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Intelligent Computing and Sensing Systems for Sustainable Precision Agriculture
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Intelligent Computing and Sensing Systems for Sustainable Precision Agriculture

A special issue of Computers (ISSN 2073-431X). This special issue belongs to the section "AI-Driven Innovations".

Deadline for manuscript submissions: 31 December 2026 | Viewed by 1084

Special Issue Editor

Dr. Abhilash Chandel

E-Mail Website
Guest Editor
Department of Biological Systems Engineering, Tidewater AREC, Virginia Tech, Suffolk, VA 23437, USA
Interests: precision agriculture; smart farming; digital agriculture; remote sensing; Internet of Things (IoT); edge computing; cloud computing; real-time data processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

This Special Issue aims to present cutting-edge research and innovative applications at the intersection of artificial intelligence, advanced sensing, and scalable computing systems to advance sustainable precision agriculture. With growing challenges such as climate uncertainties, resource scarcity, and increasing global food demand, there is an urgent need to develop intelligent, efficient, and environmentally responsible farming solutions. This Issue will gather high-quality contributions that explore how data-driven technologies—including IoT, edge and cloud computing, robotics, and AI—can be integrated to enable real-time monitoring, decision-making, and autonomous operations in agricultural systems. We welcome original research and review articles that address both theoretical advances and practical implementations supporting the transition toward more productive, resilient, and sustainable agricultural practices.

Topics of interest include (but are not limited to) the following:

  • AI and machine learning for crop and soil monitoring.
  • IoT and wireless sensor networks in agriculture.
  • UAVs and satellite-based remote sensing for precision farming.
  • Edge and cloud computing platforms for agricultural data analytics.
  • Robotics and automation for smart farming operations.
  • Digital twins and decision support systems for farm management.
  • Energy-efficient and sustainable computing solutions for agriculture.
  • Hyperspectral and multimodal sensing in field conditions.
  • Real-time control systems for irrigation, fertilization, and pest management.
  • Data fusion and big data analytics in agroecological systems.
  • Human-in-the-loop and human–robot collaboration in farming.
  • Cybersecurity and data privacy in agricultural cyber-physical systems.

We invite researchers and practitioners to submit their work to help shape the future of intelligent and sustainable agriculture.

Dr. Abhilash Chandel
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. Computers is an international peer-reviewed open access monthly 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 1800 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

  • sustainable precision agriculture
  • intelligent computing
  • sensing systems
  • smart farming
  • digital agriculture
  • artificial intelligence (AI)
  • machine learning
  • Internet of Things (IoT)
  • remote sensing
  • edge computing
  • cloud computing
  • robotics and automation
  • digital twins
  • data analytics
  • computer vision
  • wireless sensor networks
  • crop monitoring

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

Published Papers (3 papers)

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Research

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16 pages, 2961 KB  
Article
Non-Destructive Determination of Hass Avocado Harvest Maturity in Colombia Based on Low-Cost Bioimpedance Spectroscopy and Machine Learning
by Froylan Jimenez Sanchez, Jose Aguilar and Marta Tabares-Betancur
Computers 2026, 15(3), 166; https://doi.org/10.3390/computers15030166 - 4 Mar 2026
Abstract
The export of Hass avocado (Persea americana Mill.) from Colombia requires accurate determination of harvest maturity, currently assessed through destructive dry matter (DM) measurements that are wasteful and limited in throughput. The objective of the article is to propose a low-cost, non-destructive [...] Read more.
The export of Hass avocado (Persea americana Mill.) from Colombia requires accurate determination of harvest maturity, currently assessed through destructive dry matter (DM) measurements that are wasteful and limited in throughput. The objective of the article is to propose a low-cost, non-destructive approach to determine the maturity of the Hass avocado crop based on machine learning techniques. The approach consists of a low-cost, non-invasive bioimpedance spectroscopy system operating in the 1–10 kHz range, featuring a custom Analog Front End (AFE) and a tetrapolar surface probe to mitigate skin contact resistance, which collects data for predictive models of avocado maturity. To evaluate the quality of the approach, a longitudinal field study (n = 100) was conducted in a commercial orchard in Cundinamarca, Colombia, tracking complex impedance features—Magnitude, Phase Angle, Resistance, and Reactance—of tagged fruits over 8 weeks across four measurement timepoints. The predictive performance of a classical chemometric model (PLS-DA), non-linear classifiers (SVM, Random Forest), and a temporal Deep Learning (LSTM) architecture was compared using a Stratified Group K-Fold Cross-Validation scheme to prevent data leakage across fruits from the same tree. The 4-electrode configuration successfully isolated mesocarp impedance, identifying the 5–7.2 kHz band as the most sensitive to physiological maturation. In turn, the LSTM model achieved a mean accuracy of 92.0% and an AUC of 0.94, outperforming the other models by 4.0% in mean accuracy. The results demonstrate that modeling the temporal trajectory of impedance, rather than single-point measurements, improves harvest maturity classification in Hass avocados, providing a scalable, low-cost alternative to destructive testing. Full article
(This article belongs to the Special Issue Intelligent Computing and Sensing Systems for Sustainable Precision Agriculture)
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Other

