Abstract: In order to meet the demands of the expanding population, agriculture plays a vital role in boosting food supply. Unfortunately, conventional techniques for identifying diseases and applying pesticides to crops are labor-intensive, slow, and frequently ineffective. We suggest improving a machine learning-based pest recognition and pesticide sprayer in order to address the aforementioned problems. The goal of this project is to use IOT and artificial intelligence technology to automate disease diagnosis and pesticide spraying procedures. For intruder detection and control, the robot makes use of an Arduino microcontroller, motors, motor drivers, a Bluetooth module, and a PIR sensor. We also employ machine learning models for plant disease identification that are available on Google Colab. This technique seeks to increase food security, decrease the need for physical labor, minimize the use of pesticides, and increase agricultural output.

Keywords: Machine Learning, Pest Recognition, Pesticides Spray, Google Colab, Internet of Things (IOT).

| DOI: 10.17148/IARJSET.2024.11561