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Sensing and Machine Learning in Autonomous Agriculture
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Sensing and Machine Learning in Autonomous Agriculture

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

Deadline for manuscript submissions: 15 March 2026 | Viewed by 817

Special Issue Editor

Dr. Ahamed Tofael

E-Mail Website
Guest Editor
Institute of Life and Environmental Sciences, University of Tsukuba, Tsukuba, Japan
Interests: artificial intelligence; internet of things; data-driven system; deep learning; agricultural safety system; artificial pollination; disease detection; agricultural remote sensing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Smart agricultural technology offers opportunities to enhance agricultural production across outdoor and indoor crop cultivation systems, orchard management, non-destructive quality assessment, and safety systems. The application of state-of-the-art technologies is explored to develop solutions through AI-based and data-driven approaches in agriculture integrated with IoT. As the agricultural sector continues to advance, the safety of agricultural vehicles remains a significant concern. Furthermore, data-driven agricultural systems significantly contribute to disease detection in fruits and vegetables for quality management, automation to enhance pollination mechanisms, and insect control management in crop production across various stages, from pre-harvest to post-harvest.

Therefore, “Sensing and Machine Learning in Autonomous Agriculture” is an open Special Issue that welcomes a variety of novel scientific articles, including innovative and cutting-edge research using deep learning and data-driven systems in agricultural production systems. The Editor invites contributions of original research, review articles, and case studies to provide insights into the application of a deep learning approach to agricultural systems. The aim is to develop AI-based deep learning systems to enable intelligent digital farming in the smart agriculture space, covering a wide range of applications from outdoor to indoor production systems. This will encourage researchers to contribute to the development of new robotic platforms and data data-driven systems that can be used to address labor shortages and ensure safety while improving productivity through minimal human intervention. Furthermore, this Special Issue also invites articles focusing on the application of deep learning systems for disease detection in fruit crops, the automation of pollination mechanisms, and insect management in crop production.

Dr. Ahamed Tofael
Guest Editor

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. 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

  • autonomous sensing and robotic system
  • agricultural driver safety
  • plant disease detection
  • artificial pollination mechanism

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Published Papers (1 paper)

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Research

39 pages, 31456 KB  
Article
AHE-FNUQ: An Advanced Hierarchical Ensemble Framework with Neural Network Fusion and Uncertainty Quantification for Outlier Detection in Agri-IoT
by Ahmed Amamou, Mimoun Lamrini, Bilal Ben Mahria, Younes Balboul, Said Hraoui, Omar Hegazy and Abdellah Touhafi
Sensors 2025, 25(22), 6841; https://doi.org/10.3390/s25226841 - 8 Nov 2025
Viewed by 612
Abstract
Agricultural Internet of Things (Agri-IoT) systems need strong anomaly detection to monitor crops effectively. However, current approaches lack accuracy and efficiency. To mitigate this problem, we proposed an advanced hierarchical ensemble framework with neural network fusion and uncertainty quantification (AHE-FNUQ). This framework combines [...] Read more.
Agricultural Internet of Things (Agri-IoT) systems need strong anomaly detection to monitor crops effectively. However, current approaches lack accuracy and efficiency. To mitigate this problem, we proposed an advanced hierarchical ensemble framework with neural network fusion and uncertainty quantification (AHE-FNUQ). This framework combines six detection algorithms: Isolation Forest, ECOD (empirical cumulative distribution-based outlier detection), COPOD (copula-based outlier detection), HBOS (histogram-based outlier score), OC-SVM (one-class support vector machine), and KNN (k-nearest neighbors). It uses a three-level decision process: (1) selecting models with good performance (ROC AUC > 0.75), (2) applying recall-weighted ensemble fusion, and (3) using a fusion neural network (FusionNN) to improve uncertain predictions in the confidence range [0.75,0.9]. The framework was tested on three agricultural datasets with contamination levels between 10% and 50%. The result showed strong performance: ROC AUC between 0.93 and 0.99, PR AUC between 0.90 and 0.98, and F1-scores between 0.85 and 0.90. Moreover, we have conducted a statistical test (Friedman test, χ2=63.02, p<0.0001) and confirmed that AHE-FNUQ is significantly better than common methods such as COPOD, ECOD, HBOS, Isolation Forest, and KNN. Full article
(This article belongs to the Special Issue Sensing and Machine Learning in Autonomous Agriculture)
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