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Plants
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Smart Agriculture for Sustainable Crop Production: From Precision Technologies to...
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Smart Agriculture for Sustainable Crop Production: From Precision Technologies to Field Applications

A special issue of Plants (ISSN 2223-7747). This special issue belongs to the section "Plant Modeling".

Deadline for manuscript submissions: 21 December 2026 | Viewed by 2444

Special Issue Editors

Prof. Dr. Fábio Henrique Rojo Baio

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Guest Editor
Department of Agronomy, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, Brazil
Interests: agriculture; agriculture engineering; energy in agriculture; sustainable agriculture; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Paulo Eduardo Teodoro

E-Mail Website
Guest Editor
Department of Agronomy, Federal University of Mato Grosso do Sul (UFMS), Chapadão do Sul 79560-000, Brazil
Interests: statistics; multivariate analysis; plant breeding; biometrics; remote sensing; sensors; genomic selection; geostatistics; precision agriculture
Special Issues, Collections and Topics in MDPI journals
Dr. Larissa Pereira Ribeiro Teodoro

E-Mail Website
Guest Editor
Department of Agronomy, Universidade Federal de Mato Grosso do Sul, Chapadão do Sul 7956-000, Brazil
Interests: UAV; random forest; nitrogen; maize
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Precision agriculture represents a paradigm shift in crop management, leveraging cutting-edge technologies to optimize resource efficiency, enhance sustainability, and address global food security challenges. This Special Issue of Plants explores the integration of advanced tools, such as AI-driven analytics, hyperspectral imaging, IoT sensors, and autonomous machinery, into agricultural systems. We invite contributions that dissect the synergies between technological innovation and plant science, highlighting scalable solutions for diverse agroecosystems. This Special Issue will bridge the gap between theoretical innovation and on-ground implementation, highlighting technologies accessible to both industrial farms and smallholders. Submissions should demonstrate tangible outcomes, e.g., enhanced crop resilience, resource savings, or socio-economic improvements, while addressing scalability and adoption barriers. We seek original research, reviews, and case studies addressing the following:

  • Sensing and Monitoring: Remote/proximal sensing for plant phenotyping, soil health assessment, and stress detection;
  • Data Analytics: Machine learning models for yield prediction, disease identification, and nutrient management;
  • Automation: Robotics for precision planting, irrigation, and harvesting; drone applications in crop scouting;
  • Resource Optimization: Variable-rate technology (VRT) for water, fertilizers, and pesticides;
  • Sustainability Metrics: Quantifying environmental benefits (e.g., reduced emissions, biodiversity conservation) and economic impacts;
  • Cross-disciplinary Approaches: Blockchain for traceability, genomics-assisted breeding, and climate-resilient practices.

Prof. Dr. Fábio Henrique Rojo Baio
Prof. Dr. Paulo Eduardo Teodoro
Dr. Larissa Pereira Ribeiro Teodoro
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. 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

  • sensors
  • remote sensing
  • digital agriculture
  • high-precision phenotyping
  • computational intelligence

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

Published Papers (2 papers)

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Research

25 pages, 25354 KB  
Article
OpenPlant: A Large-Scale Benchmark Dataset for Agricultural Plant Classification Using CNNs, ViTs, and VLMs
by Kaiqi Liu, Wei Sun, Guanping Wang, Quan Feng and Hui Li
Plants 2026, 15(5), 727; https://doi.org/10.3390/plants15050727 - 27 Feb 2026
Viewed by 1357
Abstract
Accurate plant classification based on deep learning is important for precision agriculture, such as weed control, crop monitoring, and smart farming systems. The accuracies of deep learning models rely on datasets. Although many datasets have been proposed in recent decades, they have the [...] Read more.
Accurate plant classification based on deep learning is important for precision agriculture, such as weed control, crop monitoring, and smart farming systems. The accuracies of deep learning models rely on datasets. Although many datasets have been proposed in recent decades, they have the common limitations in terms of scale, less environmental diversity, and challenges of data integration. To solve these problems, in this paper, we introduce a new dataset named OpenPlant, which is a large-scale and open dataset containing 635,176 RGB images across 1167 plant species. OpenPlant includes diverse growth stages of plants, plant structures, and environmental conditions, and its annotations were carefully verified to ensure quality. The proposed OpenPlant can be a benchmark for agricultural plant classification. In this paper, we benchmarked 10 widely used convolutional neural networks (CNNs), 6 vision transformers (ViTs), and 12 vision–language models (VLMs) to provide a comprehensive evaluation. The OpenPlant dataset offers a comprehensive benchmark for agricultural research using deep learning and the results provide insights into future directions. Full article
(This article belongs to the Special Issue Smart Agriculture for Sustainable Crop Production: From Precision Technologies to Field Applications)
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20 pages, 3878 KB  
Article
TreeSeg-Net: An End-to-End Instance Segmentation Network for Leaf-Off Forest Point Clouds Using Global Context and Spatial Proximity
by Xingmei Xu, Ruihang Zhang, Shunfu Xiao, Jiayuan Li, Xinyue Zhang, Liying Cao, Helong Yu, Yuntao Ma, Jian Zhang and Xiyang Zhao
Plants 2026, 15(4), 525; https://doi.org/10.3390/plants15040525 - 7 Feb 2026
Viewed by 676
Abstract
Forest ecosystems play a pivotal role in maintaining the balance of the global carbon cycle and conserving biodiversity. High-density point clouds derived from unmanned aerial vehicle (UAV) structure from motion (SfM) and multi-view stereo (MVS) technologies offer a cost-effective solution for data acquisition. [...] Read more.
Forest ecosystems play a pivotal role in maintaining the balance of the global carbon cycle and conserving biodiversity. High-density point clouds derived from unmanned aerial vehicle (UAV) structure from motion (SfM) and multi-view stereo (MVS) technologies offer a cost-effective solution for data acquisition. These technologies have become efficient tools for facilitating precision forest resource management and extracting individual tree structural parameters. However, in complex forest scenarios during the leaf-off season, canopies exhibit unstructured branch network morphologies due to the absence of leaf occlusion, and adjacent crowns are heavily interlaced. Consequently, existing segmentation methods struggle to overcome challenges associated with fuzzy boundaries and instance adhesion. To address these challenges, this study proposes TreeSeg-Net, an end-to-end instance segmentation network designed to precisely separate individual trees directly from raw point clouds. The network incorporates a global context attention module (GCAM) to capture long-range feature dependencies, thereby compensating for the limitations of sparse convolution in perceiving global information. Simultaneously, a spatial proximity weighting module (SPWM) is designed. By introducing geometric center constraints and a distance penalty mechanism, this module effectively mitigates under-segmentation issues caused by the feature similarity of adjacent branches in high-canopy-density environments. Experimental results demonstrate that TreeSeg-Net achieves an average precision (AP) of 97.2% in instance segmentation tasks and a mean intersection over union (mIoU) of 99.7% in semantic segmentation tasks. Compared to mainstream networks, the proposed method exhibits superior segmentation accuracy, providing an efficient and automated technical solution for precise resource inventory in complex forest environments. Full article
(This article belongs to the Special Issue Smart Agriculture for Sustainable Crop Production: From Precision Technologies to Field Applications)
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