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Intelligent Sensing and Machine Vision in Precision Agriculture: 2nd Edition
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Intelligent Sensing and Machine Vision in Precision Agriculture: 2nd Edition

A special issue of Sensors (ISSN 1424-8220). This special issue belongs to the section "Smart Agriculture".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 2566

Special Issue Editors

Dr. Lu Liu

E-Mail Website
Guest Editor
College of Engineering, Anhui Agricultural University, Hefei 230036, China
Interests: smart agriculture; intelligent agricultural equipment
Special Issues, Collections and Topics in MDPI journals
Dr. Jianjun Yin

E-Mail Website
Guest Editor
School of Agricultural Engineering, Jiangsu University, Zhenjiang 212013, China
Interests: intelligent agriculture machinery; agriculture robot
Dr. Haiyong Weng

E-Mail Website
Guest Editor
College of Mechanical and Electrical Engineering, Fujian Agriculture and Forestry University, Fuzhou 350002, China
Interests: machine vision; precision agriculture
Dr. Yuwei Wang

E-Mail Website
Guest Editor
College of Engineering, Anhui Agricultural University, Hefei 230036, China
Interests: machine vision; optical measurement; smart agriculture
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear colleagues,

Precision agriculture seeks to employ information technology to support farming operation and management, such as fertilizer inputs, irrigation management, pesticide application, etc. The temporal, spatial, and individual information related to environmental parameters and crop features are gathered, processed, and analyzed through various intelligent sensing technologies. Among them, machine vision technologies, including 3D/2D imaging, visible/near-infrared imaging, and hyperspectral/multispectral imaging, have been extensively used for precision agriculture, such as plant phenotyping, autonomous navigation, disease detection, production prediction, etc. Moreover, deep learning has greatly promoted the development of intelligent sensing technologies, which have a range of potential applications in precision agriculture.

Dr. Lu Liu
Dr. Jianjun Yin
Dr. Haiyong Weng
Dr. Yuwei Wang
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 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. Sensors 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 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

  • precision agriculture
  • agricultural robot
  • machine vision
  • image processing
  • multispectral imaging
  • plant phenotyping
  • optical measurement
  • disease detection
  • deep learning
  • artificial intelligence

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Related Special Issue

Published Papers (4 papers)

