Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
6.5
4.0
Journals
Plants
Special Issues
Precision Agriculture in Crop Production
share announcement

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 December 2024 | Viewed by 1356

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (2 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

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
Viewed by 406
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)
►▼ Show Figures

Figure 1

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
Viewed by 551
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-2
Back to TopTop