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Smart Sensing Systems for Arable Crop and Grassland Management
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Smart Sensing Systems for Arable Crop and Grassland Management

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

Deadline for manuscript submissions: 30 June 2025 | Viewed by 394

Special Issue Editors

Dr. Ruben Linares

E-Mail Website
Guest Editor
Departamento de Producción Agraria, Escuela Técnica Superior de Ingeniería Agronómica, Alimentaria y de Biosistemas, Universidad Politécnica de Madrid, Madrid, Spain
Interests: water resources management; nutrient management; soil management in agriculture systems
Dr. Lorena Parra

E-Mail Website
Guest Editor
Research Institute for Integrated Management of Coastal Areas (IGIC), Universitat Politècnica de València, 46022 València, Spain
Interests: water quality; wireless sensor networks; environmental monitoring; aquaculture; precision agriculture; pollution monitoring; physical sensors; chemical sensors; remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

In recent decades, multiple sensor-based systems for agriculture have appeared. Using these sensors combined with different methodologies based on artificial intelligence, such as machine learning, has provided multiple solutions. Most of these solutions have been designed to be applied in greenhouses and permanent crops, while systems for arable crops and grasslands have been less proposed.

In this Special Issue, we aim to collect novel developments of smart sensing systems to support the management of arable crops and grasslands, including urban and periurban grasslands. These systems can be based on multiple types of sensors: optical sensors, acoustic sensors, electromagnetic sensors, or other physical/chemical sensors. The systems can monitor the parameters of soil, water, plants, or climate and combine them. The gathered information should be used as input for algorithms that provide smart, data-based agricultural management recommendations linked to irrigation, fertilization, and pest management.

  • smart agriculture
  • smart farming
  • arable crop management
  • meadow management
  • urban grassland management
  • IoT in agriculture
  • WSN in agriculture
  • proximal sensing

Dr. Ruben Linares
Dr. Lorena Parra
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

  • smart agriculture
  • smart farming
  • IoT in agriculture
  • proximal sensing

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Published Papers (1 paper)

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Research

17 pages, 9644 KiB  
Article
Intelligent Detection of Tomato Ripening in Natural Environments Using YOLO-DGS
by Mengyuan Zhao, Beibei Cui, Yuehao Yu, Xiaoyi Zhang, Jiaxin Xu, Fengzheng Shi and Liang Zhao
Sensors 2025, 25(9), 2664; https://doi.org/10.3390/s25092664 - 23 Apr 2025
Viewed by 202
Abstract
To achieve accurate detection of tomato fruit maturity and enable automated harvesting in natural environments, this paper presents a more lightweight and efficient maturity detection algorithm, YOLO-DGS, addressing the challenges of subtle maturity differences between regular and cherry tomatoes, as well as fruit [...] Read more.
To achieve accurate detection of tomato fruit maturity and enable automated harvesting in natural environments, this paper presents a more lightweight and efficient maturity detection algorithm, YOLO-DGS, addressing the challenges of subtle maturity differences between regular and cherry tomatoes, as well as fruit occlusion. First, to enhance feature extraction at various levels of abstraction in the input data, this paper proposes a novel segment-wise convolution module, C2f-GB. This module performs convolution in stages on the feature map, generating more feature maps with fewer parameters and computational resources, thereby improving the model’s feature extraction capability while reducing parameter count and computational cost. Next, based on the YOLO v10 algorithm, this paper removes redundant detection layers to enhance the model’s ability to capture specific features and further reduce the number of parameters. This paper then integrates a bidirectional feature pyramid network (BiFPN) into the neck network to improve feature capture across different scales, enhancing the model’s ability to handle objects of varying sizes and complexities. Finally, we introduce a novel channel attention mechanism that allows the network to dynamically adjust its focus on channels, efficiently utilizing available information. Experimental results demonstrate that the improved YOLO-DGS model achieves a 2.6% increase in F1 score, 2.1% in recall, 2% in mAP50, and 1% in mAP50-95. Additionally, inference speed is improved by 12.5%, and the number of parameters is reduced by 26.3%. Compared to current mainstream lightweight object detection models, YOLO-DGS outperforms them, offering an efficient solution for the tomato harvesting robot vision system in natural environments. Full article
(This article belongs to the Special Issue Smart Sensing Systems for Arable Crop and Grassland Management)
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