide by May, and was consistently high through November. This study showcased the benefits of timely, detailed drone imagery for orchard managers. Full article

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

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19 pages, 4609 KiB

Open AccessEditor’s ChoiceArticle

A Framework for Soil Salinity Monitoring in Coastal Wetland Reclamation Areas Based on Combined Unmanned Aerial Vehicle (UAV) Data and Satellite Data

by Lijian Xie, Xiuli Feng, Chi Zhang, Yuyi Dong, Junjie Huang and Junkai Cheng

Drones 2022, 6(9), 257; https://doi.org/10.3390/drones6090257 - 16 Sep 2022

Cited by 13 | Viewed by 2836

Abstract

Soil salinization is one of the most important causes of land degradation and desertification, often threatening land management and sustainable agricultural development. Due to the low resolution of satellites, fine mapping of soil salinity cannot be completed, while high-resolution images from UAVs can only achieve accurate mapping of soil salinity in a small area. Therefore, how to realize fine mapping of salinity on a large scale based on UAV and satellite data is an urgent problem to be solved. Therefore, in this paper, the most relevant spectral variables for soil salinity were firstly determined using Pearson correlation analysis, and then the optimal inversion model was established based on the screened variables. Secondly, the feasibility of correcting satellite data based on UAV data was determined using Pearson correlation analysis and spectral variation trends, and the correction of satellite data was completed using least squares-based polynomial curve fitting for both UAV data and satellite data. Finally, the reflectance received from the vegetated area did not directly reflect the surface reflectance condition, so we used the support vector machine classification method to divide the study area into two categories: bare land and vegetated area, and built a model based on the classification results to realize the advantages of complementing the accurate spectral information of UAV and large-scale satellite spectral data in the study areas. By comparing the modeling inversion results using only satellite data with the inversion results based on optimized satellite data, our method framework could effectively improve the accuracy of soil salinity inversion in large satellite areas by 6–19%. Our method can meet the needs of large-scale accurate mapping, and can provide the necessary means and reference for soil condition monitoring. Full article

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

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17 pages, 5771 KiB

Open AccessFeature PaperArticle

Developing Novel Rice Yield Index Using UAV Remote Sensing Imagery Fusion Technology

by Jun Zhou, Xiangyu Lu, Rui Yang, Huizhe Chen, Yaliang Wang, Yuping Zhang, Jing Huang and Fei Liu

Drones 2022, 6(6), 151; https://doi.org/10.3390/drones6060151 - 17 Jun 2022

Cited by 13 | Viewed by 3355

Abstract

Efficient and quick yield prediction is of great significance for ensuring world food security and crop breeding research. The rapid development of unmanned aerial vehicle (UAV) technology makes it more timely and accurate to monitor crops by remote sensing. The objective of this study was to explore the method of developing a novel yield index (YI) with wide adaptability for yield prediction by fusing vegetation indices (VIs), color indices (CIs), and texture indices (TIs) from UAV-based imagery. Six field experiments with 24 varieties of rice and 21 fertilization methods were carried out in three experimental stations in 2019 and 2020. The multispectral and RGB images of the rice canopy collected by the UAV platform were used to rebuild six new VIs and TIs. The performance of VI-based YI (MAPE = 13.98%) developed by quadratic nonlinear regression at the maturity stage was better than other stages, and outperformed that of CI-based (MAPE = 22.21%) and TI-based (MAPE = 18.60%). Then six VIs, six CIs, and six TIs were fused to build YI by multiple linear regression and random forest models. Compared with heading stage (R² = 0.78, MAPE = 9.72%) and all stage (R² = 0.59, MAPE = 22.21%), the best performance of YI was developed by random forest with fusing VIs + CIs + TIs at maturity stage (R² = 0.84, MAPE = 7.86%). Our findings suggest that the novel YI proposed in this study has great potential in crop yield monitoring. Full article

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

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