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Precision Agriculture with Deep and Machine Learning
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Precision Agriculture with Deep and Machine Learning

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

Deadline for manuscript submissions: closed (1 July 2023) | Viewed by 11048

Special Issue Editors

Dr. Mohamed Shehata

E-Mail Website
Guest Editor
Department of Bioengineering, Speed School of Engineering, University of Louisville, Louisville, KY 40292, USA
Interests: medical image analysis; artificial intelligence in medicine; deep learning; computer-aided diagnostics; precsion medicine; diagnostics and prognostic markers; bigdata in medicine
Special Issues, Collections and Topics in MDPI journals
Dr. Mahmoud Badawy

E-Mail
Guest Editor
Computer Science and Information Department, Applied College, Taibah University, Medinah 41461, Saudi Arabia
Interests: artificial intelligence (AI); machine learning; and deep learning; non-invasive computer-assisted diagnosis systems and computer vision; Internet of Things; image processing; distributed computing
Prof. Dr. Mostafa Elhosseini

E-Mail Website
Guest Editor
Computers and Control Systems Engineering, Faculty of Engineering, Mansoura University, Mansoura 35516, Egypt
Interests: artificial intelligence (AI); machine learning; deep learning; robotics; metaheuristics; computer-assisted diagnosis systems; computer vision; bioinspired optimization algorithms; smart systems engineering
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As a method of farm management, precision agriculture (PA) uses information technology (IT) to precisely provide plants and soil with their needs for optimum growth and yield. Profitability, sustainability, and environmental safety are the aims of PA. Precision agriculture heavily depends on specialized hardware, software, and IT services. Moreover, digital technology has boosted the industrialization of agriculture. Mechanization has ushered in Agriculture 4.0, where intensive agriculture now represents a major contributor to production efficiency. This includes GPS, Big Data, IoT, cloud computing, and image feature extraction.

This Special Issue gathers research and review articles on recent breakthroughs, technologies, solutions, applications, and emerging challenges in precision agriculture.

Potential topics include:

  • Deep learning for precision agriculture;
  • Crop yield prediction;
  • Weather prediction;
  • Plant disease detection;
  • Livestock management;
  • Site-specific nutrient management;
  • Consumer analysis;
  • Inventory management;
  • Pest and weed control;
  • Soil quality monitoring;
  • Management of supply chains;
  • Biosensors for precision agriculture;
  • Food security.

Dr. Mohamed Shehata
Dr. Mahmoud Badawy
Prof. Dr. Mostafa Elhosseini
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. Sensors 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 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

  • precision agriculture
  • pesticides
  • sustainability
  • machine learning
  • deep learning
  • weather prediction, irrigation management
  • crop yield prediction
  • environment
  • livestock
  • soil quality

