Artificial Neural Network-Based Image Analysis for Evaluation of Quality Attributes of Agricultural Produce

Abstract

The present study aimed to apply artificial neural networking for quantification of quality attributes of agricultural commodity based on color and size. Three feed forward neural network models (NN) were developed: first for conversion of RGB to L*, a* and b* values (NN1), second for identification of ripening stages of tomato and third for correlating projection area/size of tomato with weight. Results showed that NN1 was able to convert RGB to L*, a* and b* values with accuracy percentage of 99%. The best results showed with excellent abilities were at 30 hidden units with R2 of 0.9980 and mean squared error (MSE) of 0.00021, whereas NN2 classified tomatoes in three ripening stages with an accuracy of 96%, with 30 hidden neurons and a 100% classification was performed when a threshold value of 0.7 was used. In addition, NN3 was able to correlate area with weight with an accuracy of 99%, with three hidden neurons. Practical Applications Computer vision system‐based measurement of color and projection areas has been considered for the assessment of tomato quality. Image processing, a nondestructive method for evaluation of quality attributes of agricultural produce, serves as a best tool for measurement of size and color of nonuniform products. The color of whole surface can be evaluated using image analysis. Image processing was used as the basic tool and their functional relationships are expressed in terms of artificial neural network model.

Development of an Intelligent Imaging System for Ripeness Determination of Wild Pistachios

“…Similarly, QDA has been used by scholars to classify the ripeness levels of apricot (CCR = 92.3% using R, G, B channels, gray-scale, L*, a*, and b* color space) [ 58 ], and persimmon (CCR = 90.2% RGB + L* a* b* color space) [ 28 ]. ANN has also been implemented by scientists to classify the ripeness levels of mulberry (CCR = 96% using various color spaces [ 34 ]), banana (CCR = 96% using RGB color space) [ 60 ], tomato (CCR = 96% using L* a* b* color space) [ 30 ], and watermelon (CCR = 86.51% using YCbCr color space) [ 61 ]. Compared to the aforementioned works, our study confirms the reliability of visible imaging and image processing in identifying the ripeness stages of a new fruit (wild pistachio).…”

“…In this regard, scholars have explored various tools such as near-infrared spectroscopy [ 16 , 17 , 18 , 19 ], or imaging techniques [ 20 , 21 , 22 , 23 ] to predict the ripeness levels of various agriproducts and/or to evaluate their quality parameters [ 24 , 25 , 26 ]. For example, the maturity of persimmon blueberry [ 27 , 28 , 29 ], tomato [ 30 ], apple [ 31 , 32 ], citrus [ 33 ], mulberry [ 34 ], and oil palm fruit [ 35 ] have been estimated using imaging and machine vision algorithms. Among the various spectral bands that can be explored in machine vision systems (such as visible, near-infrared, nuclear magnetic resonance, X-ray, and gamma-ray [ 36 , 37 , 38 , 39 , 40 ], the visible imaging range has been identified to be the most affordable.…”

Deep Learning based Image Processing Solutions in Food Engineering: A Review

“…In (Rafiq et al, 2016), authors used Artificial Neural Network (ANN) for quality evaluation of tomatoes based on color and size features. Authors also design an image acquisition system to serve the purpose.…”

Classification of white rice grain quality using ANN: a review

“…According to research done by [23], shows that ANN classifiers can recognize the grain based on its size and shape with an average accuracy of 98.76% and 96.67%, respectively. Next, [24] used ANN as a classifier and the percentage accuracy gives more than 96% and according to [25], the performance can reach up to 99% using ANN as classifier.…”