Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
8.2
3.5
Journals
Sensors
Special Issues
Sensing and Machine Learning in Autonomous Agriculture
sensors-logo
Submit to Special Issue Submit Abstract to Special Issue Review for Sensors Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

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: 31 July 2026 | Viewed by 2528

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

Benefits of Publishing in a Special Issue

  • Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
  • Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
  • Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
  • External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
  • Reprint: MDPI Books provides the opportunity to republish successful Special Issues in book format, both online and in print.

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

Published Papers (3 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

22 pages, 62906 KB  
Article
In-Field Nondestructive Detection of Nitrogen Status on ‘Yotsuboshi’ Strawberry Using Deep Learning Algorithm
by Bryan V. Apacionado and Tofael Ahamed
Sensors 2026, 26(10), 3107; https://doi.org/10.3390/s26103107 - 14 May 2026
Viewed by 297
Abstract
Nitrogen (N) management is critical for optimizing growth and fruit quality in open-field strawberry cultivation, demanding advanced technological solutions for reliable nutrient assessment. However, visual symptom diagnosis, though widely utilized for nutrient monitoring, is inherently subjective and prone to observer bias, resulting in [...] Read more.
Nitrogen (N) management is critical for optimizing growth and fruit quality in open-field strawberry cultivation, demanding advanced technological solutions for reliable nutrient assessment. However, visual symptom diagnosis, though widely utilized for nutrient monitoring, is inherently subjective and prone to observer bias, resulting in inconsistent and often unreliable assessments. While available accurate tissue analysis is destructive and costly. Nondestructive, in-field imaging techniques such as the normalized difference vegetation index (NDVI) exist but require expensive multispectral imaging systems. To address these limitations, this study developed a streamlined methodology for in-field N status detection using deep learning on standard RGB images. The experiment utilized ‘Yotsuboshi’ strawberries in a randomized complete block design with sufficient nitrogen (T1) and deficient nitrogen (T2) treatments. To mitigate ambient light variability, a key challenge in open-field phenotyping, a low-cost phenotyping cylinder was developed for standardized smartphone image acquisition. Rigorous four-stage annotation criteria were also introduced to classify the nitrogen status in strawberry leaves as NormalN, LowN, or AdvancedLowN, ensuring a high-quality novel dataset. A YOLO11 model trained on this dataset achieved precision, recall, and mAP50 values exceeding 99%. Subsequent testing using the phenotyping cylinder yielded a mAP50 of 87%. In-field validation without a phenotyping cylinder also demonstrated robust performance under diffuse cloudy conditions (82.7% mAP50), outperforming direct sunlight (79% mAP50). Moreover, the model’s classifications of ‘NormalN’ and ‘LowN’ statuses strongly corresponded with NDVI measurements, validating the accuracy of the RGB-based approach. This research demonstrates the significant potential of combining deep learning and phenotyping cylinder to create a rapid, low-cost, nondestructive and reliable tool for in-field nitrogen detection, with possible application across different crops and environmental conditions. Full article
(This article belongs to the Special Issue Sensing and Machine Learning in Autonomous Agriculture)
►▼ Show Figures

Figure 1

19 pages, 1015 KB  
Article
Smart Energy Management in Agricultural Wireless Sensor Nodes Using TinyML-Based Adaptive Sampling
by Adrian Hinostroza, Jimmy Tarrillo and Moises Nuñez
Sensors 2026, 26(7), 2014; https://doi.org/10.3390/s26072014 - 24 Mar 2026
Viewed by 654
Abstract
Smart sensors are increasingly used in agriculture to monitor environmental conditions and support data-driven decision-making. However, traditional sensor implementations face critical challenges related to power consumption, especially in remote farms—such as pitaya plantations—where access to electricity and ongoing maintenance is limited. This paper [...] Read more.
Smart sensors are increasingly used in agriculture to monitor environmental conditions and support data-driven decision-making. However, traditional sensor implementations face critical challenges related to power consumption, especially in remote farms—such as pitaya plantations—where access to electricity and ongoing maintenance is limited. This paper presents a smart energy management system for agricultural sensor nodes integrating a machine learning model for adaptive sampling and a batching strategy to optimize energy usage. A lightweight Stochastic Gradient Descent (SGD) regressor trained on temperature dynamics runs on-device to predict the sampling interval (Ts). In parallel, the node adjusts the number of buffered samples as the battery state of charge (SOC) decreases, reducing Long Range (LoRa) transmissions. Field experiments show that the proposed approach reduces energy consumption by 77.8% compared with fixed-interval sampling, while maintaining good temperature fidelity with Mean Absolute Error (MAE) of 0.537 °C for temperature reconstruction. Full article
(This article belongs to the Special Issue Sensing and Machine Learning in Autonomous Agriculture)
►▼ Show Figures

Figure 1

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 1130
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)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-3
Back to TopTop