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Advanced Image Collection, Processing, and Analysis in Crop and Livestock...
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Advanced Image Collection, Processing, and Analysis in Crop and Livestock Management

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Digital Agriculture".

Deadline for manuscript submissions: 15 November 2024 | Viewed by 2609

Special Issue Editors

Dr. Weizhen Liang

E-Mail Website
Guest Editor
Panhandle Research and Extension Center, University of Nebraska-Lincoln, Scottsbluff, NE, USA
Interests: application of image analysis in crop and livestock management; advanced crop and livestock modeling; Internet of Things (IoT); precision agriculture
Dr. Jingqiu Chen

E-Mail Website
Guest Editor
Biological Systems Engineering, College of Agriculture and Food Sciences, Florida A&M University, Tallahassee, FL, USA
Interests: agricultural engineering; digital agriculture; large-scale hydrologic and water quality modeling; remote sensing applications; environmental system optimization with a focus on green infrastructure and urban sustainability

Special Issue Information

Dear Colleagues,

The integration of imaging technology in agriculture has evolved significantly from basic field photography to sophisticated data collection systems. This transformation, driven by advances in computing and image processing, now empowers more precise agricultural practices through Artificial Intelligence (AI). Today, these technologies are essential for advancing crop and livestock management.

This Special Issue, titled "Advanced Image Collection, Processing, and Analysis in Crop and Livestock Management", underscores the value of image-based data in agriculture, providing insights into crop health and livestock management. Furthermore, the integration of AI, particularly through deep learning, is transforming agricultural practices by enabling more precise and informed decision-making. The focus extends to innovative image analysis techniques that enhance crop and soil monitoring, disease detection, and yield predictions through data from sensors and images. It also explores the use of remote sensing for near real-time, comprehensive management integral to digital agriculture. In livestock management, AI-driven image analysis contributes to sophisticated health monitoring and behavioral analytics, enabling strategies such as early illness detection and optimized feeding. The issue further examines the inclusion of RGB, depth, multispectral, and hyperspectral imaging to augment the data quality and utility. Additionally, the value of quick, mobile imaging processes tailored for modern agriculture’s fast-paced needs is highlighted, alongside the role of edge-computing in managing the significant 'digitization footprint' that these advanced imaging technologies bring to agricultural management. Contributions are encouraged to explore the integration of spatial and edge processing AI with web-based applications and visual analytics, aiming to enhance both productivity and sustainability in agriculture.

Dr. Weizhen Liang
Dr. Jingqiu Chen
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. Agriculture 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

  • crop monitoring
  • image analysis
  • AI
  • deep learning
  • remote sensing
  • digital agriculture
  • edge computing

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Published Papers (4 papers)

