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Agriculture
Special Issues
Application of Spectroscopy and Sensor Technology in Agricultural Products—Series II
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Application of Spectroscopy and Sensor Technology in Agricultural Products—Series II

A special issue of Agriculture (ISSN 2077-0472). This special issue belongs to the section "Agricultural Technology".

Deadline for manuscript submissions: closed (25 February 2024) | Viewed by 2583

Special Issue Editors

Dr. Ahmed Mustafa Rady

E-Mail Website
Guest Editor
1. Teagasc, The Agriculture and Food Development Authority, Dublin, D15 KN3K, Ireland
2. Food, Water, Waste Research Group, Faculty of Engineering, University of Nottingham, University Park Campus, Nottingham NG7 2RD, UK
Interests: postharvest technology; machine learning; nir spectroscopy; hyperspectral imaging; deep learning; postharvest engineering; machine vision; food quality; noninvasive sensors
Special Issues, Collections and Topics in MDPI journals
Dr. Ewa Ropelewska

E-Mail Website
Guest Editor
Fruit and Vegetable Storage and Processing Department, The National Institute of Horticultural Research, Konstytucji 3 Maja 1/3, 96-100 Skierniewice, Poland
Interests: image analysis; machine vision; deep learning; neural network; hyperspectral imaging; multispectral imaging; computed tomography; horticulture; fruit quality; vegetable quality; fruit and vegetable processing
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

As the current world populationis greater than 7 billion, more effort needs to be placed on reducing losses in the food chain, especially at the production, handling, storage, and transportation levels. Food losses can be as high as 19% in developing countries and 23% in developed countries. These losses not only affect the available food on the global market, but they also reflect wasted resources (i.e., soil, water, energy) and subsequently cause more greenhouse gas emissions. Digital technologies have emerged in the last two decades with applications in manufacturing automation, smart homes, and even driverless cars and trucks. Among such technologies, spectroscopic, color, gas, ultrasonic, and other sensors have been shown to have a significant capability to be utilized as non-invasive and/or rapid sensors for monitoring various quality aspects of agricultural commodities in a robust, reproducible, and accurate manner. Furthermore, such sensors have been recently implemented at a relatively lower cost to suit SME businesses. The significant advancement of the IoT and smart manufacturing facilities have provided further applications of non-invasive sensors for online quality evaluation and that can be integrated with cloud computing platforms. Over the last decade, there has been intensive research on improving machine learning algorithms that can be tremendous tools for high-dimensional data analysis, among which deep learning is an innovative, highly accurate, and deployable model that is accelerating the application of non-invasive sensors for the online quality evaluation of agricultural products.

This Special Issue of Agriculture targets a wide spectrum of original research and review studies focusing on the applications of optical, ultrasonic, and other sensors, along with machine learning algorithms, for the detection of the quality of agricultural products during the production, harvesting, handling, and storage stages.

Dr. Ahmed Mustafa Rady
Dr. Ewa Ropelewska
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. Agriculture 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

  • spectroscopy
  • image processing
  • computer vision
  • ultrasonic
  • sensor fusion
  • machine learning
  • deep learning
  • postharvest technology
  • handling
  • sorting
  • IOT
  • smart agriculture

Published Papers (2 papers)

