Recognition and Classification of Food Grains, Fruits and Flowers Using Machine Vision

Abstract

In this paper, we have presented different methodologies devised for recognition and classification of images of agricultural/horticultural produce. A classifier based on BPNN is developed which uses the color, texture and morphological features to recognize and classify the different agricultural/horticultural produce. Even though these features have given different accuracies in isolation for varieties of food grains, mangoes and jasmine flowers, the combination of features proved to be very effective. The average recognition and classification accuracies using colour features are 87.5%, 78.4% and 75.7% for food grains, mango and jasmine flowers, respectively, and the average accuracies have increased to 90.8%, 80.2% and 85.8% for food grains, mangoes and jasmine flowers ,respectively, using texture features. The average accuracies have increased to 94.1%, 84.0% and 90.1% for food grains, mangoes and jasmine flowers, respectively. The results are encouraging and promise a good machine vision system in the area of recognition and classification of agricultural/horticultural produce.

<b>Recognition and classification of White Wholes (WW) grade cashew kernel using artificial neural networks

“…This is calculated by Equation 1 of each color component of an image (Savakar & Anami, 2009;Ganganagowdar, & Siddaramappa, 2014). …”

“…The Standard Deviation is the average distance from the mean of the overall perceived brightness and contrast of each color component in a cashew kernel image (Savakar & Anami, 2009;Ganganagowdar, & Siddaramappa, 2014).…”

“…b) The following algorithm 02 involved in obtaining the 24 color features (Savakar & Anami, 2009;Ganganagowdar, & Siddaramappa, 2011;Ganganagowdar, & Siddaramappa, 2014).…”

Automatic Classification of Fruit Defects based on Co-Occurrence Matrix and Neural Networks

“…Machine vision systems are successfully used for recognition of greenhouse cucumber fruit using computer vision [23]. A method for the classification and gradation of different grains (for a single grain kernel) such as groundnut, Bengal gram, Wheat etc., is described in [24]. The effect of foreign bodies on recognition and classification of food grains is given in [25].…”

Rapid Detection Method of Moldy Maize Kernels Based on Color Feature

“…At present, it has been widely used in food detection [8], such as food quality classification and food safety detection. Pearson et al have developed a classifier by imaging algorithms which can classify normal pistachio, pistachios with oily or dark stains, and pistachios having kernel defects [9].…”