Abstract-This paper reports involvement of image processing and machine vision technique to detect and count of fruits on-tree, in field condition, have been reviewed. In addition, this paper also associated with the grading of fruits in post-harvesting. Different types of algorithms are available for counting and to extract the feature of fruit characters by capturing the on-tree fruit image by any conventional RGB camera. With the help of this counting algorithm and feature extraction technique, fruit is detected and counted. This work also surveys grading method applied to the post-harvest fruits. Grading method involves: identification of mature & immature fruits, intact & diseased fruits and also predict the weight of the fruit from its shape. The grading of fruit can be done by using different types of the classifier. The main features, drawback and future prospective of previous work in this area are summarized.
In the era of the computer age, with the development of new technologies, the need to compute with accuracy is increasing. The natural approach for detection of the quality of fruits is done by the experts on the basis of the human eye. Automation of quality analysis of fruits is important in order to reduce human efforts and save time. The paper describes the recent development and application of image analysis and soft computing system in quality evaluation of products in the field of agriculture. Soft computing is a rapid, consistent and objective inspection technique, which has expanded into many diverse industries. Image processing can be used to detect the quality of fruit which includes extraction of morphological features. After feature extraction, feature vectors are formed on which K-Means clustering segmentation process is applied to form clusters. In this paper, we present the framework for Carica papaya grading using the Artificial Bee Colony algorithm (ABC) to classify the papaya fruits from digital images. Our initial experiment on the image features indicates that affected area, shape and textures could be used as the parameters for the ABC algorithm for classifying the papaya fruits into its respective grades. Finally, for papaya grading, a comparison between the performance of GUI using support vector machine (SVM), Naive bays classifier and fuzzy logic is done. In the classification process, an input papaya is classified into two categories of healthy and defected. In all grading steps, SVM classifier gives an accuracy of 93.5%, naive Bayes classifier gives 92% and fuzzy logic gives 86.04% respectively. Moreover, the accuracy of the proposed optimization algorithms including for different papaya fruits image databases is 94.04% respectively.
Papaya (Carica papaya) is a tropical fruit having profitable importance because of its high nutritive and medicinal value. India leads the world in papaya production with an annual yield of about 3 million tons [1]. Before collection of these fruits are graded according to their maturity. The major parameter which is differentiating between mature and immature fruit is its color. And also by counting individually mature and immature fruit which implies knowledge about the productivity of the plant. Image analysis is a technique to count the number of fruits on-Tree. In this paper, we present a method for detecting and counting mature and immature fruits from images taken with a tree. We have demonstrated that the proposed method is able to achieve an accuracy of 89.18% and 100% for detecting as well as counting of immature and mature fruit respectively.
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