Machine Learning-Based Classification of Potato and Sweet Potato in Maharashtra's AgroClimatic Zones
Year: 2025
Type: article
Abstract: The ever-growing market for organic vegetables insists on the use of automated systems which are not onlyefficient but also able to precisely differentiate between organic and non-organic vegetables. The paper at hand puts forwarda machine learning-based methodology for categorizing vegetables into two types by gathering a novel dataset from theMAFCO Market in Vashi, Maharashtra. The dataset includes 2,000 pictures of potatoes and sweet potatoes, where 500samples of each of the organic and inorganic varieties are featured. The new system uses the YOLOv11 classification modelthat is further supplemented by data augmentation methods for performance improvement and also a web application thatallows instantaneous classification. The high accuracy in recognizing organic and inorganic vegetables, which isdemonstrated by the experiments, can provide a solution that can be scaled for markets in various agro-climatic zones ofMaharashtra.
Author: Vinit Jayeshbhai Patel
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W6893470754
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