Farmer Helper App

Status: published

Abstract

Chemical misuse in Indian agriculture
remains a critical issue due to illiteracy, ambiguous
pesticide labels, and lack of standard information sources.
This paper presents an intelligent, software-only mobile
solution—Farmer
Helper
App—that
identifies
agrochemical products from label images using Optical
Character Recognition (OCR) and Machine Learning
(ML). The system integrates image preprocessing, text
extraction, fuzzy string similarity, and semantic text
embedding to classify products with high accuracy. A
fully implemented Flutter frontend provides multilingual
UI, camera capture, offline database support, and dosage
visualization, while a Python-FastAPI backend performs
OCR and product identification. A formal mathematical
model defines the system’s computational flow, mappings,
and successful conditions. Experimental results
demonstrate high recognition accuracy and rapid
processing, proving the app’s effectiveness as a scalable,
low-cost digital assistant for farmers.

Authors

• Shivraj Ramesh Kakde — U.G. Student — shivrajkakde2022@gmail.com
• Prof. S. P. Gunjal — Associate Professor — somnathgunjal717@gmail.com
• Atish Madhukar Kharde — U.G. Student — atishkharde279@gmail.com
• Rushikesh Arun Mate — U.G. Student — rushikeshmate286@gmail.com
• Rohan Bhagwan Kale — U.G. Student — rohankale2244@gmail.com

Keywords: Smart Agriculture, OCR, Machine Learning, Semantic Matching, Agrochemical Identification, Flutter, FastAPI.

Download published PDF