학술논문

AI-based Leaf Disease Identification Robot using IoT Approach
Document Type
Conference
Source
2023 2nd International Conference on Edge Computing and Applications (ICECAA) Edge Computing and Applications (ICECAA), 2023 2nd International Conference on. :1375-1379 Jul, 2023
Subject
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Computing and Processing
Robotics and Control Systems
Productivity
Microcontrollers
Crops
Robot sensing systems
Feature extraction
Internet of Things
Artificial intelligence
Artificial Intelligence
Arduino uno
IOT
Image processing
Faster R-CNN
Image segmentation
Language
Abstract
Every area of the global economy has seen great advancements thanks to artificial intelligence, and agronomy is no exception. Modern agricultural farming faces great challenges in the cultivation of healthy crops. The “Internet of Things” is a system made up of actuators, sensors, or both that either directly or indirectly connect devices to the Internet. The development of the Internet of Things (IoT) can be used in smart farming to improve the standard of agriculture. The foundation of the Indian economy, agriculture, contributes to the country's overall economic growth. Yet, because of the usage of antiquated farming technology and the fact that individuals from rural areas now go to urban areas for more lucrative businesses rather than concentrating on agriculture, the obtained productivity is quite low compared to global standards. This artificial intelligence assists in increasing crop productivity and identifies or keeps track of crop illnesses. based on artificial intelligence to identify crop or leaf diseases, a robot or equipment has been created. It distinguishes or categorizes the plant as either disease-affected or unaffected. Image segmentation is a technique used to isolate the specific disease's affected area. This system's classification of diseased leaves offers farmers a better course of action. Faster feature collection, feature extraction, and illness classification methods based on R-CNN identify the disease afflicted.