Crop Disease Detection and an Automated Sprinkler System

Crop diseases pose one of the major challenges to agricultural productivity. Effective and timely treatment is necessary to prevent damage to the crops. The present project aims to develop an automated system to detect crop diseases and spray chemicals using image processing and machine learning approaches. A camera is placed to capture images of the leaves, which are processed to determine the condition of the crops, i.e., whether they are healthy, require fertilizer, or require pesticides. The Arduino Uno controller is programmed to operate a 4-channel relay module based on the classification of the image. The motors will be specifically designed to dispense fertilizers, pesticides, and to maintain the environment for healthy crops. The system does not use environmental sensors; instead, it relies on image processing for decision-making. The results of the experiments have proved that the system is effective in detecting crop diseases and can be used to automate the spraying of chemicals with high efficiency. The system is cost-effective and can be easily implemented, especially for agricultural purposes.