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Precision Agriculture in Crop Production
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Precision Agriculture in Crop Production

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

Deadline for manuscript submissions: 31 July 2025 | Viewed by 7091

Special Issue Editor

Dr. Vasileios Liakos

E-Mail Website
Guest Editor
Laboratory of Precision Agriculture, Department of Agrotechnology, University of Thessaly, Gaiopolis, 41110 Larissa, Greece
Interests: precision agriculture; agronomy; wireless sensor networks; smart irrigation; GIS; sensors; management zones

Special Issue Information

Dear Colleagues,

Precision Agriculture (PA) is a modern strategy of farming management. PA is also referred to as Precision Farming or Smart Agriculture because it uses information technology to measure inter- and intra-field variability with the aim of obtaining better quality products, sustainable profitability and higher production efficiency with minimum environmental impact. PA combines several technological advances such as remote sensing, sensors, satellite technology, variable rate application machinery, Internet of Things, geostatistics and Geographical Information Systems. The efficiency of applications of inputs such as irrigation, fertilizer and other agrochemicals, and the levels of sustainability as well as farmers’ profit, depend on applying inputs in the correct place and at the right time.

This Special Issue intends to cover the state of the art and recent progress in different aspects related to the real implementation of Precision Agriculture in a wide range of cropping systems (grain crops, grassland, horticultural crops, fruit trees and aromatic/medicinal crops). All types of manuscripts (original research and reviews) providing new insights in the application and benefits of Precision Agriculture methods and technology are welcome. Articles may include, but are not limited to, the following topics: 

  • Proximal and remote sensing of soils and crops;
  • Internet of Things, Wireless sensor networks;
  • Big data analysis for PA purposes;
  • Sampling, mapping and geostatistical analysis;
  • Precision Irrigation;
  • Precision crop protection;
  • Delineation of management zones;
  • Ag-engineering, robotics and Unmanned Aerial Vehicles (UAVs);
  • Crop models and decision support tools;
  • Augmented reality in PA.

Dr. Vasileios Liakos
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. 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

  • spatial–temporal analysis
  • remote sensing
  • proximal sensing
  • variable rate technology
  • precision farming
  • smart agriculture

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Published Papers (6 papers)

