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.

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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.

This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

Eggs are a nutritious and important food in human daily diet, which is considered as a protein source of food. The most acceptable index for evaluating egg quality is Haugh unit with two factors, i.e. the weight of intact egg and the height of broken egg’s albumin. Hauge unit has three classification: firm (higher than 72), reasonably firm (higher than 72), and weak (less than 60). Average results for Haugh unit on the first, fourth, eighth, twelfth, and sixteenth days (five eggs in each step) were 113.39, 91.47, 74.56, 72.04, and 64.14 respectively. On the first, fourth and eighth days, eggs were intact but the quality of the eggs decreases on the next days. This research aims to sort healthy eggs from others and swell the rate of sorting.