Authors : N. Muthukumaran

Volume/Issue : Volume 11 - 2026, Issue 7 - July

Google Scholar : https://tinyurl.com/2kfhzu79

Scribd : https://tinyurl.com/56p7vj85

DOI : https://doi.org/10.38124/ijisrt/26jul051

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : This article talks about an AI-driven automated vehicle farming bot that could be used for smart irrigation and controlling plant diseases. The project uses the Internet of Things, image processing, and machine learning to make crops more productive, use resources more efficiently, and be more sustainable. We use an ESP32 microcontroller with an ESP32 CAM module to take real pictures of plant leaves in the field to find diseases. After that, the pictures are looked at for plant diseases and other problems that might happen early in the infection. A machine-learning-based prediction model is used to correctly classify diseases. The system not only finds diseases, but it also uses sensors to keep an eye on the soil moisture level all the time, which makes the irrigation process easier. The controller figures out how much water is needed to keep the soil in the best condition for plant growth and to avoid both underwatering and watering stress. The system also lets farmers know about the health of their plants and the status of their irrigation in real time through cloud-based communication. So, this system's combination of automated disease detection and smart water management reduces the need for manual labour, makes better use of water, reduces crop loss, and promotes sustainable precision farming.

Keywords : Smart Agriculture, Internet of Things, Plant Disease Detection, Smart Irrigation, Image Processing, Machine Learning, ESP32 Microcontroller

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