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Research

22 pages, 11108 KiB  
Article
Remote Sensing Identification and Rapid Yield Estimation of Pitaya Plants in Different Karst Mountainous Complex Habitats
by Zhongfa Zhou, Ruiwen Peng, Ruoshuang Li, Yiqiu Li, Denghong Huang and Meng Zhu
Agriculture 2023, 13(9), 1742; https://doi.org/10.3390/agriculture13091742 - 01 Sep 2023
Cited by 1 | Viewed by 759
Abstract
The Pitaya industry is a specialty fruit industry in the mountainous region of Guizhou, China. The planted area in Guizhou reaches 7200 ha, ranking first in the country. At present, Pitaya planting lacks efficient yield estimation methods, which has a negative impact on [...] Read more.
The Pitaya industry is a specialty fruit industry in the mountainous region of Guizhou, China. The planted area in Guizhou reaches 7200 ha, ranking first in the country. At present, Pitaya planting lacks efficient yield estimation methods, which has a negative impact on the Pitaya downstream industry chain, stymying the constant growing market. The fragmented and complex terrain in karst mountainous areas and the capricious local weather have hindered accurate crop identification using traditional satellite remote sensing methods, and there is currently little attempt made to tackle the mountainous specialty crops’ yield estimation. In this paper, based on UAV (unmanned aerial vehicle) remote sensing images, the complexity of Pitaya planting sites in the karst background has been divided into three different scenes as complex scenes with similar colors, with topographic variations, and with the coexistence of multiple crops. In scenes with similar colors, using the Close Color Vegetation Index (CCVI) to extract Pitaya plants, the accuracy reached 92.37% on average in the sample sites; in scenes with complex topographic variations, using point clouds data based on the Canopy Height Model (CHM) to extract Pitaya plants, the accuracy reached 89.09%; and in scenes with the coexistence of multiple crops, using the U-Net Deep Learning Model (DLM) to identify Pitaya plants, the accuracy reached 92.76%. Thereafter, the Pitaya yield estimation model was constructed based on the fruit yield data measured in the field for several periods, and the fast yield estimations were carried out and examined for three application scenes. The results showed that the average accuracy of yield estimation in complex scenes with similar colors was 91.25%, the average accuracy of yield estimation in scenes with topographic variations was 93.40%, and the accuracy of yield estimation in scenes with the coexistence of multiple crops was 95.18%. The overall yield estimation results show a high accuracy. The experimental results show that it is feasible to use UAV remote sensing images to identify and rapidly estimate the characteristic crops in the complex karst habitat, which can also provide scientific reference for the rapid yield estimation of other crops in mountainous regions. Full article
(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products—Series II)
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11 pages, 1435 KiB  
Article
Distinguishing Seed Cultivars of Quince (Cydonia oblonga Mill.) Using Models Based on Image Textures Built Using Traditional Machine Learning Algorithms
by Ewa Ropelewska, Dorota E. Kruczyńska and Monika Mieszczakowska-Frąc
Agriculture 2023, 13(7), 1310; https://doi.org/10.3390/agriculture13071310 - 26 Jun 2023
Cited by 1 | Viewed by 1143
Abstract
Different cultivars of seeds may have different properties. Therefore, distinguishing cultivars may be important for seed processing and product quality. This study was aimed at revealing the usefulness of innovative models developed based on selected image textures built using traditional machine algorithms for [...] Read more.
Different cultivars of seeds may have different properties. Therefore, distinguishing cultivars may be important for seed processing and product quality. This study was aimed at revealing the usefulness of innovative models developed based on selected image textures built using traditional machine algorithms for cultivar classification of quince seeds. The quince seeds belonging to four cultivars ‘Uspiech’, ‘Leskovac’, ‘Bereczki’, and ‘Kaszczenko’ were considered. In total, 1629 image textures from different color channels for each seed were extracted from color images acquired using a flatbed scanner. Texture parameters were used to build models for a combined set of selected textures from all color channels, sets of selected textures from color spaces RGB, Lab, and XYZ, and individual color channels R, G, B, L, a, b, X, Y, and Z using algorithms from different groups. The most successful models were developed using the Logistic (group of Functions), IBk (Lazy), LogitBoost (Meta), LMT (Trees), and naïve Bayes (Bayes). The classification accuracy reached 98.75% in the case of a model based on a combined set of textures selected from images in all color channels developed using the Logistic algorithm. For most models, the greatest misclassification of cases was observed between seeds ‘Bereczki’ and ‘Kaszczenko’. The developed procedure can be used in practice to distinguish quince seeds in terms of a cultivar and avoid mixing seed cultivars with different properties intended for further processing. Full article
(This article belongs to the Special Issue Application of Spectroscopy and Sensor Technology in Agricultural Products—Series II)
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