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Research

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27 pages, 8828 KiB  
Article
Research on Detection Method of Chaotian Pepper in Complex Field Environments Based on YOLOv8
by Yichu Duan, Jianing Li and Chi Zou
Sensors 2024, 24(17), 5632; https://doi.org/10.3390/s24175632 - 30 Aug 2024
Viewed by 427
Abstract
The intelligent detection of chili peppers is crucial for achieving automated operations. In complex field environments, challenges such as overlapping plants, branch occlusions, and uneven lighting make detection difficult. This study conducted comparative experiments to select the optimal detection model based on YOLOv8 [...] Read more.
The intelligent detection of chili peppers is crucial for achieving automated operations. In complex field environments, challenges such as overlapping plants, branch occlusions, and uneven lighting make detection difficult. This study conducted comparative experiments to select the optimal detection model based on YOLOv8 and further enhanced it. The model was optimized by incorporating BiFPN, LSKNet, and FasterNet modules, followed by the addition of attention and lightweight modules such as EMBC, EMSCP, DAttention, MSBlock, and Faster. Adjustments to CIoU, Inner CIoU, Inner GIoU, and inner_mpdiou loss functions and scaling factors further improved overall performance. After optimization, the YOLOv8 model achieved precision, recall, and mAP scores of 79.0%, 75.3%, and 83.2%, respectively, representing increases of 1.1, 4.3, and 1.6 percentage points over the base model. Additionally, GFLOPs were reduced by 13.6%, the model size decreased to 66.7% of the base model, and the FPS reached 301.4. This resulted in accurate and rapid detection of chili peppers in complex field environments, providing data support and experimental references for the development of intelligent picking equipment. Full article
(This article belongs to the Special Issue Intelligent Sensing and Machine Vision in Precision Agriculture: 2nd Edition)
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19 pages, 3177 KiB  
Article
Developing Machine Vision in Tree-Fruit Applications—Fruit Count, Fruit Size and Branch Avoidance in Automated Harvesting
by Chiranjivi Neupane, Kerry B. Walsh, Rafael Goulart and Anand Koirala
Sensors 2024, 24(17), 5593; https://doi.org/10.3390/s24175593 - 29 Aug 2024
Viewed by 481
Abstract
Recent developments in affordable depth imaging hardware and the use of 2D Convolutional Neural Networks (CNN) in object detection and segmentation have accelerated the adoption of machine vision in a range of applications, with mainstream models often out-performing previous application-specific architectures. The need [...] Read more.
Recent developments in affordable depth imaging hardware and the use of 2D Convolutional Neural Networks (CNN) in object detection and segmentation have accelerated the adoption of machine vision in a range of applications, with mainstream models often out-performing previous application-specific architectures. The need for the release of training and test datasets with any work reporting model development is emphasized to enable the re-evaluation of published work. An additional reporting need is the documentation of the performance of the re-training of a given model, quantifying the impact of stochastic processes in training. Three mango orchard applications were considered: the (i) fruit count, (ii) fruit size and (iii) branch avoidance in automated harvesting. All training and test datasets used in this work are available publicly. The mAP ‘coefficient of variation’ (Standard Deviation, SD, divided by mean of predictions using models of repeated trainings × 100) was approximately 0.2% for the fruit detection model and 1 and 2% for the fruit and branch segmentation models, respectively. A YOLOv8m model achieved a mAP50 of 99.3%, outperforming the previous benchmark, the purpose-designed ‘MangoYOLO’, for the application of the real-time detection of mango fruit on images of tree canopies using an edge computing device as a viable use case. YOLOv8 and v9 models outperformed the benchmark MaskR-CNN model in terms of their accuracy and inference time, achieving up to a 98.8% mAP50 on fruit predictions and 66.2% on branches in a leafy canopy. For fruit sizing, the accuracy of YOLOv8m-seg was like that achieved using Mask R-CNN, but the inference time was much shorter, again an enabler for the field adoption of this technology. A branch avoidance algorithm was proposed, where the implementation of this algorithm in real-time on an edge computing device was enabled by the short inference time of a YOLOv8-seg model for branches and fruit. This capability contributes to the development of automated fruit harvesting. Full article
(This article belongs to the Special Issue Intelligent Sensing and Machine Vision in Precision Agriculture: 2nd Edition)
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16 pages, 3229 KiB  
Article
Streamlining YOLOv7 for Rapid and Accurate Detection of Rapeseed Varieties on Embedded Device
by Siqi Gu, Wei Meng and Guodong Sun
Sensors 2024, 24(17), 5585; https://doi.org/10.3390/s24175585 - 28 Aug 2024
Viewed by 399
Abstract
Real-time seed detection on resource-constrained embedded devices is essential for the agriculture industry and crop yield. However, traditional seed variety detection methods either suffer from low accuracy or cannot directly run on embedded devices with desirable real-time performance. In this paper, we focus [...] Read more.
Real-time seed detection on resource-constrained embedded devices is essential for the agriculture industry and crop yield. However, traditional seed variety detection methods either suffer from low accuracy or cannot directly run on embedded devices with desirable real-time performance. In this paper, we focus on the detection of rapeseed varieties and design a dual-dimensional (spatial and channel) pruning method to lighten the YOLOv7 (a popular object detection model based on deep learning). We design experiments to prove the effectiveness of the spatial dimension pruning strategy. And after evaluating three different channel pruning methods, we select the custom ratio layer-by-layer pruning, which offers the best performance for the model. The results show that using custom ratio layer-by-layer pruning can achieve the best model performance. Compared to the YOLOv7 model, this approach results in mAP increasing from 96.68% to 96.89%, the number of parameters reducing from 36.5 M to 9.19 M, and the inference time per image on the Raspberry Pi 4B reducing from 4.48 s to 1.18 s. Overall, our model is suitable for deployment on embedded devices and can perform real-time detection tasks accurately and efficiently in various application scenarios. Full article
(This article belongs to the Special Issue Intelligent Sensing and Machine Vision in Precision Agriculture: 2nd Edition)
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Review

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42 pages, 13582 KiB  
Review
A Comprehensive Review of LiDAR Applications in Crop Management for Precision Agriculture
by Sheikh Muhammad Farhan, Jianjun Yin, Zhijian Chen and Muhammad Sohail Memon
Sensors 2024, 24(16), 5409; https://doi.org/10.3390/s24165409 - 21 Aug 2024
Viewed by 933
Abstract
Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. [...] Read more.
Precision agriculture has revolutionized crop management and agricultural production, with LiDAR technology attracting significant interest among various technological advancements. This extensive review examines the various applications of LiDAR in precision agriculture, with a particular emphasis on its function in crop cultivation and harvests. The introduction provides an overview of precision agriculture, highlighting the need for effective agricultural management and the growing significance of LiDAR technology. The prospective advantages of LiDAR for increasing productivity, optimizing resource utilization, managing crop diseases and pesticides, and reducing environmental impact are discussed. The introduction comprehensively covers LiDAR technology in precision agriculture, detailing airborne, terrestrial, and mobile systems along with their specialized applications in the field. After that, the paper reviews the several uses of LiDAR in agricultural cultivation, including crop growth and yield estimate, disease detection, weed control, and plant health evaluation. The use of LiDAR for soil analysis and management, including soil mapping and categorization and the measurement of moisture content and nutrient levels, is reviewed. Additionally, the article examines how LiDAR is used for harvesting crops, including its use in autonomous harvesting systems, post-harvest quality evaluation, and the prediction of crop maturity and yield. Future perspectives, emergent trends, and innovative developments in LiDAR technology for precision agriculture are discussed, along with the critical challenges and research gaps that must be filled. The review concludes by emphasizing potential solutions and future directions for maximizing LiDAR’s potential in precision agriculture. This in-depth review of the uses of LiDAR gives helpful insights for academics, practitioners, and stakeholders interested in using this technology for effective and environmentally friendly crop management, which will eventually contribute to the development of precision agricultural methods. Full article
(This article belongs to the Special Issue Intelligent Sensing and Machine Vision in Precision Agriculture: 2nd Edition)
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