Advanced Image Collection, Processing, and Analysis in Crop Management—2nd Edition
A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Artificial Intelligence and Digital Agriculture".
Deadline for manuscript submissions: 30 June 2026 | Viewed by 22
Special Issue Editors
Interests: precision irrigation; irrigation science; application of image analysis in crop and livestock management; advanced crop and livestock modeling; Internet of Things (IoT); precision agriculture
Special Issues, Collections and Topics in MDPI journals
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 Issues, Collections and Topics in MDPI journals
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 management.
This Special Issue, titled "Advanced Image Collection, Processing, and Analysis in Crop Management", underscores the value of image-based data in agriculture, providing insights into crop health 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, weed detection, 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. The Special 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 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-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 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
- crop monitoring
- image analysis
- Artificial Intelligence
- deep learning
- remote sensing
- digital agriculture
- edge computing
- computer vision
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