The Future of Artificial Intelligence in Agriculture

A special issue of AgriEngineering (ISSN 2624-7402). This special issue belongs to the section "Computer Applications and Artificial Intelligence in Agriculture".

Deadline for manuscript submissions: 31 May 2025 | Viewed by 1514

Special Issue Editors


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Guest Editor
Department of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: antenna design; microwave components design; wireless communications; evolutionary algorithms; machine learning
Special Issues, Collections and Topics in MDPI journals

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Guest Editor

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Guest Editor
ELEDIA@AUTH, Department of Physics, Aristotle University of Thessaloniki, 54124 Thessaloniki, Greece
Interests: wireless sensor networks; Internet of Things; broadband communications; non-ionizing radiation protection; broadband fiber-optic networks; antenna design and optimization; 5G communication networks; artificial intelligence
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The world of agriculture is undergoing a profound transformation, which many believe will be as impactful as previous agricultural revolutions. The ability to more precisely monitor crops and control irrigation, fertilization, and treatments at a much finer granularity than before, is enabling the use of land previously not considered for agricultural purposes, optimizing the use of resources and maximizing the health of plants and the yield of crops. Utilizing information retrieval and processing technologies such as blockchain, IoT, machine learning, deep learning, cloud computing, and edge computing is advantageous. Applications of computer vision, machine learning, and IoT will increase the production, quality, and ultimately the profitability of farmers and related industries. Precision learning in the field of agriculture is crucial for increasing the overall harvest yield.

Artificial Intelligence techniques such as deep learning as well as optimization techniques using evolutionary algorithms broaden and extend their application scope. Their application to the agriculture domain is constantly growing. Artificial Intelligence is the current technology that is assisting farmers in minimizing crop losses by providing rich crop-related recommendations and insights.

This Special Issue aims to publish extended versions of papers in the area of Artificial Intelligence. Potential topics include but are not limited to the following:

  • Deep learning
  • Harvesting
  • Machine learning
  • Smart agriculture
  • Evolutionary Algorithms
  • Optimization techniques
  • IoT
  • Edge Intelligence
  • Computer Vision

Dr. Sotirios K. Goudos
Prof. Dr. Shaohua Wan
Dr. Achilles Boursianis
Guest Editors

Manuscript Submission Information

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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 1600 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

  • deep learning
  • harvesting
  • machine learning
  • smart agriculture
  • evolutionary algorithms
  • optimization techniques
  • IoT
  • edge intelligence
  • computer vision

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Published Papers (1 paper)

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Research

15 pages, 3524 KiB  
Article
ChickenSense: A Low-Cost Deep Learning-Based Solution for Poultry Feed Consumption Monitoring Using Sound Technology
by Ahmad Amirivojdan, Amin Nasiri, Shengyu Zhou, Yang Zhao and Hao Gan
AgriEngineering 2024, 6(3), 2115-2129; https://doi.org/10.3390/agriengineering6030124 - 9 Jul 2024
Cited by 1 | Viewed by 868
Abstract
This research proposes a low-cost system consisting of a hardware setup and a deep learning-based model to estimate broiler chickens’ feed intake, utilizing audio signals captured by piezoelectric sensors. The signals were recorded 24/7 for 19 consecutive days. A subset of the raw [...] Read more.
This research proposes a low-cost system consisting of a hardware setup and a deep learning-based model to estimate broiler chickens’ feed intake, utilizing audio signals captured by piezoelectric sensors. The signals were recorded 24/7 for 19 consecutive days. A subset of the raw data was chosen, and events were labeled in two classes, feed-pecking and non-pecking (including singing, anomaly, and silence samples). Next, the labeled data were preprocessed through a noise removal algorithm and a band-pass filter. Then, the spectrogram and the signal envelope were extracted from each signal and fed as inputs to a VGG-16-based convolutional neural network (CNN) with two branches for 1D and 2D feature extraction followed by a binary classification head to classify feed-pecking and non-pecking events. The model achieved 92% accuracy in feed-pecking vs. non-pecking events classification with an f1-score of 91%. Finally, the entire raw dataset was processed utilizing the developed model, and the resulting feed intake estimation was compared with the ground truth data from scale measures. The estimated feed consumption showed an 8 ± 7% mean percent error on daily feed intake estimation with a 71% R2 score and 85% Pearson product moment correlation coefficient (PPMCC) on hourly intake estimation. The results demonstrate that the proposed system estimates broiler feed intake at each feeder and has the potential to be implemented in commercial farms. Full article
(This article belongs to the Special Issue The Future of Artificial Intelligence in Agriculture)
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