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Authors = Vahid Farzand Ahmadi ORCID = 0000-0002-5345-2161

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15 pages, 2401 KiB  
Article
Comparison of Different Image Processing Methods for Segregation of Peanut (Arachis hypogaea L.) Seeds Infected by Aflatoxin-Producing Fungi
by Peyman Ziyaee, Vahid Farzand Ahmadi, Pourya Bazyar and Eugenio Cavallo
Agronomy 2021, 11(5), 873; https://doi.org/10.3390/agronomy11050873 - 29 Apr 2021
Cited by 17 | Viewed by 3611
Abstract
Fungi such as Aspergillus flavus and Aspergillus parasiticus are molds infecting food and animal feed, are responsible for aflatoxin contamination, and cause a significant problem for human and animal health. The detection of aflatoxin and aflatoxigenic fungi on raw material is a major [...] Read more.
Fungi such as Aspergillus flavus and Aspergillus parasiticus are molds infecting food and animal feed, are responsible for aflatoxin contamination, and cause a significant problem for human and animal health. The detection of aflatoxin and aflatoxigenic fungi on raw material is a major concern to protect health, secure food and feed, and preserve their value. The effectiveness of image processing, combined with computational techniques, has been investigated to detect and segregate peanut (Arachis hypogaea L.) seeds infected with an aflatoxin producing fungus. After inoculation with Aspergillus flavus, images of peanuts seeds were taken using various lighting sources (LED, UV, and fluorescent lights) on two backgrounds (black and white) at 0, 48, and 72 h after inoculation. Images were post-processed with three different machine learning tools: the artificial neural network (ANN), the support vector machine (SVM), and the adaptive neuro-fuzzy inference system (ANFIS) to detect the Aspergillus flavus growth on peanuts. The results of the study show that the combination of LED light and a white background with ANN had 99.7% accuracy in detecting fungal growth on peanuts 72 h from infection with Aspergillus. Additionally, UV lights and a black background with ANFIS achieve 99.9% accuracy in detecting fungal growth on peanuts 48 h after their infection with Aspergillus. Full article
(This article belongs to the Special Issue Sensing and Perception Systems for Situational Awareness of Agricultural Robotic Vehicles)
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11 pages, 1355 KiB  
Article
Development and Testing of a Low-Cost Belt-and-Roller Machine for Spheroid Fruit Sorting
by Vahid Farzand Ahmadi, Peyman Ziyaee, Pourya Bazyar and Eugenio Cavallo
AgriEngineering 2020, 2(4), 596-606; https://doi.org/10.3390/agriengineering2040040 - 4 Dec 2020
Cited by 6 | Viewed by 7807
Abstract
Sorting is one of the most critical factors in the marketing development of fruit and vegetable and should be performed without any damage to the product. This article reports results of the development and testing of a prototype of a low-cost mechanical spherical [...] Read more.
Sorting is one of the most critical factors in the marketing development of fruit and vegetable and should be performed without any damage to the product. This article reports results of the development and testing of a prototype of a low-cost mechanical spherical fruit sorter based on a belt-and-roller device built at the State University of Tabriz, Iran. The efficiency and damage effect of the prototype of the machine was tested at different sorting rates on apples (Red Delicious and Golden Delicious) and oranges. Performance tests indicated that the speed of the feeding belt and transporting belt as well as the spherical coefficient significantly affect the machine’s sizing performance and damages. The results of the test showed a 95.28% and 92.48% accuracy in sorting for Red Delicious and Golden Delicious, respectively, and 94.28% for orange. Furthermore, the machine sorts fruits without any significant damage. Full article
(This article belongs to the Special Issue Evaluation of New Technological Solutions in Agriculture)
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