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International Journal of Advanced Engineering Application

ISSN: 3048-6807

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Machine Learning-Based Classification of Potato and Sweet Potato in Maharashtra's Agro Climatic Zones

Author(s):Vinit Jayeshbhai Patel1, Vaibhav Prakash Vasani2

Affiliation: 1,2Affiliated with KJ. Somaiya School of Engineering (formally known as K J Somaiya College of Engineering), Somaiya Vidyavihar University, Mumbai, India

Page No: 1-6

Volume issue & Publishing Year: Volume 2 Issue 7,July-2025

Journal: International Journal of Advanced Engineering Application (IJAEA)

ISSN NO: 3048-6807

DOI: https://doi.org/10.5281/zenodo.16723695

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Abstract:
The ever-growing market for organic vegetables insists on the use of automated systems which are not only efficient but also able to precisely differentiate between organic and non-organic vegetables. The paper at hand puts forward a machine learning-based methodology for categorizing vegetables into two types by gathering a novel dataset from the MAFCO Market in Vashi, Maharashtra. The dataset includes 2,000 pictures of potatoes and sweet potatoes, where 500 samples of each of the organic and inorganic varieties are featured. The new system uses the YOLOv11 classification model that is further supplemented by data augmentation methods for performance improvement and also a web application that allows instantaneous classification. The high accuracy in recognizing organic and inorganic vegetables, which is demonstrated by the experiments, can provide a solution that can be scaled for markets in various agro-climatic zones of Maharashtra.

Keywords: YOLOv11, vegetable classification, organic produce, agro-climatic zones,deep learning, Maharashtra agriculture.

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