Investigation of Deep Learning Methods for Disease Detection in Cotton Fields using UAV Imagery
Year: 2025
Type: conference-paper
Abstract: — Cotton, an important crop for growing textile sector, has been in decline with constant trends. This research primarily focuses on aligning with an improved system for detecting and classifying diseases in cotton fields using deep learning techniques applied to Unmanned Aerial Vehicle (UAV) imagery. As UAV deals with nonlinear behavior in flight control and detects disease in cotton field using UAV Imagery constantly poses serious challenges. The challenges include, Image noise, camera motion problem, pictorial distortions and issue of lighting conditions at various locations. The UAV is usually aimed at flying over the field to gather data and make sure that the image gather has better resolution to make use for disease detection in larger areas. Preprocessing is often necessary if they need to incorporate all sectors of working in different conditions. The earlier data in this regard is useful to preprocess. This study also targets improving existing methods of disease detection for informed pest management and sustainable agriculture practices.
Authors: Anwar Iqbal, Kunwar Faraz Ahmad Khan, Shahbaz Khan, Syed Irtiza Ali Shah, Zartasha Mustansar, Muhammad Azhar Javed
Language: English
Topic: Smart Agriculture and AI
Subfield: Plant Science
Domain: Life Sciences
SDG: Zero hunger
Open Access status: green
OpenAlex ID: W4412405525