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This work is licensed under a Creative Commons Attribution 4.0 International License.
Fruit Grade
Prof. Prof. Smita Thakare, Pallavi Jankiram Solanke, Bhagyashri Mahendra Pardeshi, Tanisha Ajay Lokhande, Parth Prabhanja Naik
DOI: 10.17148/IJARCCE.2026.15375
Abstract: Maintaining consistent fruit quality evaluation and establishing efficient market connectivity are major challenges in the modern agricultural supply chain. Traditional fruit grading methods rely heavily on manual inspection, which can lead to inconsistent results, human error, and inaccurate pricing. Such limitations often cause financial losses for farmers and vendors while also affecting the reliability of quality standards in the market.
The proposed SmartFruit β AI-powered Fruit Quality Analysis & Vendor Connect Platform introduces an intelligent solution that utilizes Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate fruit quality assessment. The system analyzes fruit images captured through a mobile or web-based application and classifies them into predefined quality categories such as Grade A, Grade B, and Grade C. Using image processing and deep learning models, the platform evaluates visual attributes including color consistency, ripeness level, surface texture, and visible defects to determine the overall quality of fruits.
In addition to automated grading, the platform integrates a vendor connectivity module that helps farmers identify nearby buyers or vendors, compare potential offers, and maintain digital records of their transactions. The application also provides user-friendly features such as text-based results, optional voice feedback, and historical analysis tracking to improve accessibility and usability.
By combining automated fruit grading with digital market connectivity, the SmartFruit platform enhances transparency in quality evaluation, reduces dependence on manual inspection, and supports efficient decision-making within the agricultural ecosystem. The system ultimately contributes to minimizing post-harvest losses and strengthening the direct connection between farmers and market stakeholders.
Keywords: Artificial Intelligence (AI), Machine Learning, Fruit Quality Classification, Computer Vision, Image Processing, Smart Agriculture Systems, Vendor Connectivity Platform, AI-Based Fruit Grading, Agricultural Supply Chain Management.
The proposed SmartFruit β AI-powered Fruit Quality Analysis & Vendor Connect Platform introduces an intelligent solution that utilizes Artificial Intelligence (AI) and Machine Learning (ML) techniques to automate fruit quality assessment. The system analyzes fruit images captured through a mobile or web-based application and classifies them into predefined quality categories such as Grade A, Grade B, and Grade C. Using image processing and deep learning models, the platform evaluates visual attributes including color consistency, ripeness level, surface texture, and visible defects to determine the overall quality of fruits.
In addition to automated grading, the platform integrates a vendor connectivity module that helps farmers identify nearby buyers or vendors, compare potential offers, and maintain digital records of their transactions. The application also provides user-friendly features such as text-based results, optional voice feedback, and historical analysis tracking to improve accessibility and usability.
By combining automated fruit grading with digital market connectivity, the SmartFruit platform enhances transparency in quality evaluation, reduces dependence on manual inspection, and supports efficient decision-making within the agricultural ecosystem. The system ultimately contributes to minimizing post-harvest losses and strengthening the direct connection between farmers and market stakeholders.
Keywords: Artificial Intelligence (AI), Machine Learning, Fruit Quality Classification, Computer Vision, Image Processing, Smart Agriculture Systems, Vendor Connectivity Platform, AI-Based Fruit Grading, Agricultural Supply Chain Management.
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How to Cite:
[1] Prof. Prof. Smita Thakare, Pallavi Jankiram Solanke, Bhagyashri Mahendra Pardeshi, Tanisha Ajay Lokhande, Parth Prabhanja Naik, βFruit Grade,β International Journal of Advanced Research in Computer and Communication Engineering (IJARCCE), DOI: 10.17148/IJARCCE.2026.15375
