IoT-Enabled Steganography-Based Smart Agriculture Using Machine Learning Models in Industry 5.0

Abstract

Farmers are facing a lot of hurdles in cultivation and earning profit out of it. The advancements in technology are growing rapidly and can be used by the farmers to increase their yield. This work enables the use of artificial intelligence-enabled Industry 5.0 in the field of agriculture. The farmers can manage their farms by using smart IoT-enabled technologies in three phases. The first stage is farm management where they can manage planting time and harvest. The second stage is internet of things (IoT)-enabled monitoring in which IoT devices such as node MCU, soil moisture sensor, and DHT11 sensor are used to monitor soil moisture, air humidity, and temperature. These data are collected and transferred to the user in a secure manner using stenographic techniques. The third stage is the detection of crop diseases which helps the farmers to upload pictures of infected leaves using cell phones. The machine learning models are used to analyze steganographic data which is uploaded by the farmers and suggest treatments for plant illnesses with high precision.