A Survey on Fruit Ripeness Classification Based On Image Processing with Machine Learning

Abstract

Agriculture has a major role in the economic development of our country. Productive growth and high yield production of fruits is essential and required for the agricultural industry. Due to the lack of skilled workers, 30–35% of the harvested fruits is wasted. also, because of human perception subjectivity identification, classification and grading of fruits not done precisely. So, it is required to impose the automation system in the fruit industry. The machine learning techniques with adequate concepts of image processing have a great scope to provide intelligence for designing an automation system to differentiate the fruits according to its type, variety, matureness and intactness. Application of image processing has helped agriculture to improve yield estimation, disease detection, fruit sorting, irrigation and maturity grading. Image processing techniques can be used to reduce the time consumption and has made it cost efficient. In this paper, an automatic system is reviewed to identify the ripening stages of fruit from images. Various feature extraction is performed using different algorithm to get the low to high level features automatically and later classification is carried out using various machine learning algorithm to get ripening stages of fruit as predicted output.

Effectiveness of Deep Feature Extraction Algorithm in Determining the Maturity of Fruits: A Review

“…Usha Mittal and Nagnath Aherwadi investi-gated and presented an article on ML and DL fruit identification, classification, and quality detection techniques [3]. Charan G et al studied real-time fruit detection and classification methods using image processing and CNN [10]. In 2023, Jeisson Enrique Cueva Caro and Jorge Isaac Necochea-Chamorro conducted a systematic review on fruit identification using ML and DL in 2023 [11].…”

Tunicate Henry gas solubility optimization‐based deep residual network for fruit ripeness classification

“…Deep learning is considered a sophisticated technique for analyzing the images in precision agriculture all through harvesting. The convolution neural network (CNN) 6 is a major deep model that has been adapted for classifying the images 7,8 . The detection of fruit and estimation of ripeness is done using a deep model and is considered a major part of detecting the field growth.…”

“…The convolution neural network (CNN) 6 is a major deep model that has been adapted for classifying the images. 7,8 The detection of fruit and estimation of ripeness is done using a deep model and is considered a major part of detecting the field growth. Some of the techniques utilized deep learning for detecting the fruit industrially.…”