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Artificial Intelligence-Powered Sensors in Smart Agriculture and Precision Forestry

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

Deadline for manuscript submissions: 25 July 2024 | Viewed by 1068

Special Issue Editor


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Guest Editor
Department of Agriculture and Forest Sciences (DAFNE), Via San Camillo de Lellis s.n.c., 01100 Viterbo, Italy
Interests: precision agriculture; machine learning; deep learning; precision forestry; condensed matter physics; spectroscopy
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The improvements brought by AI technologies in smart agriculture and precision forestry have the potential to increase productivity, reduce waste, and improve sustainability. Indeed, AI-powered sensors can grant a complete map of the culture, allowing for informed decisions about fertilization, irrigation, and pest control, resulting in improved crop yields and more efficient use of resources. AI applications to precision forestry can be used to analyze signals collected by remote and proximal sensors,  allowing the precise identification of areas that are at risk of disease or wildfire and the development of targeted strategies for managing these risks. Finally, AI-based signal analysis can be used also to improve work safety during in-field operations. 

This Special Issue will address all types of AI-powered sensors applied to smart agriculture and precision forestry.

Dr. Luciano Ortenzi
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 100 words) can be sent to the Editorial Office for announcement on this website.

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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

  • machine learning
  • deep learning
  • pattern recognition
  • image classification
  • image segmentation
  • object detection
  • hyperspectral and multispectral signals
  • thermal images
  • proximal sensing
  • remote sensing
  • smart agriculture
  • smart pruning
  • precision forestry
  • animal tracking

Published Papers (1 paper)

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Research

15 pages, 2876 KiB  
Article
Real-Time AI-Assisted Push-Broom Hyperspectral System for Precision Agriculture
by Igor Neri, Silvia Caponi, Francesco Bonacci, Giacomo Clementi, Francesco Cottone, Luca Gammaitoni, Simone Figorilli, Luciano Ortenzi, Simone Aisa, Federico Pallottino and Maurizio Mattarelli
Sensors 2024, 24(2), 344; https://doi.org/10.3390/s24020344 - 06 Jan 2024
Viewed by 763
Abstract
In the ever-evolving landscape of modern agriculture, the integration of advanced technologies has become indispensable for optimizing crop management and ensuring sustainable food production. This paper presents the development and implementation of a real-time AI-assisted push-broom hyperspectral system for plant identification. The push-broom [...] Read more.
In the ever-evolving landscape of modern agriculture, the integration of advanced technologies has become indispensable for optimizing crop management and ensuring sustainable food production. This paper presents the development and implementation of a real-time AI-assisted push-broom hyperspectral system for plant identification. The push-broom hyperspectral technique, coupled with artificial intelligence, offers unprecedented detail and accuracy in crop monitoring. This paper details the design and construction of the spectrometer, including optical assembly and system integration. The real-time acquisition and classification system, utilizing an embedded computing solution, is also described. The calibration and resolution analysis demonstrates the accuracy of the system in capturing spectral data. As a test, the system was applied to the classification of plant leaves. The AI algorithm based on neural networks allows for the continuous analysis of hyperspectral data relative up to 720 ground positions at 50 fps. Full article
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