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Deep Learning-Based Early Detection of Plant Diseases for Precision Agriculture

Students & Supervisors

Student Authors
Raif Tanjim
Bachelor of Science in Electrical & Electronic Engineering, FE
Tanvir Ahmed
Bachelor of Science in Electrical & Electronic Engineering, FE
Mobashwar Mostafa
Bachelor of Science in Electrical & Electronic Engineering, FE
Aftab Alam Asif
Bachelor of Science in Electrical & Electronic Engineering, FE
Supervisors
Abu Shufian
Lecturer, Faculty, FE
Md Sajid Hossain
Senior Assistant Professor, Faculty, FE
Muhammad Tanzeer Sayeed Joheb
Lecturer, Faculty, FE
Md. Ashiquzzaman
Associate Professor, Faculty, FE

Abstract

The objective of this research is to design and evaluate deep learning models, specifically CNN and ANN, for early crop disease detection and classification using leaf images. The models are compared based on accuracy, highlighting the importance of spatial feature extraction in CNN architectures. By developing an automated and reliable system, this work aims to support precision agriculture, assist farmers in disease identification, reduce crop losses, and improve food security through timely diagnosis.

Keywords

Crop Disease Detection Deep Learning CNN ANN Precision Agriculture

Publication Details

  • Type of Publication:
  • Conference Name: IEEE Region 10 TENSYMP 2026
  • Date of Conference: 29/06/2026 - 29/06/2026
  • Venue: IEEE Malaysia Section, Penang, Malaysia
  • Organizer: IEEE Malaysia Section
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