Special Issue "Applications of Machine Learning and Deep Learning in Agriculture"

A special issue of Informatics (ISSN 2227-9709). This special issue belongs to the section "Machine Learning".

Deadline for manuscript submissions: 30 September 2022 | Viewed by 2100

Special Issue Editors

Dr. Phuong T. Nguyen
E-Mail Website
Guest Editor
Department of Information Engineering, Computer Science and Mathematics, Università degli Studi dell'Aquila, L'Aquila, Italy
Interests: mining software repositories; recommender systems; semantic web and linked data; machine learning/deep learning with applications in healthcare and agriculture
Dr. Vito Walter Anelli
E-Mail Website
Guest Editor
SisInf Lab - Information Systems Laboratory, Politecnico di Bari, Bari, Italy
Interests: artificial intelligence; recommender systems; semantic web; deep neural networks

Special Issue Information

Dear Colleagues,

Deep learning algorithms enable machines to simulate humans’ learning activities and acquire real-world knowledge by generalizing from data. In this way, they are capable of identifying patterns and making decisions solely by means of data, without resorting to constant interventions from humans. The combination of deep neural networks with transfer learning is a successful strategy to address the problem of training a model given a limited amount of data. In this respect, deep learning has gained momentum, and its applications can be seen in a wide range of domains. To date, various fuzzy inference and computational intelligence techniques have been deployed to empower agricultural systems. Among others, the deployment of digital technologies to facilitate farming activities has been on the rise in recent years.

Our Special Issue titled “Applications of Machine Learning and Deep Learning in Agriculture” offers a venue for researchers and practitioners to share their experience on the evaluation and in-depth investigation of machine learning/deep learning and their applications in real life, with focus on the agriculture sector. We solicit research work to increase synergy among various communities, including machine learning, agricultural informatics, and recommender systems.

Topics of interest for the Special Issue include but are not limited to:

  • Applications of machine learning and deep learning techniques in agricultural systems;
  • Deep learning for recommender systems;
  • Deep learning for building expert systems in agriculture to support harvest and production;
  • Case studies of real-world implementations for expert systems;
  • Adversarial machine learning in agricultural systems: risks and countermeasures;
  • Reinforcement learning and applications in agricultural imaging;
  • Transfer learning;
  • Recommender systems for supporting smart farming.

Dr. Phuong T. Nguyen
Dr. Vito Walter Anelli
Guest Editors

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.

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. Informatics is an international peer-reviewed open access quarterly 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 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
  • transfer learning
  • agrucultural systems
  • expert systems
  • recommender systems

Published Papers (2 papers)

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Research

Article
AgroAId: A Mobile App System for Visual Classification of Plant Species and Diseases Using Deep Learning and TensorFlow Lite
Informatics 2022, 9(3), 55; https://doi.org/10.3390/informatics9030055 - 26 Jul 2022
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Abstract
This paper aims to assist novice gardeners in identifying plant diseases to circumvent misdiagnosing their plants and to increase general horticultural knowledge for better plant growth. In this paper, we develop a mobile plant care support system (“AgroAId”), which incorporates computer [...] Read more.
This paper aims to assist novice gardeners in identifying plant diseases to circumvent misdiagnosing their plants and to increase general horticultural knowledge for better plant growth. In this paper, we develop a mobile plant care support system (“AgroAId”), which incorporates computer vision technology to classify a plant’s [species–disease] combination from an input plant leaf image, recognizing 39 [species-and-disease] classes. Our method comprises a comparative analysis to maximize our multi-label classification model’s performance and determine the effects of varying the convolutional neural network (CNN) architectures, transfer learning approach, and hyperparameter optimizations. We tested four lightweight, mobile-optimized CNNs—MobileNet, MobileNetV2, NasNetMobile, and EfficientNetB0—and tested four transfer learning scenarios (percentage of frozen-vs.-retrained base layers): (1) freezing all convolutional layers; (2) freezing 80% of layers; (3) freezing 50% only; and (4) retraining all layers. A total of 32 model variations are built and assessed using standard metrics (accuracy, F1-score, confusion matrices). The most lightweight, high-accuracy model is concluded to be an EfficientNetB0 model using a fully retrained base network with optimized hyperparameters, achieving 99% accuracy and demonstrating the efficacy of the proposed approach; it is integrated into our plant care support system in a TensorFlow Lite format alongside the front-end mobile application and centralized cloud database. Finally, our system also uses the collective user classification data to generate spatiotemporal analytics about regional and seasonal disease trends, making these analytics accessible to all system users to increase awareness of global agricultural trends. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Deep Learning in Agriculture)
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Article
Computer Vision and Machine Learning for Tuna and Salmon Meat Classification
Informatics 2021, 8(4), 70; https://doi.org/10.3390/informatics8040070 - 19 Oct 2021
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Abstract
Aquatic products are popular among consumers, and their visual quality used to be detected manually for freshness assessment. This paper presents a solution to inspect tuna and salmon meat from digital images. The solution proposes hardware and a protocol for preprocessing images and [...] Read more.
Aquatic products are popular among consumers, and their visual quality used to be detected manually for freshness assessment. This paper presents a solution to inspect tuna and salmon meat from digital images. The solution proposes hardware and a protocol for preprocessing images and extracting parameters from the RGB, HSV, HSI, and L*a*b* spaces of the collected images to generate the datasets. Experiments are performed using machine learning classification methods. We evaluated the AutoML models to classify the freshness levels of tuna and salmon samples through the metrics of: accuracy, receiver operating characteristic curve, precision, recall, f1-score, and confusion matrix (CM). The ensembles generated by AutoML, for both tuna and salmon, reached 100% in all metrics, noting that the method of inspection of fish freshness from image collection, through preprocessing and extraction/fitting of features showed exceptional results when datasets were subjected to the machine learning models. We emphasize how easy it is to use the proposed solution in different contexts. Computer vision and machine learning, as a nondestructive method, were viable for external quality detection of tuna and salmon meat products through its efficiency, objectiveness, consistency, and reliability due to the experiments’ high accuracy. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Deep Learning in Agriculture)
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