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

22 pages, 3133 KiB  
Article
Multispectral Plant Disease Detection with Vision Transformer–Convolutional Neural Network Hybrid Approaches
by Malithi De Silva and Dane Brown
Sensors 2023, 23(20), 8531; https://doi.org/10.3390/s23208531 - 17 Oct 2023
Cited by 8 | Viewed by 4932
Abstract
Plant diseases pose a critical threat to global agricultural productivity, demanding timely detection for effective crop yield management. Traditional methods for disease identification are laborious and require specialised expertise. Leveraging cutting-edge deep learning algorithms, this study explores innovative approaches to plant disease identification, [...] Read more.
Plant diseases pose a critical threat to global agricultural productivity, demanding timely detection for effective crop yield management. Traditional methods for disease identification are laborious and require specialised expertise. Leveraging cutting-edge deep learning algorithms, this study explores innovative approaches to plant disease identification, combining Convolutional Neural Networks (CNNs) and Vision Transformers (ViTs) to enhance accuracy. A multispectral dataset was meticulously collected to facilitate this research using six 50 mm filter filters, covering both the visible and several near-infrared (NIR) wavelengths. Among the models employed, ViT-B16 notably achieved the highest test accuracy, precision, recall, and F1 score across all filters, with averages of 83.3%, 90.1%, 90.75%, and 89.5%, respectively. Furthermore, a comparative analysis highlights the pivotal role of balanced datasets in selecting the appropriate wavelength and deep learning model for robust disease identification. These findings promise to advance crop disease management in real-world agricultural applications and contribute to global food security. The study underscores the significance of machine learning in transforming plant disease diagnostics and encourages further research in this field. Full article
(This article belongs to the Special Issue Precision Agriculture with Deep and Machine Learning)
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14 pages, 881 KiB  
Article
Predicting Daily Aerobiological Risk Level of Potato Late Blight Using C5.0 and Random Forest Algorithms under Field Conditions
by Laura Meno, Olga Escuredo, Isaac K. Abuley and M. Carmen Seijo
Sensors 2023, 23(8), 3818; https://doi.org/10.3390/s23083818 - 8 Apr 2023
Cited by 4 | Viewed by 2143
Abstract
Late blight, caused by Phytophthora infestans, is a major disease of the potato crop with a strong negative impact on tuber yield and tuber quality. The control of late blight in conventional potato production systems is often through weekly application of prophylactic [...] Read more.
Late blight, caused by Phytophthora infestans, is a major disease of the potato crop with a strong negative impact on tuber yield and tuber quality. The control of late blight in conventional potato production systems is often through weekly application of prophylactic fungicides, moving away from a sustainable production system. In support of integrated pest management practices, machine learning algorithms were proposed as tools to forecast aerobiological risk level (ARL) of Phytophthora infestans (>10 sporangia/m3) as inoculum to new infections. For this, meteorological and aerobiological data were monitored during five potato crop seasons in Galicia (northwest Spain). Mild temperatures (T) and high relative humidity (RH) were predominant during the foliar development (FD), coinciding with higher presence of sporangia in this phenological stage. The infection pressure (IP), wind, escape or leaf wetness (LW) of the same day also were significantly correlated with sporangia according to Spearman’s correlation test. ML algorithms such as random forest (RF) and C5.0 decision tree (C5.0) were successfully used to predict daily sporangia levels, with an accuracy of the models of 87% and 85%, respectively. Currently, existing late blight forecasting systems assume a constant presence of critical inoculum. Therefore, ML algorithms offer the possibility of predicting critical levels of Phytophthora infestans concentration. The inclusion of this type of information in forecasting systems would increase the exactitude in the estimation of the sporangia of this potato pathogen. Full article
(This article belongs to the Special Issue Precision Agriculture with Deep and Machine Learning)
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17 pages, 2776 KiB  
Article
An Optimized Ensemble Deep Learning Model for Predicting Plant miRNA–IncRNA Based on Artificial Gorilla Troops Algorithm
by Walid Hamdy, Amr Ismail, Wael A. Awad, Ali H. Ibrahim and Aboul Ella Hassanien
Sensors 2023, 23(4), 2219; https://doi.org/10.3390/s23042219 - 16 Feb 2023
Cited by 3 | Viewed by 2355
Abstract
MicroRNAs (miRNA) are small, non-coding regulatory molecules whose effective alteration might result in abnormal gene manifestation in the downstream pathway of their target. miRNA gene variants can impact miRNA transcription, maturation, or target selectivity, impairing their usefulness in plant growth and stress responses. [...] Read more.
MicroRNAs (miRNA) are small, non-coding regulatory molecules whose effective alteration might result in abnormal gene manifestation in the downstream pathway of their target. miRNA gene variants can impact miRNA transcription, maturation, or target selectivity, impairing their usefulness in plant growth and stress responses. Simple Sequence Repeat (SSR) based on miRNA is a newly introduced functional marker that has recently been used in plant breeding. MicroRNA and long non-coding RNA (lncRNA) are two examples of non-coding RNA (ncRNA) that play a vital role in controlling the biological processes of animals and plants. According to recent studies, the major objective for decoding their functional activities is predicting the relationship between lncRNA and miRNA. Traditional feature-based classification systems’ prediction accuracy and reliability are frequently harmed because of the small data size, human factors’ limits, and huge quantity of noise. This paper proposes an optimized deep learning model built with Independently Recurrent Neural Networks (IndRNNs) and Convolutional Neural Networks (CNNs) to predict the interaction in plants between lncRNA and miRNA. The deep learning ensemble model automatically investigates the function characteristics of genetic sequences. The proposed model’s main advantage is the enhanced accuracy in plant miRNA–IncRNA prediction due to optimal hyperparameter tuning, which is performed by the artificial Gorilla Troops Algorithm and the proposed intelligent preying algorithm. IndRNN is adapted to derive the representation of learned sequence dependencies and sequence features by overcoming the inaccuracies of natural factors in traditional feature architecture. Working with large-scale data, the suggested model outperforms the current deep learning model and shallow machine learning, notably for extended sequences, according to the findings of the experiments, where we obtained an accuracy of 97.7% in the proposed method. Full article
(This article belongs to the Special Issue Precision Agriculture with Deep and Machine Learning)
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