Hydroponic Plant Disease Detection using CNN ...

Hydroponic Plant Disease Detection using CNN and Autoencoders

Author Name : Atharva Uttarkar, Harsh Mane, Nimish Marathe, Akhilesh Choudhary, Pradnya Mehta, Dipti Tamboli

ABSTRACT Hydroponic farming depends on automated disease detection and management technologies to increase productivity and lower labor costs. This work presented here deals with the application of a computer-aided approach for state-of-the-art hydroponic plant pathology that focuses on recommending treatment automatically, detecting diseases early, and assessing the severity of diseases like powdery mildew and root rot. This cutting-edge technology maximizes speed and precision in diagnosis by merging environment sensors (for example, pH, and temperature) with image analysis and formulating a hybrid sickness detection system. This technology could communicate well with automated systems of water and nutrient delivery so that it could be conditioned in real-time with the right severity of sickness and recommended personalized treatment. Root rot is the most common disease in a hydroponic gardening system that can kill, severely damage, or hinder their growth.