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Research

28 pages, 6110 KiB  
Article
MAF-MixNet: Few-Shot Tea Disease Detection Based on Mixed Attention and Multi-Path Feature Fusion
by Wenjing Zhang, Ke Tan, Han Wang, Di Hu and Haibo Pu
Plants 2025, 14(8), 1259; https://doi.org/10.3390/plants14081259 - 21 Apr 2025
Viewed by 302
Abstract
Tea (Camellia sinensis L.) disease detection in complex field conditions faces significant challenges due to the scarcity of labeled data. While current mainstream visual deep learning algorithms depend on large-scale curated datasets. To address this, we propose a novel few-shot end-to-end detection [...] Read more.
Tea (Camellia sinensis L.) disease detection in complex field conditions faces significant challenges due to the scarcity of labeled data. While current mainstream visual deep learning algorithms depend on large-scale curated datasets. To address this, we propose a novel few-shot end-to-end detection network called MAF-MixNet that achieves robust detection with minimal annotation data. The network effectively overcomes the bottleneck of insufficient feature extraction under limited samples of existing methods, through the design of a mixed attention branch (MA-Branch) and a multi-path feature fusion module (MAFM). The former extracts contextual features, while the latter combines and enhances the local and global features. The entire model uses a two-stage paradigm to pretrain on public datasets and fine-tune on balanced subset datasets, including novel tea disease classes, anthracnose, and brown blight. Comparative experiments with six models on four evaluation metrics verified the advancement of our model. At 5-shot, MAF-MixNet achieves scores of 62.0%, 60.1%, and 65.9% in precision, nAP50, and F1 score, respectively, significantly outperforming other models. Similar superiority is achieved in the 10-shot scenario, where nAP50 is 73.8%. Our model maintains a certain computational efficiency and achieves the second fastest inference speed at 11.63 FPS, making it viable for real-world deployment. The results confirm MAF-MixNet’s potential to enable cost-effective, intelligent disease monitoring in precision agriculture. Full article
(This article belongs to the Special Issue Precision Agriculture in Crop Production)
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19 pages, 2949 KiB  
Article
Precision Estimation of Rice Nitrogen Fertilizer Topdressing According to the Nitrogen Nutrition Index Using UAV Multi-Spectral Remote Sensing: A Case Study in Southwest China
by Lijuan Wang, Qihan Ling, Zhan Liu, Mingzhu Dai, Yu Zhou, Xiaojun Shi and Jie Wang
Plants 2025, 14(8), 1195; https://doi.org/10.3390/plants14081195 - 11 Apr 2025
Viewed by 329
Abstract
The precision estimation of N fertilizer application according to the nitrogen nutrition index (NNI) using unmanned aerial vehicle (UAV) multi-spectral measurements remains to be tested in different rice cultivars and planting areas. Therefore, two field experiments were conducted using varied N rates (0, [...] Read more.
The precision estimation of N fertilizer application according to the nitrogen nutrition index (NNI) using unmanned aerial vehicle (UAV) multi-spectral measurements remains to be tested in different rice cultivars and planting areas. Therefore, two field experiments were conducted using varied N rates (0, 60, 120, 160, and 200 kg N ha−1) on two rice cultivars, Yunjing37 (YJ-37, Oryza sativa subsp. Japonica Kato., the Institute of Food Crops at the Yunnan Academy of Agricultural Sciences, Kunming, China) and Jiyou6135 (JY-6135, Oryza sativa subsp. indica Kato., Hunan Longping Gaoke Nongping seed industry Co., Ltd., Changsha, China), in southwest China. The rice canopy spectral images were measured by the UAV’s multi-spectral remote sensing at three growing stages. The NNI was calculated based on the critical N (Nc) dilution curve. A random forest model integrating multi-vegetation indices established the NNI inversion, facilitating precise N topdressing through a linear platform of NNI-Relative Yield and the remote sensing NNI-based N balance approaches. The Nc dilution curve calibrated with aboveground dry matter demonstrated the highest accuracy (R2 = 0.93, 0.97 for shoot components in cultivars YJ-37 and JY-6135), outperforming stem (R2 = 0.70, 0.76) and leaf (R2 = 0.80, 0.89) based models. The RF combined with six vegetation index combinations was found to be the best predictor of NNI at each growing period (YJ-37: R2 is 0.70–0.97, RMSE is 0.02~0.04; JY-6135: R2 is 0.71–0.92, RMSE is 0.04~0.05). The RF surpassed BPNN/PLSR by 6.14–10.10% in R2 and 13.71–33.65% in error reduction across the critical rice growth stages. The topdressing amounts of YJ-37 and JY-6135 were 111–124 kg ha−1 and 80–133 kg ha−1, with low errors of 2.50~8.73 kg ha−1 for YJ-37 and 2.52~5.53 kg ha−1 for JY-6135 in the jointing (JT) and heading (HD) stages. These results are promising for the precise topdressing of rice using a remote sensing NNI-based N balance method. The combination of UAV multi-spectral imaging with the NNI-nitrogen balance method was tested for the first time in southwest China, demonstrating its feasibility and offering a regional approach for precise rice topdressing. Full article
