Neural Network-based Techniques for Classifying IoT-Enabled Smart Irrigation Data in Agriculture

– Conventional farming has supported human
advancement for a long time, but humanity will need to start
using smarter, more effective farming techniques because of
rising population constraints and water scarcity. Agriculture
4.0 has arisen in this setting, improving resource management
through the use of cutting-edge technology like crop health
monitoring and remote sensing. Using SIS, or Smart
Irrigation Data in Agriculture, to maximise water efficiency is
a crucial part of this transition. In order to enhance SIS
prediction and classification, this work suggests a hybrid
model called CNRN-AHB. There are two parts to the model's
operation: preprocessing, which uses data refinement
techniques to make sure the input is high-quality, and
training, which uses a combination of CNN and RNN
networks optimised by the Adaptive Honey Badger algorithm.
The model's prediction capabilities are improved by the
hybrid technique, which allows it to successfully distinguish
between exudates and non-exudates. The CNRN-AHB model
outperforms current state-of-the-art methods, according to
analytical data, which demonstrate an accuracy of about 95.24
percent in classification and prediction. Contributing to
sustainable farming and efficient water management within
modern agriculture, this research highlights the significance
of intelligent data-driven strategies in smart irrigation.