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: closed (15 April 2023) | Viewed by 16065

Special Issue Editors

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
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

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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 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

  • deep learning
  • transfer learning
  • agrucultural systems
  • expert systems
  • recommender systems

Published Papers (5 papers)

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Research

30 pages, 6660 KiB  
Article
Finding Good Attribute Subsets for Improved Decision Trees Using a Genetic Algorithm Wrapper; a Supervised Learning Application in the Food Business Sector for Wine Type Classification
by Dimitris C. Gkikas, Prokopis K. Theodoridis, Theodoros Theodoridis and Marios C. Gkikas
Informatics 2023, 10(3), 63; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics10030063 - 21 Jul 2023
Viewed by 1207
Abstract
This study aims to provide a method that will assist decision makers in managing large datasets, eliminating the decision risk and highlighting significant subsets of data with certain weight. Thus, binary decision tree (BDT) and genetic algorithm (GA) methods are combined using a [...] Read more.
This study aims to provide a method that will assist decision makers in managing large datasets, eliminating the decision risk and highlighting significant subsets of data with certain weight. Thus, binary decision tree (BDT) and genetic algorithm (GA) methods are combined using a wrapping technique. The BDT algorithm is used to classify data in a tree structure, while the GA is used to identify the best attribute combinations from a set of possible combinations, referred to as generations. The study seeks to address the problem of overfitting that may occur when classifying large datasets by reducing the number of attributes used in classification. Using the GA, the number of selected attributes is minimized, reducing the risk of overfitting. The algorithm produces many attribute sets that are classified using the BDT algorithm and are assigned a fitness number based on their accuracy. The fittest set of attributes, or chromosomes, as well as the BDTs, are then selected for further analysis. The training process uses the data of a chemical analysis of wines grown in the same region but derived from three different cultivars. The results demonstrate the effectiveness of this innovative approach in defining certain ingredients and weights of wine’s origin. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Deep Learning in Agriculture)
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15 pages, 10298 KiB  
Article
CerealNet: A Hybrid Deep Learning Architecture for Cereal Crop Mapping Using Sentinel-2 Time-Series
by Mouad Alami Machichi, Loubna El Mansouri, Yasmina Imani, Omar Bourja, Rachid Hadria, Ouiam Lahlou, Samir Benmansour, Yahya Zennayi and François Bourzeix
Informatics 2022, 9(4), 96; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics9040096 - 30 Nov 2022
Cited by 4 | Viewed by 2189
Abstract
Remote sensing-based crop mapping has continued to grow in economic importance over the last two decades. Given the ever-increasing rate of population growth and the implications of multiplying global food production, the necessity for timely, accurate, and reliable agricultural data is of the [...] Read more.
Remote sensing-based crop mapping has continued to grow in economic importance over the last two decades. Given the ever-increasing rate of population growth and the implications of multiplying global food production, the necessity for timely, accurate, and reliable agricultural data is of the utmost importance. When it comes to ensuring high accuracy in crop maps, spectral similarities between crops represent serious limiting factors. Crops that display similar spectral responses are notorious for being nearly impossible to discriminate using classical multi-spectral imagery analysis. Chief among these crops are soft wheat, durum wheat, oats, and barley. In this paper, we propose a unique multi-input deep learning approach for cereal crop mapping, called “CerealNet”. Two time-series used as input, from the Sentinel-2 bands and NDVI (Normalized Difference Vegetation Index), were fed into separate branches of the LSTM-Conv1D (Long Short-Term Memory Convolutional Neural Networks) model to extract the temporal and spectral features necessary for the pixel-based crop mapping. The approach was evaluated using ground-truth data collected in the Gharb region (northwest of Morocco). We noted a categorical accuracy and an F1-score of 95% and 94%, respectively, with minimal confusion between the four cereal classes. CerealNet proved insensitive to sample size, as the least-represented crop, oats, had the highest F1-score. This model was compared with several state-of-the-art crop mapping classifiers and was found to outperform them. The modularity of CerealNet could possibly allow for injecting additional data such as Synthetic Aperture Radar (SAR) bands, especially when optical imagery is not available. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Deep Learning in Agriculture)
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17 pages, 2121 KiB  
Article
Machine Learning Applied to Tree Crop Yield Prediction Using Field Data and Satellite Imagery: A Case Study in a Citrus Orchard
by Abdellatif Moussaid, Sanaa El Fkihi, Yahya Zennayi, Ouiam Lahlou, Ismail Kassou, François Bourzeix, Loubna El Mansouri and Yasmina Imani
Informatics 2022, 9(4), 80; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics9040080 - 08 Oct 2022
Cited by 3 | Viewed by 3045
Abstract
The overall goal of this study is to define an intelligent system for predicting citrus fruit yield before the harvest period. This system uses a machine learning algorithm trained on historical field data combined with spectral information extracted from satellite images. To this [...] Read more.
The overall goal of this study is to define an intelligent system for predicting citrus fruit yield before the harvest period. This system uses a machine learning algorithm trained on historical field data combined with spectral information extracted from satellite images. To this end, we used 5 years of historical data for a Moroccan orchard composed of 50 parcels. These data are related to climate, amount of water used for irrigation, fertilization products by dose, phytosanitary treatment dose, parcel size, and root-stock type on each parcel. Additionally, two very popular indices, the normalized difference vegetation index and normalized difference water index were extracted from Sentinel 2 and Landsat satellite images to improve prediction scores. We managed to build a total dataset composed of 250 rows, representing the 50 parcels over a period of 5 years labeled with the yield of each parcel. Several machine learning algorithms were tested with the necessary parameter optimization, while the orthonormal automatic pursuit algorithm gave good prediction scores of 0.2489 (MAE: Mean Absolute Error) and 0.0843 (MSE: Mean Squared Error). Finally, the approach followed in this study shows excellent potential for fruit yield prediction. In fact, the test was performed on a citrus orchard, but the same approach can be used on other tree crops to achieve the same goal. Full article
(This article belongs to the Special Issue Applications of Machine Learning and Deep Learning in Agriculture)
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42 pages, 19654 KiB  
Article
AgroAId: A Mobile App System for Visual Classification of Plant Species and Diseases Using Deep Learning and TensorFlow Lite
by Mariam Reda, Rawan Suwwan, Seba Alkafri, Yara Rashed and Tamer Shanableh
Informatics 2022, 9(3), 55; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics9030055 - 26 Jul 2022
Cited by 8 | Viewed by 4115
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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20 pages, 5359 KiB  
Article
Computer Vision and Machine Learning for Tuna and Salmon Meat Classification
by Erika Carlos Medeiros, Leandro Maciel Almeida and José Gilson de Almeida Teixeira Filho
Informatics 2021, 8(4), 70; https://0-doi-org.brum.beds.ac.uk/10.3390/informatics8040070 - 19 Oct 2021
Cited by 6 | Viewed by 4133
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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