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Applied Sciences
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Machine Learning in Agricultural Informatization
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Machine Learning in Agricultural Informatization

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Agricultural Science and Technology".

Deadline for manuscript submissions: closed (20 July 2022) | Viewed by 18772

Special Issue Editor

Dr. Minjuan Wang

E-Mail Website
Guest Editor
College of Information and Electrical Engineering, China Agricultural University, Beijing 100083, China
Interests: image data processing; agricultural information technology

Special Issue Information

Dear Colleagues,

Agricultural machine learning is not a mysterious trick or magic, but a set of well-defined models that collect specific data and apply specific algorithms to achieve expected results. Accurate data sensing and processing are basic part of quantitative decision-making in smart agriculture management. Image sensing provides multi-dimensional information for agriculture detection, such as color, visible-near infrared spectroscopy, thermal radiation and 3D representation. The traditional way of analyzing these datasets focuses on the characteristics of color, morphology, texture, spectral reflection, etc. The limitations of sample mounts and extracted features always lead to problems such as insufficient noise reduction and the low accuracy of the recognition and detection models, especially for complex background changes and unknown samples. Deep learning (DL), a subset of machine learning approaches, emerged, and combined neural networks to extract and represent the high-level features of image. This could help to build reliable predictions of complex and uncertain phenomena in agriculture.

This Special Issue aims to explore the state of the art of the latest advances in the estimation of machine learning in the agricultural field. This will also cover studies that adapt existing algorithms to agriculture information, as well as literature reviews.

Dr. Minjuan Wang
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.

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. Applied Sciences is an international peer-reviewed open access semimonthly 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 2400 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
  • convolutional neural network
  • agricultural dataset
  • big data
  • agriculture detection

Published Papers (4 papers)

