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Agronomy
Special Issues
Innovation of Intelligent Detection and Pesticide Application Technology for Horticultural...
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Innovation of Intelligent Detection and Pesticide Application Technology for Horticultural Crops

A special issue of Agronomy (ISSN 2073-4395). This special issue belongs to the section "Precision and Digital Agriculture".

Deadline for manuscript submissions: 30 September 2024 | Viewed by 1213

Special Issue Editors

Dr. Hongxing Peng

E-Mail Website
Guest Editor
College of Mathematics and Informatics, South China Agricultural University, Guangzhou 510642, China
Interests: machine vision; artificial intelligence; intelligent agriculture; agricultural robots
Dr. Yuanyuan Shao

E-Mail Website
Guest Editor
College of Mechanical and Electronic Engineering, Shandong Agricultural University, Shandong 271002, China
Interests: intelligent agriculture; agricultural product detection; hyperspectral image processing

Special Issue Information

Dear Colleagues,

Intelligent detection and pesticide application technologies have always been key areas of research in horticultural crop production. With the development of technology and the need for precise agriculture, intelligent detection and pesticide application technologies have become increasingly important in terms of solving the problems of agricultural production, such as ensuring crop yield and quality, reducing pesticide usage, and protecting the environment. This topic has attracted widespread attention from scholars worldwide.

The aim of this Special Issue is to collect and publish cutting-edge research regarding the intelligent detection and pesticide application technologies used for horticultural crops. We aim to provide a platform for scholars to share their experiences, ideas, and latest research results. The scope of this Special Issue includes, but is not limited to, the following topics:

  • Intelligent detection technology for horticultural crop diseases, pests, and weeds;
  • Pesticide application technology for horticultural crops;
  • Numerical simulation and optimized design of pesticide applications;
  • Evaluation methods and standards for pesticide residue in horticultural products;
  • Intelligent agriculture;
  • Agricultural product detection;
  • Hyperspectral image processing;
  • Machine vision;
  • Artificial intelligence.

This Special Issue welcomes high-quality papers related to the intelligent detection and pesticide application technologies used for horticultural crops. The papers should be original works not yet published elsewhere or review articles summarizing relevant research progress in this field.

Dr. Hongxing Peng
Dr. Yuanyuan Shao
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. Agronomy 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 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

  • intelligent detection
  • precision agriculture
  • machine vision
  • hyperspectral image processing
  • pesticide application
  • agricultural robots
  • agricultural big data
  • agricultural product quality and safety

Published Papers (1 paper)

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Research

16 pages, 3571 KiB  
Article
Detection and Analysis of Chili Pepper Root Rot by Hyperspectral Imaging Technology
by Yuanyuan Shao, Shengheng Ji, Guantao Xuan, Yanyun Ren, Wenjie Feng, Huijie Jia, Qiuyun Wang and Shuguo He
Agronomy 2024, 14(1), 226; https://0-doi-org.brum.beds.ac.uk/10.3390/agronomy14010226 - 21 Jan 2024
Viewed by 902
Abstract
The objective is to develop a portable device capable of promptly identifying root rot in the field. This study employs hyperspectral imaging technology to detect root rot by analyzing spectral variations in chili pepper leaves during times of health, incubation, and disease under [...] Read more.
The objective is to develop a portable device capable of promptly identifying root rot in the field. This study employs hyperspectral imaging technology to detect root rot by analyzing spectral variations in chili pepper leaves during times of health, incubation, and disease under the stress of root rot. Two types of chili pepper seeds (Manshanhong and Shanjiao No. 4) were cultured until they had grown two to three pairs of true leaves. Subsequently, robust young plants were infected with Fusarium root rot fungi by the root-irrigation technique. The effective wavelength for discriminating between distinct stages was determined using the successive projections algorithm (SPA) after capturing hyperspectral images. The optimal index related to root rot between each normalized difference spectral index (NDSI) was obtained using the Pearson correlation coefficient. The early detection of root rot illness can be modeled using spectral information at effective wavelengths and in NDSI, together with the application of partial least squares discriminant analysis (PLS-DA), least squares support vector machine (LSSVM), and back-propagation (BP) neural network technology. The SPA-BP model demonstrates outstanding predictive capabilities compared with other models, with a classification accuracy of 92.3% for the prediction set. However, employing SPA to acquire an excessive number of efficient wave-lengths is not advantageous for immediate detection in practical field scenarios. In contrast, the NDSI (R445, R433)-BP model uses only two wavelengths of spectral information, but the prediction accuracy can reach 89.7%, which is more suitable for rapid detection of root rot. This thesis can provide theoretical support for the early detection of chili root rot and technical support for the design of a portable root rot detector. Full article
(This article belongs to the Special Issue Innovation of Intelligent Detection and Pesticide Application Technology for Horticultural Crops)
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