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80 pages, 3968 KB  
Systematic Review
Agroclimatic Sensing, Communication, and Computational Systems-Based Methods and Technologies for Precision Irrigation Management: Current State and Prospects
by Aminata Sarr, Abhilash K. Chandel, Lamine Diop, Yrébégnan Moussa Soro, Alain K. Tossa, Smrutilipi Hota and Arunachalam Manimozhian
Computers 2026, 15(2), 137; https://doi.org/10.3390/computers15020137 - 23 Feb 2026
Viewed by 261
Abstract
Agriculture uses most of the world’s fresh water. Given that the worldwide population is expanding at an alarming rate, more land cultivation is apparently in demand. As a result, much more water would be required to irrigate cultivable lands. However, fresh water is [...] Read more.
Agriculture uses most of the world’s fresh water. Given that the worldwide population is expanding at an alarming rate, more land cultivation is apparently in demand. As a result, much more water would be required to irrigate cultivable lands. However, fresh water is becoming scarce at a faster rate due to climate uncertainties and over-exploitation. Several controlled irrigation techniques, such as drip and sprinkler irrigation, have been introduced to safeguard water resources. However, these techniques do not readily meet crop water demands and often end up causing overapplication of water. Under these circumstances, smart precision irrigation is the best solution. Smart irrigation techniques facilitate delivery of water in an amount that is required by the crop as per site/location and temporal requirements. Several studies have been carried out in this area, and remarkable progress has been observed. These studies range from making use of in situ sophisticated sensors that are low-cost and consume minimum energy up to the use of small unmanned aerial systems (SUAS) and satellite imagery for irrigation management. This review summarizes research studies that highlight the components of developing and deploying various precision irrigation technologies, their benefits, and their limitations. Specifically, the scientific value of this study lies in outlining implications of using different sensors, parameters, and equipment, the agroclimatic models, communication technologies, artificial intelligence, and the energy sources to implement automated irrigation systems. A future scope of precision irrigation is also discussed in accordance with cost-effectiveness and sustainability. This study should also act as a referring guideline for new researchers as well as technology manufacturers who seek to design and develop a futuristic yet efficient irrigation system. Overall, this review is aimed at contributing to the understanding of automated irrigation systems for their effective deployment towards enhanced agricultural production, conserved water resources, and sustainable use of energy sources. Full article
(This article belongs to the Special Issue Intelligent Computing and Sensing Systems for Sustainable Precision Agriculture)
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53 pages, 3892 KB  
Systematic Review
Research Advances in Maize Crop Disease Detection Using Machine Learning and Deep Learning Approaches
by Thangavel Murugan, Nasurudeen Ahamed Noor Mohamed Badusha, Nura Shifa Musa, Eiman Mubarak Masoud Alahbabi, Ruqayyah Ali Ahmed Alyammahi, Abebe Belay Adege, Afedi Abdi and Zemzem Mohammed Megersa
Computers 2026, 15(2), 99; https://doi.org/10.3390/computers15020099 - 2 Feb 2026
Viewed by 519
Abstract
Recent developments in machine learning (ML) and deep learning (DL) algorithms have introduced a new approach to the automatic detection of plant diseases. However, existing reviews of this field tend to be broader than maize-focused and do not offer a comprehensive synthesis of [...] Read more.
Recent developments in machine learning (ML) and deep learning (DL) algorithms have introduced a new approach to the automatic detection of plant diseases. However, existing reviews of this field tend to be broader than maize-focused and do not offer a comprehensive synthesis of how ML and DL methods have been applied to image-based detection of maize leaf disease. Following the PRISMA guidelines, this systematic review of 102 peer-reviewed papers published between 2017 and 2025 examined methods and approaches used to classify leaf images for detecting disease in maize plants. The 102 papers were categorized by disease type, dataset, task, learning approach, architecture, and metrics used to evaluate performance. The analysis results indicate that traditional ML methods, when combined with effective feature engineering, can achieve classification accuracies of approximately 79–100%, while DL, especially CNNs, provide consistent, superior classification performance on controlled benchmark datasets (up to 99.9%). Yet in “real field” conditions, many of these improvements typically decrease or disappear due to dataset bias, environmental factors, and limited evaluation. The review provides a comprehensive overview of emerging trends, performance trade-offs, and ongoing gaps in developing field-ready, explainable, reliable, and scalable maize leaf disease detection systems. Full article
(This article belongs to the Special Issue Intelligent Computing and Sensing Systems for Sustainable Precision Agriculture)
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