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Research

14 pages, 4478 KiB  
Article
A New Kiwi Fruit Detection Algorithm Based on an Improved Lightweight Network
by Yi Yang, Lijun Su, Aying Zong, Wanghai Tao, Xiaoping Xu, Yixin Chai and Weiyi Mu
Agriculture 2024, 14(10), 1823; https://doi.org/10.3390/agriculture14101823 - 16 Oct 2024
Viewed by 396
Abstract
To address the challenges associated with kiwi fruit detection methods, such as low average accuracy, inaccurate recognition of fruits, and long recognition time, this study proposes a novel kiwi fruit recognition method based on an improved lightweight network S-YOLOv4-tiny detection algorithm. Firstly, the [...] Read more.
To address the challenges associated with kiwi fruit detection methods, such as low average accuracy, inaccurate recognition of fruits, and long recognition time, this study proposes a novel kiwi fruit recognition method based on an improved lightweight network S-YOLOv4-tiny detection algorithm. Firstly, the YOLOv4-tiny algorithm utilizes the CSPdarknet53-tiny network as a backbone feature extraction network, replacing the CSPdarknet53 network in the YOLOv4 algorithm to enhance the speed of kiwi fruit recognition. Additionally, a squeeze-and-excitation network has been incorporated into the S-YOLOv4-tiny detection algorithm to improve accurate image extraction of kiwi fruit characteristics. Finally, enhancing dataset pictures using mosaic methods has improved precision in the characteristic recognition of kiwi fruits. The experimental results demonstrate that the recognition and positioning of kiwi fruits have yielded improved outcomes. The mean average precision (mAP) stands at 89.75%, with a detection precision of 93.96% and a single-picture detection time of 8.50 ms. Compared to the YOLOv4-tiny detection algorithm network, the network in this study exhibits a 7.07% increase in mean average precision and a 1.16% acceleration in detection time. Furthermore, an enhancement method based on the Squeeze-and-Excitation Network (SENet) is proposed, as opposed to the convolutional block attention module (CBAM) and efficient channel attention (ECA). This approach effectively addresses issues related to slow training speed and low recognition accuracy of kiwi fruit, offering valuable technical insights for efficient mechanical picking methods. Full article
(This article belongs to the Special Issue Advanced Image Collection, Processing, and Analysis in Crop and Livestock Management)
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22 pages, 7012 KiB  
Article
A Multi-View Real-Time Approach for Rapid Point Cloud Acquisition and Reconstruction in Goats
by Yi Sun, Qifeng Li, Weihong Ma, Mingyu Li, Anne De La Torre, Simon X. Yang and Chunjiang Zhao
Agriculture 2024, 14(10), 1785; https://doi.org/10.3390/agriculture14101785 - 11 Oct 2024
Viewed by 327
Abstract
The body size, shape, weight, and scoring of goats are crucial indicators for assessing their growth, health, and meat production. The application of computer vision technology to measure these parameters is becoming increasingly prevalent. However, in real farm environments, obstacles, such as fences, [...] Read more.
The body size, shape, weight, and scoring of goats are crucial indicators for assessing their growth, health, and meat production. The application of computer vision technology to measure these parameters is becoming increasingly prevalent. However, in real farm environments, obstacles, such as fences, ground conditions, and dust, pose significant challenges for obtaining accurate goat point cloud data. These obstacles lead to difficulties in rapid data extraction and result in incomplete reconstructions, causing substantial measurement errors. To address these challenges, we developed a system for real-time, non-contact acquisition, extraction, and reconstruction of goat point clouds using three depth cameras. The system operates in a scenario where goats walk naturally through a designated channel, and bidirectional distributed triggering logic is employed to ensure real-time acquisition of the point cloud. We also designed a noise recognition and filtering method tailored to handle complex environmental interferences found on farms, enabling automatic extraction of the goat point cloud. Furthermore, a distributed point cloud completion algorithm was developed to reconstruct missing sections of the goat point cloud caused by unavoidable factors such as railings and dust. Measurements of body height, body slant length, and chest circumference were calculated separately with deviation of no more than 25 mm and an average error of 3.1%. The system processes each goat in an average time of 3–5 s. This method provides rapid and accurate extraction and complementary reconstruction of 3D point clouds of goats in motion on real farms, without human intervention. It offers a valuable technological solution for non-contact monitoring and evaluation of goat body size, weight, shape, and appearance. Full article
(This article belongs to the Special Issue Advanced Image Collection, Processing, and Analysis in Crop and Livestock Management)
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26 pages, 21442 KiB  
Article
DGS-YOLOv8: A Method for Ginseng Appearance Quality Detection
by Lijuan Zhang, Haohai You, Zhanchen Wei, Zhiyi Li, Haojie Jia, Shengpeng Yu, Chunxi Zhao, Yan Lv and Dongming Li
