Artificial intelligence for evaluation of quality in fruit ripening

Registration ID: IJNRD_220023

Published ID: IJNRD2404793

DOI: Click Here to Get

Rakshitha K V , Bhavya R A , Ravi Kumar K , Jayasudha Y S , Navachethan A T

The ripening process of fruits and vegetables encompass ethylene gas, agricultural science, fruit quality analysis, image processing, Artificial Neural Networks (ANN), phytohormones, texture softening, flavor development, and controlled environment

In agricultural science, the quality analysis of fruits and vegetables were classified with differentiating color, shape, size and texture and further type of disease by human vision. However, it is crucial to analysis by the normal traditional method. The maturation phase in fruits is a crucial stage, leading to increased tenderness, sweetness, and palatability, driven by the acceleration of metabolic processes. During maturation, ethylene (C2H4), a gaseous plant hormone known as a phytohormone, plays a pivotal role. It facilitates the softening of the fruit's skin and converts complex polysaccharides into simpler sugars. However, ripe fruits become delicate and susceptible to damage, making handling and transportation challenging. Therefore, it is essential to carefully monitor the ripening process under controlled conditions, including temperature and humidity. Analytical techniques are required to assess the ripening process, which is the focus of researchers' efforts. The key aspect of this research is utilizing ethylene gas levels, ranging from 10 to 100 ppm, to induce fruit ripening, which can be detected and analyzed using alternative term. Image processing techniques and Artificial Neural Networks (ANN) algorithms are utilized. Furthermore, in the maturation process of vegetables, they achieve their desired flavor, quality, color, palatability, and other textural attributes. Apples, pears, bananas, and mangoes are among the fruits that emit ethylene during ripening. Ethylene is known to be responsible for texture changes, softening, color alterations, and other ripening- related processes. The monitoring of the ripening process involves employing image analysis supported by various AI methods. Our analysis involves image preprocessing, segmentation, and feature extraction steps, followed by further analysis using other artificial intelligence methods, to swiftly identify the ripening process and monitor ethylene gas concentrations in the ripening chambers to prevent overripening. The application of various algorithms to climacteric fruits yields highly accurate and efficient outcomes, facilitating prompt moderation and ensuring a short shelf life, thereby posing challenges in transportation and storage. These results benefit farmers and consumers by providing timely assistance in appropriate decision-making.

Published Paper   E-Certificate

Rakshitha K V, Bhavya R A, Ravi Kumar K, Jayasudha Y S, & Navachethan A T (April-2024). Artificial intelligence for evaluation of quality in fruit ripening. INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT, 9(4), h856-h861. https://ijnrd.org/papers/IJNRD2404793.pdf

Volume 9 Issue 4, April-2024

Pages : h856-h861

Paper Reg. ID: IJNRD_220023

Published Paper Id: IJNRD2404793

Downloads: 000121987

Research Area: Computer Science & Technology 

Country: CHIKKABALLAPURA, karnataka, India

Published Paper PDF: https://ijnrd.org/papers/IJNRD2404793.pdf

Published Paper URL: https://ijnrd.org/viewpaperforall?paper=IJNRD2404793

Journal Name: INTERNATIONAL JOURNAL OF NOVEL RESEARCH AND DEVELOPMENT(IJNRD)

ISSN: 2456-4184 | IMPACT FACTOR: 8.76 Calculated By Google Scholar | ESTD YEAR: 2016

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