(This article belongs to the Special Issue Precision Agriculture in Crop Production)
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22 pages, 9128 KiB  
Article
Deposition Characteristics of Air-Assisted Sprayer Based on Canopy Volume and Leaf Area of Orchard Trees
by Chenchen Gu, Jiahui Sun, Si Li, Shuo Yang, Wei Zou and Changyuan Zhai
Plants 2025, 14(2), 220; https://doi.org/10.3390/plants14020220 - 14 Jan 2025
Viewed by 683
Abstract
Precision pesticide application mainly relies on canopy volume, resulting in varied application effectiveness across different density areas of orchard trees. This study examined pesticide application effectiveness based on the spray wind, canopy volume, and leaf area within the canopy, providing variable bases for [...] Read more.
Precision pesticide application mainly relies on canopy volume, resulting in varied application effectiveness across different density areas of orchard trees. This study examined pesticide application effectiveness based on the spray wind, canopy volume, and leaf area within the canopy, providing variable bases for precise regulation of spray wind and pesticide dosage. The study addresses the knowledge gap by utilizing laser detection and ranging (LiDAR) to measure the thickness and leaf area of orchard tree canopies. The spray experiments were conducted on canopies of different regions, using an air-assisted sprayer with varying fan speeds of 1381 r/min, 1502 r/min, and 1676 r/min. The deposition effects were analyzed using water-sensitive papers. The inlet air speed within the canopy did not increase proportionally when the spray fan speed increased, and it showed a significant variation in locations with sparse foliage. Furthermore, droplets exhibited abnormal median volume diameters of the canopy regions with lower wind loss rates and smaller leaf areas. The influences were in the order of canopy thickness, leaf area, and inlet air speed on the cumulative deposition of droplets on both sides of the water-sensitive papers, as well as the ratio of deposition between the two sides, from big to small, are inlet air speed, leaf area, and canopy thickness. The study provides a scientific foundation for air control in precision pesticide application in apple orchards and contributes to the rapid development of precision spraying technologies. Full article
(This article belongs to the Special Issue Precision Agriculture in Crop Production)
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13 pages, 5892 KiB  
Article
Detecting Sensitive Spectral Bands and Vegetation Indices for Potato Yield Using Handheld Spectroradiometer Data
by Diego Gomez, Pablo Salvador, Juan Fernando Rodrigo and Jorge Gil
Plants 2024, 13(23), 3436; https://doi.org/10.3390/plants13233436 - 7 Dec 2024
Viewed by 1264
Abstract
Remote sensing is a valuable tool in precision agriculture due to its spatial and temporal coverage, non-destructive method of data collection, and cost-effectiveness. In this study, we measured the canopy reflectance of potato (Solanum tuberosum L.) crops on a plant-by-plant basis with [...] Read more.
Remote sensing is a valuable tool in precision agriculture due to its spatial and temporal coverage, non-destructive method of data collection, and cost-effectiveness. In this study, we measured the canopy reflectance of potato (Solanum tuberosum L.) crops on a plant-by-plant basis with a handheld spectrometer instrument. Our study pursues two primary objectives: (1) determining the optimal temporal aggregation for measuring canopy signals related to potato yield and (2) identifying the best spectral bands in the 350–2500 nm domain and vegetation indices. The study was conducted over two consecutive years (2020 and 2021) with 60 plants per plot, encompassing six potato varieties and three replicates annually throughout the growth season. Employing correlation analysis and dimensionality reduction, we identified 23 independent features significantly correlated with tuber yield. We used multiple linear regression analysis to model the relationship between the selected features and yield and to compare their influence in the fitted model. We used the Leave-One-Out Cross-Validation (LOOCV) method to assess the validity of the model (RMSE = 702 g and %RMSE = 29.2%). The most significant features included the Gitelson2 and Vogelmann indices. The optimal time period for measurements was determined to be from 56 to 100 days after planting. These findings may contribute to the advancement of precision farming by proposing tailored sensor applications, paving the way for improved agricultural practices and enhanced food security. Full article
(This article belongs to the Special Issue Precision Agriculture in Crop Production)
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20 pages, 3618 KiB  
Article
Rapeseed Flower Counting Method Based on GhP2-YOLO and StrongSORT Algorithm
by Nan Wang, Haijuan Cao, Xia Huang and Mingquan Ding
Plants 2024, 13(17), 2388; https://doi.org/10.3390/plants13172388 - 27 Aug 2024
Cited by 8 | Viewed by 1893
Abstract
