Embedded system application development on Raspberry Pi 4 (alpha crop weed and disease detection)

• Main Author: Goh, Jun De
• Other Authors: Chong Yong Kim
• Format: Final Year Project
• Language: English
• Published: Nanyang Technological University 2023
• Subjects: Engineering::Electrical and electronic engineering
• Online Access: https://hdl.handle.net/10356/167008
• Institution: Nanyang Technological University

This paper presents the development of a smart farming application using Raspberry Pi to detect disease and weeds in cotton crops. The proposed system utilises deep learning and machine learning algorithms to analyse images of cotton plants captured by a camera attached to the Raspberry Pi. The system is trained using a custom dataset to recognize disease and weed patterns in cotton plants. The application also provides real-time notifications to farmers, allowing them to take immediate action to prevent further damage to the crop. The experimental results show that the proposed system has high accuracy in detecting disease and weeds in cotton crops. The smart farming application using Raspberry Pi offers an effective and efficient solution for disease and weed detection in agriculture, which can help farmers to optimise crop yield and reduce crop losses.