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Horticulturae
Special Issues
Artificial Intelligence in Horticulture Production
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Artificial Intelligence in Horticulture Production

A special issue of Horticulturae (ISSN 2311-7524). This special issue belongs to the section "Protected Culture".

Deadline for manuscript submissions: 5 November 2024 | Viewed by 1536

Special Issue Editors

Dr. Md Parvez Islam

E-Mail Website
Guest Editor
Faculty of Agriculture and The United Graduate School of Agricultural Sciences, Ehime University, Matsuyama 790-8566, Ehime, Japan
Interests: horticulture; image processing; AI
Prof. Dr. Kenji Hatou

E-Mail Website
Guest Editor
Faculty of Agriculture and The United Graduate School of Agricultural Sciences, Ehime University, Matsuyama 790-8566, Ehime, Japan
Interests: horticulture; image processing; AI
Dr. Xudong Sun

E-Mail Website
Guest Editor
School of Mechatronics & Vehicle Engineering, East China Jiaotong University, Nanchang 330013, China
Interests: fruit optimal harvest date estimation; fruit quality assessment using spectroscopy and image

Special Issue Information

Dear Colleagues,

Today, the agricultural sector faces huge challenges due to local and global ecological and political factors. However, to feed a growing world population, new approaches are needed to optimize resource allocation for precision agriculture, predictable production planning to ensure food security, and autonomous solutions for harvesting, processing and marketing of agricultural products. In this context, data-driven AI offers a wide range of application areas such as crop monitoring to detect diseases, nutrient deficiencies, pests, yield prediction, and price prediction. The papers in this Special Issue on “Artificial Intelligence in Horticulture Production” will focus on basic and applied research targeting AI in all stages of agriculture. This Special Issue will cover a number of topics of interest, including but not limited to:

  • AI for smart farming and agriculture;
  • AI-assisted automation;
  • AI-assisted real-time IoT data analytics;
  • Computer vision in agriculture;
  • Spatial AI-based agricultural robotics;
  • AI-based soil and plant nutrient analysis;
  • AI-based crop monitoring;
  • AI-assisted intelligent irrigation for agriculture;
  • AI-assisted phenotyping and genotyping;
  • AI-assisted livestock health monitoring;
  • AI in food supply chain;
  • AI-assisted predictive analytics for agriculture;
  • DL for managing security in IoT data processing;
  • DL for IoT attack detection and prevention.

Dr. Md Parvez Islam
Prof. Dr. Kenji Hatou
Dr. Xudong Sun
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. Horticulturae is an international peer-reviewed open access monthly 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 2200 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

  • smart farming
  • automation IoT data
  • computer vision
  • agricultural robotics
  • crop monitoring
  • intelligent irrigation
  • health monitoring
  • food supply chain
  • phenotyping and genotyping

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

Published Papers (1 paper)

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Research

18 pages, 5188 KiB  
Article
Using Machine Learning Algorithms to Investigate the Impact of Temperature Treatment and Salt Stress on Four Forage Peas (Pisum sativum var. arvense L.)
by Onur Okumuş, Ahmet Say, Barış Eren, Fatih Demirel, Satı Uzun, Mehmet Yaman and Adnan Aydın
Horticulturae 2024, 10(6), 656; https://doi.org/10.3390/horticulturae10060656 - 20 Jun 2024
Cited by 3 | Viewed by 786
Abstract
The combination of high or low temperatures and high salt may cause significant harm to the yield, quality, and overall productivity of forage pea crops. The germination process, a crucial phase in the life cycle of forage peas, may be greatly influenced by [...] Read more.
The combination of high or low temperatures and high salt may cause significant harm to the yield, quality, and overall productivity of forage pea crops. The germination process, a crucial phase in the life cycle of forage peas, may be greatly influenced by varying temperature and salinity conditions. To comprehend the influence of these elements on the germination of forage peas, one must use many tactics, including the choice of resilient forage pea cultivars. The experiment aimed to evaluate the response of four forage pea cultivars (Arda, Ozkaynak, Taskent, and Tore) caused by various temperature (10 °C, 15 °C, and 20 °C) and salt (0, 5, 10, 15, and 20 dS m−1) conditions at the germination stage using multivariate analysis and machine learning methods. An observation of statistical significance (p < 0.01) was made regarding the variations between genotypes, temperature–salt levels, and the interaction of the observed factors: germination percentage (GP), shoot length (SL), root length (RL), fresh weight (FW), and dry weight (DW). The cultivar Tore had the best values for SL (1.63 cm), RL (5.38 cm), FW (1.10 g), and DW (0.13 g) among all the cultivars. On the other hand, the Ozkaynak cultivar had the highest value for GP (89.13%). The values of all of the parameters that were investigated decreased as the salt level rose, whereas the values increased when the temperature level increased. As a result, the Tore cultivar exhibited the highest values for shoot length, root length, fresh weight, and dry weight variables when exposed to a maximum temperature of 20 °C and a saline level of 0 dS m−1. It was determined that temperature treatment of fodder peas can reduce salt stress if kept at optimum levels. The effects of temperature and salt treatments on the germination data of several fodder pea cultivars were analyzed and predicted. Three distinct machine learning algorithms were used to create predictions. Based on R2 (0.899), MSE (5.344), MAPE (6.953), and MAD (4.125) measures, the MARS model predicted germination power (GP) better. The GPC model performed better in predicting shoot length (R2 = 0.922, MSE = 0.602, MAPE = 11.850, and MAD = 0.326) and root length (R2 = 0.900, MSE = 0.719, MAPE = 12.673, and MAD = 0.554), whereas the Xgboost model performed better in estimating fresh weight (R2 = 0.966, MSE = 0.130, MAPE = 11.635, and MAD = 0.090) and dry weight (R2 = 0.895, MSE = 0.021, MAPE = 12.395, and MAD = 0.013). The results of the research show that the techniques and analyses used can estimate stress tolerance, susceptibility levels, and other plant parameters, making it a cost-effective and reliable way to quickly and accurately study forage peas and related species. Full article
(This article belongs to the Special Issue Artificial Intelligence in Horticulture Production)
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