AgriAid: Android-based Crop Disease Detection and Advisory System

• Author(s): Mohd Ayman Khan; Muhammad Umar Maniyar; Md Zulquar Nain; Tasaduque Ullah; Mrs. Mumtaj K P
• Paper ID: 1713011
• Page: 1490-1493
• Published Date: 20-12-2025
• Published In: Iconic Research And Engineering Journals
• Publisher: IRE Journals
• e-ISSN: 2456-8880
• Volume/Issue: Volume 9 Issue 6 December-2025

Abstract

Agriculture faces mounting threats from plant diseases that significantly reduce yields and incomes for smallholder farmers. AgriAid is an Android-based application that provides offline, on-device crop leaf disease diagnosis using multiple optimized TensorFlow Lite (TFLite) models. The application supports several crops and executes real-time inference on resource-constrained devices, delivering high accuracy and low latency under varied field conditions. This paper presents the system design, model integration approach, implementation details, and empirical performance metrics obtained through extensive testing.

Keywords

Crop disease detection; TensorFlow Lite; offline inference; mobile agriculture; Android application.

Citations

IRE Journals:
Mohd Ayman Khan, Muhammad Umar Maniyar, Md Zulquar Nain, Tasaduque Ullah, Mrs. Mumtaj K P "AgriAid: Android-based Crop Disease Detection and Advisory System" Iconic Research And Engineering Journals Volume 9 Issue 6 2025 Page 1490-1493 https://doi.org/10.64388/IREV9I6-1713011

IEEE:
Mohd Ayman Khan, Muhammad Umar Maniyar, Md Zulquar Nain, Tasaduque Ullah, Mrs. Mumtaj K P "AgriAid: Android-based Crop Disease Detection and Advisory System" Iconic Research And Engineering Journals, 9(6) https://doi.org/10.64388/IREV9I6-1713011