On Tree Detection, Counting & Post- Harvest grading of fruits Based on Image Processing and Machine Learning Approach-A Review

Abstract

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.

Quantification of Pinus pinea L. Pinecone Productivity Using Machine Learning of UAV and Field Images

“…Numerous studies have been developed along the lines of tree crown delineation at different spatial scales, including UAVs [7,8], yet the very fine detail required for the segmentation of pinecones and other fruits using UAVs is relatively recent [9][10][11]. Malik et al [9] were successful with RGB images from UAVs but had the benefit of segmenting bright orange fruit from mostly just green leaves and used a simpler k-means segmentation statistical approach.…”

“…Likewise, Qureshi et al [10] also achieved good results, using a combined k-nearest neighbor pixel classification and contour segmentation and support vector approaches but on more colorfully distinct mango tree canopies. Sethy et al [11] present a thoughtful review of several statistical advancements focused on more colorful fruits rather than pinecones. In many cases, the counting of expertly pruned commercial fruit trees bearing easily distinguished fruits has proven to be less of a challenge than counting pinecones in forest plantations.…”

Development of 360-degree imaging system for fresh fruit bunch (FFB) identification

“…The benefits of utilizing these techniques are because of its simplicity, non-destructive and modest techniques that do not require any complex apparatus for the process (Sinecen et al, 2014). Sethy et al (2017) conducted a study regarding image processing and machine vision technique to detect and count of fruits on-tree directly in field condition. Numerous types of algorithms are available for counting and extracting the features of fruit characters using RGB camera to capture on-tree fruit images.…”

Apricot Variety Classification Using Image Processing and Machine Learning Approaches

“…Nowadays, computer sciences are getting more and more involved in agricultural and food science to make a decision based on estimated or actual parameters named as a feature [6]- [8]. Various artificial intelligence methods including machine vision and soft computing techniques have vast applications in fruit grading and to provide a higher quality product at the consumer end [9]- [11]. Image processing has been utilized as effective tools for measuring external features of fruits and plants such as color intensity, color homogeneity, bruises, size, shape, and stem identification [12], [13].…”