Agriculture 2024, 14(8), 1353; https://doi.org/10.3390/agriculture14081353 - 13 Aug 2024
Viewed by 830
Abstract
In recent years, the research and application of ginseng, a famous and valuable medicinal herb, has received extensive attention at home and abroad. However, with the gradual increase in the demand for ginseng, discrepancies are inevitable when using the traditional manual method for [...] Read more.
In recent years, the research and application of ginseng, a famous and valuable medicinal herb, has received extensive attention at home and abroad. However, with the gradual increase in the demand for ginseng, discrepancies are inevitable when using the traditional manual method for grading the appearance and quality of ginseng. Addressing these challenges was the primary focus of this study. This study obtained a batch of ginseng samples and enhanced the dataset by data augmentation, based on which we refined the YOLOv8 network in three key dimensions: firstly, we used the C2f-DCNv2 module and the SimAM attention mechanism to augment the model’s effectiveness in recognizing ginseng appearance features, followed by the use of the Slim-Neck combination (GSConv + VoVGSCSP) to lighten the model These improvements constitute our proposed DGS-YOLOv8 model, which achieved an impressive mAP50 of 95.3% for ginseng appearance quality detection. The improved model not only has a reduced number of parameters and smaller size but also improves 6.86%, 2.73%, and 3.82% in precision, mAP50, and mAP50-95 over the YOLOv8n model, which comprehensively outperforms the other related models. With its potential demonstrated in this experiment, this technology can be deployed in large-scale production lines to benefit the food and traditional Chinese medicine industries. In summary, the DGS-YOLOv8 model has the advantages of high detection accuracy, small model space occupation, easy deployment, and robustness. Full article
(This article belongs to the Special Issue Advanced Image Collection, Processing, and Analysis in Crop and Livestock Management)
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15 pages, 2006 KiB  
Article
Tracking Free-Ranging Pantaneiro Sheep during Extreme Drought in the Pantanal through Precision Technologies
by Gianni Aguiar da Silva, Sandra Aparecida Santos, Paulo Roberto de Lima Meirelles, Rafael Silvio Bonilha Pinheiro, Marcos Paulo Silva Gôlo, Jorge Luiz Franco, Igor Alexandre Hany Fuzeta Schabib Péres, Laysa Fontes Moura and Ciniro Costa
Agriculture 2024, 14(7), 1154; https://doi.org/10.3390/agriculture14071154 - 16 Jul 2024
Viewed by 600
Abstract
The Pantanal has been facing consecutive years of extreme drought, with an impact on the quantity and quality of available pasture. However, little is known about how locally adapted breeds respond to the distribution of forage resources in this extreme drought scenario. This [...] Read more.
The Pantanal has been facing consecutive years of extreme drought, with an impact on the quantity and quality of available pasture. However, little is known about how locally adapted breeds respond to the distribution of forage resources in this extreme drought scenario. This study aimed to evaluate the movement of free-grazing Pantaneiro sheep using a low-cost GPS to assess the main grazing sites, measure the daily distance traveled, and determine the energy requirements for walking with body weight monitoring. In a herd of 100 animals, 31 were selected for weighing, and six ewes were outfitted with GPS collars. GPS data collected on these animals every 10 m from August 2020 to May 2021 was analyzed using the Python programming language. The traveled distance and activity energy requirements (ACT) for horizontal walking (Mcal/d of NEm) were determined. The 31 ewes were weighed at the beginning and end of each season. The available dry matter (DM) and floristic composition of the grazing sites were estimated at the peak of the drought. DM was predicted using power regression with NDVI (normalized difference vegetation index) (R2 = 0.94). DM estimates averaged 450 kg/ha, ranging from traces to 3830 kg/ha, indicating overall very low values. Individual variation in the frequency of use of grazing sites was observed (p < 0.05), reflecting the distances traveled and the energetic cost of the activity. The range of distances traveled by the animals varied from 3.3 to 17.7 km/d, with an average of 5.9 km/d, indicating low energy for walking. However, the traveled distance and ACT remained consistent over time; there were no significant differences observed between seasons (p > 0.05). On average, the ewes’ initial weight did not differ from the weight at the drought peak (p > 0.05), indicating that they maintained their initial weight, which is important for locally adapted breeds as it confers robustness and resilience. This study also highlighted the importance of the breed’s biodiverse diet during extreme drought, which enabled the selection of forage for energy and nutrient supplementation. The results demonstrated that precision tools such as GPS and satellite imagery enabled the study of animals in extensive systems, thereby contributing to decision-making within the production system. Full article
(This article belongs to the Special Issue Advanced Image Collection, Processing, and Analysis in Crop and Livestock Management)
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