International Research Journal of Engineering and Technology (IRJET)
e-ISSN: 2395-0056
Volume: 11 Issue: 05 | May 2024
p-ISSN: 2395-0072
www.irjet.net
PREDICTION OF FRUIT MATURITY AND QUALITY Anuraj Sule #1, Aniket Chavan#2, Gaurav Pednekar#3, Himanshu Patil#4 Mr. Anas Dange Artificial Intelligence & Machine Learning, Universal College of Engineering Mumbai, Vasai, India ------------------------------------------------------------------------***--------------------------------------------------------------------Abstract— The accurate assessment of fruit maturity and INTRODUCTION quality is paramount in the agricultural industry to ensure optimal harvesting, storage, and distribution practices. This research proposes a novel approach leveraging advanced sensing technologies and machine learning algorithms for nondestructive fruit maturity and quality detection. By integrating multispectral imaging, near-infrared spectroscopy, and computer vision techniques, our methodology enables rapid and precise evaluation of key fruit attributes such as ripeness, firmness, sugar content, and internal quality parameters. In this study, we present a comprehensive framework encompassing data acquisition, preprocessing, feature extraction, and model development to facilitate real-time assessment of fruit maturity and quality. We employ a diverse dataset comprising various fruit types and maturity stages to train and validate our predictive models, ensuring robust performance across different scenarios. Furthermore, we explore the potential of deep learning architectures to enhance the accuracy and generalization capabilities of our detection system. The experimental results demonstrate the efficacy of our approach in accurately characterizing fruit maturity and quality attributes with high precision and reliability. The developed system exhibits promising performance in realworld applications, offering significant benefits to farmers, producers, and distributors by optimizing harvest timing, reducing post-harvest losses, and enhancing overall product quality. In conclusion, this research presents a valuable contribution to the field of agricultural technology, offering a cost-effective and efficient solution for fruit maturity and quality detection that can revolutionize current practices and improve the sustainability and efficiency of fruit production and distribution systems.
Keywords— Fruit maturity, Quality assessment, Non-
destructive testing, Sensing technologies, Machine learning, Multispectral imaging, Near-infrared spectroscopy, Computer vision, Ripeness detection, Firmness measurement, Sugar content analysis, Internal quality parameters, Data preprocessing, Feature extraction.
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Impact Factor value: 8.226
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The assessment of fruit maturity and quality is of fundamental importance in the agricultural industry, influencing key aspects of production, distribution, and consumer satisfaction. Accurate determination of when fruits are at their optimal ripeness stage and possess desired quality attributes such as firmness, sugar content, and overall freshness is essential for maximizing market value and minimizing post-harvest losses. Traditional methods of fruit evaluation often involve subjective human judgment or invasive sampling techniques, which are time-consuming, laborintensive, and may lead to inaccuracies in assessing fruit quality. In recent years, there has been a growing interest in the development of non-destructive and automated approaches for fruit maturity and quality detection, driven by advancements in sensing technologies and data analytics. These technologies offer the potential to revolutionize current practices by providing rapid, objective, and precise assessments of fruit attributes without compromising the integrity of the produce. Among the emerging techniques, multispectral imaging, near-infrared spectroscopy, and computer vision have shown great promise in enabling comprehensive characterization of fruit properties in a non-invasive manner. This research aims to contribute to this evolving field by proposing a novel methodology for fruit maturity and quality detection that integrates advanced sensing technologies with machine learning algorithms. By harnessing the power of multispectral imaging and near-infrared spectroscopy, coupled with sophisticated computer vision techniques, our approach seeks to provide farmers, producers, and distributors with a reliable and efficient tool for real-time evaluation of fruit attributes. By accurately determining factors such as ripeness, firmness, sugar content, and internal quality parameters, our system aims to optimize harvest timing, enhance product quality, and minimize post-harvest losses. In this paper, we present a comprehensive framework encompassing data acquisition, preprocessing, feature extraction, and model development for fruit maturity and quality detection. We describe the experimental setup, dataset collection, and methodology used for training and validating our
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