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Research

14 pages, 44471 KiB  
Article
A Hybrid Approach for the Detection and Classification of Tomato Leaf Diseases
by Maha Altalak, Mohammad Ammad Uddin, Amal Alajmi and Alwaseemah Rizg
Appl. Sci. 2022, 12(16), 8182; https://0-doi-org.brum.beds.ac.uk/10.3390/app12168182 - 16 Aug 2022
Cited by 8 | Viewed by 2354
Abstract
In this paper, we proposed a hybrid deep learning approach for detecting and classifying tomato plant leaf diseases early. This hybrid system is a combination of a convolutional neural network (CNN), convolutional attention module (CBAM), and support vector machines (SVM). Initially, the proposed [...] Read more.
In this paper, we proposed a hybrid deep learning approach for detecting and classifying tomato plant leaf diseases early. This hybrid system is a combination of a convolutional neural network (CNN), convolutional attention module (CBAM), and support vector machines (SVM). Initially, the proposed model can detect nine different tomato diseases but is not limited to this. The proposed system is tested using a database containing images of tomato leaves. The obtained results were very encouraging, giving us accuracy up to 97.2%, which can be improved with the improvement of learning processes. The proposed system is very efficient and lightweight, so the farmer can install it on any smart device having a digital camera and processing capabilities. With a bit of training, a farmer can detect any disease immediately, which will help him take timely pre-emptive action. Full article
(This article belongs to the Special Issue Machine Learning in Agricultural Informatization)
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20 pages, 2352 KiB  
Article
Smart Agriculture Applications Using Deep Learning Technologies: A Survey
by Maha Altalak, Mohammad Ammad uddin, Amal Alajmi and Alwaseemah Rizg
Appl. Sci. 2022, 12(12), 5919; https://0-doi-org.brum.beds.ac.uk/10.3390/app12125919 - 10 Jun 2022
Cited by 40 | Viewed by 8602
Abstract
Agriculture is considered an important field with a significant economic impact in several countries. Due to the substantial population growth, meeting people’s dietary needs has become a relevant concern. The transition to smart agriculture has become inevitable to achieve these food security goals. [...] Read more.
Agriculture is considered an important field with a significant economic impact in several countries. Due to the substantial population growth, meeting people’s dietary needs has become a relevant concern. The transition to smart agriculture has become inevitable to achieve these food security goals. In recent years, deep learning techniques, such as convolutional neural networks (CNN) and recurrent neural networks (RNN), have been intensely researched and applied in various fields, including agriculture. This study analyzed the recent research articles on deep learning techniques in agriculture over the previous five years and discussed the most important contributions and the challenges that have been solved. Furthermore, we investigated the agriculture parameters being monitored by the internet of things and used them to feed the deep learning algorithm for analysis. Additionally, we compared different studies regarding focused agriculture area, problems solved, the dataset used, the deep learning model used, the framework used, data preprocessing and augmentation method, and results with accuracy. We concluded in this survey that although CNN provides better results, it lacks in early detection of plant diseases. To cope with this issue, we proposed an intelligent agriculture system based on a hybrid model of CNN and SVM, capable of detecting and classifying plant leaves disease early. Full article
(This article belongs to the Special Issue Machine Learning in Agricultural Informatization)
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16 pages, 3032 KiB  
Article
Classification of Potato Varieties Drought Stress Tolerance Using Supervised Learning
by Dominika Boguszewska-Mańkowska, Bogdan Ruszczak and Krystyna Zarzyńska
Appl. Sci. 2022, 12(4), 1939; https://0-doi-org.brum.beds.ac.uk/10.3390/app12041939 - 12 Feb 2022
Cited by 6 | Viewed by 2599
Abstract
The presented study was aimed at investigating the variability for drought tolerance among potato cultivars. To achieve this, the stability of drought tolerance of potato cultivars under different water regime conditions was inspected during 11 years of consecutive experiments. The data on 50 [...] Read more.
The presented study was aimed at investigating the variability for drought tolerance among potato cultivars. To achieve this, the stability of drought tolerance of potato cultivars under different water regime conditions was inspected during 11 years of consecutive experiments. The data on 50 potato cultivars’ responses to drought stress, based on the morphological features of plants, i.e., leaf and stem mass and size of the assimilation area, have been collected. The tuber yield, as well as calculated plant tolerance indexes and Climatic Water Balance for each growing season, were analyzed. The studied cultivars were later assigned into one of three tolerance groups for soil drought. The highest linear relationship was found between the mass of leaves and stems and the tuber yield but was found too weak to raise any conclusions. Thus, the ensemble learning models have been evaluated and returned better performance results, and the final classifier is the implementation of extreme gradient boosting. The final classifier of the 96.7% accuracy, which used several measured potato parameters (Relative yield decrease, Stem mass, Maturity, Assimilation area, Leaves mass, Yield per plant, calculated Climatic water balance, and indices: MSTI and DSI) that could distinguish the different tolerance groups were evaluated in the study. Full article
(This article belongs to the Special Issue Machine Learning in Agricultural Informatization)
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11 pages, 2258 KiB  
Article
Segmenting 20 Types of Pollen Grains for the Cretan Pollen Dataset v1 (CPD-1)
by Nikos Tsiknakis, Elisavet Savvidaki, Sotiris Kafetzopoulos, Georgios Manikis, Nikolas Vidakis, Kostas Marias and Eleftherios Alissandrakis
Appl. Sci. 2021, 11(14), 6657; https://0-doi-org.brum.beds.ac.uk/10.3390/app11146657 - 20 Jul 2021
Cited by 3 | Viewed by 4244
Abstract
Pollen analysis and the classification of several pollen species is an important task in melissopalynology. The development of machine learning or deep learning based classification models depends on available datasets of pollen grains from various plant species from around the globe. In this [...] Read more.
Pollen analysis and the classification of several pollen species is an important task in melissopalynology. The development of machine learning or deep learning based classification models depends on available datasets of pollen grains from various plant species from around the globe. In this paper, Cretan Pollen Dataset v1 (CPD-1) is presented, which is a novel dataset of grains from 20 pollen species from plants gathered in Crete, Greece. The pollen grains were prepared and stained with fuchsin, in order to be captured by a camera attached to a microscope under a ×400 magnification. In addition, a pollen grain segmentation method is presented, which segments and crops each unique pollen grain and achieved an overall detection accuracy of 92%. The final dataset comprises 4034 segmented pollen grains of 20 different pollen species, as well as the raw data and ground truth, as annotated by an expert. The developed dataset is publicly accessible, which we hope will accelerate research in melissopalynology. Full article
(This article belongs to the Special Issue Machine Learning in Agricultural Informatization)
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