Accurately quantifying flora and their respective anatomical structures within natural ecosystems is paramount for both botanical breeders and agricultural cultivators. For breeders, precise plant enumeration during the flowering phase is instrumental in discriminating genotypes exhibiting heightened flowering frequencies, while for growers, such data [...] Read more.
Accurately quantifying flora and their respective anatomical structures within natural ecosystems is paramount for both botanical breeders and agricultural cultivators. For breeders, precise plant enumeration during the flowering phase is instrumental in discriminating genotypes exhibiting heightened flowering frequencies, while for growers, such data inform potential crop rotation strategies. Moreover, the quantification of specific plant components, such as flowers, can offer prognostic insights into the potential yield variances among different genotypes, thereby facilitating informed decisions pertaining to production levels. The overarching aim of the present investigation is to explore the capabilities of a neural network termed GhP2-YOLO, predicated on advanced deep learning techniques and multi-target tracking algorithms, specifically tailored for the enumeration of rapeseed flower buds and blossoms from recorded video frames. Building upon the foundation of the renowned object detection model YOLO v8, this network integrates a specialized P2 detection head and the Ghost module to augment the model’s capacity for detecting diminutive targets with lower resolutions. This modification not only renders the model more adept at target identification but also renders it more lightweight and less computationally intensive. The optimal iteration of GhP2-YOLOm demonstrated exceptional accuracy in quantifying rapeseed flower samples, showcasing an impressive mean average precision at 50% intersection over union metric surpassing 95%. Leveraging the virtues of StrongSORT, the subsequent tracking of rapeseed flower buds and blossom patterns within the video dataset was adeptly realized. By selecting 20 video segments for comparative analysis between manual and automated counts of rapeseed flowers, buds, and the overall target count, a robust correlation was evidenced, with R-squared coefficients measuring 0.9719, 0.986, and 0.9753, respectively. Conclusively, a user-friendly “Rapeseed flower detection” system was developed utilizing a GUI and PyQt5 interface, facilitating the visualization of rapeseed flowers and buds. This system holds promising utility in field surveillance apparatus, enabling agriculturalists to monitor the developmental progress of rapeseed flowers in real time. This innovative study introduces automated tracking and tallying methodologies within video footage, positioning deep convolutional neural networks and multi-target tracking protocols as invaluable assets in the realms of botanical research and agricultural administration. Full article
(This article belongs to the Special Issue Precision Agriculture in Crop Production)
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14 pages, 7797 KiB  
Article
LSD-YOLO: Enhanced YOLOv8n Algorithm for Efficient Detection of Lemon Surface Diseases
by Shuyang Wang, Qianjun Li, Tao Yang, Zhenghao Li, Dan Bai, Chenwei Tang and Haibo Pu
Plants 2024, 13(15), 2069; https://doi.org/10.3390/plants13152069 - 26 Jul 2024
Cited by 5 | Viewed by 1783
Abstract
Lemon, as an important cash crop with rich nutritional value, holds significant cultivation importance and market demand worldwide. However, lemon diseases seriously impact the quality and yield of lemons, necessitating their early detection for effective control. This paper addresses this need by collecting [...] Read more.
Lemon, as an important cash crop with rich nutritional value, holds significant cultivation importance and market demand worldwide. However, lemon diseases seriously impact the quality and yield of lemons, necessitating their early detection for effective control. This paper addresses this need by collecting a dataset of lemon diseases, consisting of 726 images captured under varying light levels, growth stages, shooting distances and disease conditions. Through cropping high-resolution images, the dataset is expanded to 2022 images, comprising 4441 healthy lemons and 718 diseased lemons, with approximately 1–6 targets per image. Then, we propose a novel model lemon surface disease YOLO (LSD-YOLO), which integrates Switchable Atrous Convolution (SAConv) and Convolutional Block Attention Module (CBAM), along with the design of C2f-SAC and the addition of a small-target detection layer to enhance the extraction of key features and the fusion of features at different scales. The experimental results demonstrate that the proposed LSD-YOLO achieves an accuracy of 90.62% on the collected datasets, with mAP@50–95 reaching 80.84%. Compared with the original YOLOv8n model, both mAP@50 and mAP@50–95 metrics are enhanced. Therefore, the LSD-YOLO model proposed in this study provides a more accurate recognition of healthy and diseased lemons, contributing effectively to solving the lemon disease detection problem. Full article
(This article belongs to the Special Issue Precision Agriculture in Crop Production)
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