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Advances in Precision Agricultural Aviation

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A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1119

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Editors

Dr. Weixiang Yao

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Guest Editor

College of Information and Electronic Engineering, Shenyang Agricultural University, Shenyang 110866, China
Interests: agricultural aerial applications; droplet deposition; drift control; plant protection; UAV fertilizer application and seeding; image processing; UAV payload transport

Dr. Weiguang Yang

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Guest Editor

College of Artificial Intelligence and Low-Altitude Technology, South China Agricultural University, Guangzhou 510642, China
Interests: UAV-based crop phenotyping; multi-sensor data fusion; hyperspectral and LiDAR integration; AI-driven inversion modeling; precision nitrogen management

Dr. Shuang Guo

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Guest Editor

School of Intelligent Science and Information Engineering, Shenyang University, Shenyang 110044, China
Interests: agricultural UAVs; aerial spraying simulation; mountain orchard application; variable rate spraying; droplet detection; agricultural informatization; UAV performance evaluation

Special Issue Information

Dear Colleagues,

Precision agricultural aviation technology, a cornerstone of smart agriculture, is reshaping crop field management practices at an unprecedented pace. Driven by the miniaturization of airborne sensors, the maturation of artificial intelligence algorithms, and the continuous refinement of aviation regulatory frameworks, precision agricultural aviation has evolved beyond a supplementary tool for field operations to become essential infrastructure within modern plant production systems.

This Special Issue aims to showcase the latest research findings and applied innovations in the field of precision agricultural aviation technology. The scope of submissions encompasses, but is not limited to, the following: crop health monitoring; precision pest and disease control; variable-rate spraying, fertilization, and seeding operations using unmanned aerial systems (UAS) and various manned aerial platforms; methods for acquiring, processing, and analyzing aerial remote sensing data; multi-sensor fusion technologies (including hyperspectral imaging, thermal imaging, and LiDAR); and AI-driven image processing and decision-support algorithms.

The Special Issue particularly encourages research focusing on the practical implementation of aerial technologies in real-world plant growth processes, alongside interdisciplinary studies capable of quantitatively assessing the impact of aerial operations on crop yield, quality, and ecological environments.

Dr. Weixiang Yao
Dr. Weiguang Yang
Dr. Shuang Guo
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Plants 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 2700 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

precision agricultural aviation
agricultural UAV
remote sensing
aerial spraying
operation effectiveness evaluation
plant protection
sensor fusion

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Published Papers (2 papers)

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Research

32 pages, 8953 KB

Open AccessArticle

Soybean Field Weed Segmentation and Prescription Map Generation Based on SCG-UNet Fusion of UAV RGB and Multispectral Images

by He Li, Qianyi Wang, Zishang Yang, Xiuyuan Zhang, Qiming Ding and Lele Wang

Plants 2026, 15(15), 2257; https://doi.org/10.3390/plants15152257 - 23 Jul 2026

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Abstract

Weed segmentation in soybean fields is essential to improving herbicide use efficiency and supporting precision variable-rate spraying. This study developed an SiLU–CPCA–Gate U-Net (SCG-UNet) using fused UAV RGB and multispectral imagery to improve the delineation of small and partially occluded weeds under complex canopy conditions. SCG-UNet integrates channel–spatial feature enhancement, attention-guided skip-feature fusion, and smooth nonlinear activation within a U-Net framework. A total of 400 spatially aligned RGB–multispectral image groups collected from a soybean field in Henan Province, China, were manually annotated for model development and evaluation. Paired bootstrap comparisons showed that RGB+NIR achieved the highest numerical performance among the tested inputs and significantly outperformed RGB, RGB+R, and RGB+G in mIoU after Holm correction, while remaining statistically comparable to RGB+REdge and RGB+NIR+REdge. With RGB+NIR input, SCG-UNet achieved an mPA of 92.35%, an mIoU of 83.43%, a Dice coefficient of 79.50%, and an F1-score of 80.77%, exceeding the baseline U-Net by 0.71, 1.50, 2.19, and 2.09 percentage points, respectively. Five-fold spatial block cross-validation yielded an mIoU of 82.92 ± 0.29% and an F1-score of 80.06 ± 0.40%, indicating stable performance across different regions of the same field. SCG-UNet also achieved the highest numerical mIoU among the evaluated convolutional, high-resolution, and Transformer-based models, exceeding TransUNet and LeViT-UNet by 0.90 and 0.71 percentage points, respectively, while requiring fewer parameters and lower reported memory consumption. The segmentation results were further converted into a conceptual variable-rate spraying prescription map with five spray volume levels ranging from 220 to 300 L/ha. These results demonstrate the potential of RGB–multispectral fusion for soybean weed mapping, although field validation of prescription execution, weed control efficacy, and economic benefits remains necessary. Full article

(This article belongs to the Special Issue Advances in Precision Agricultural Aviation)

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20 pages, 20038 KB

Open AccessArticle

Net Primary Productivity Retrieval Based on ESTARFM Fusion and an Improved CASA Model

by Yuanji Cai, Chunling Chen, Wanning Li, Hao Han, Zhichao Ren, Zihao Wang and Ziyi Feng

Plants 2026, 15(10), 1436; https://doi.org/10.3390/plants15101436 - 8 May 2026

Viewed by 475

Abstract

Net primary productivity (NPP) is an important indicator of ecosystem carbon accumulation capacity and vegetation productivity potential, and its accurate estimation is of great significance for agricultural management and regional carbon cycle research. To address the problem that the temporal continuity of single-source optical remote sensing data is easily affected by cloud cover, this study used Sentinel-2 imagery and the Moderate Resolution Imaging Spectroradiometer (MODIS) Normalized Difference Vegetation Index (NDVI) product as data sources and constructed an NDVI time series with high spatial and temporal resolution for the study area based on the Enhanced Spatial and Temporal Adaptive Reflectance Fusion Model (ESTARFM) method. On this basis, the Simple Ratio (SR) index was incorporated to supplement canopy information, and the key parameters of the Carnegie–Ames–Stanford Approach (CASA) model were differentially optimized for different crop types, thereby enabling remote sensing-based estimation of crop NPP. The results showed that the fused NDVI effectively compensated for observation gaps caused by cloud interference, and its temporal variation was generally consistent with the crop growth process. In addition, the Fraction of Photosynthetically Active Radiation (FPAR) improved with the fused NDVI, which effectively characterized phenological differences among crops. Compared with the unoptimized model, the improved model significantly improved NPP estimation accuracy for both maize and rice. Specifically, for maize, the coefficient of determination (

R^{2}

) increased from 0.75 to 0.88, and the mean absolute percentage error (MAPE) decreased from 67.00% to 34.68%. For rice, the MAPE decreased from 78.51% to 23.43%, while the mean absolute error (MAE) decreased from 345.1

gC \cdot m^{- 2} \cdot a^{- 1}

to 95.6

gC \cdot m^{- 2} \cdot a^{- 1}

. These results indicate that constructing a highly continuous vegetation index time series through spatiotemporal fusion, together with optimizing the CASA model by incorporating the SR index and crop-specific parameterization, can effectively improve the stability and accuracy of NPP estimation for agricultural crops. Full article