Optimized smart farming using WSN–IoT and multi-strategy improved zebra optimization algorithm-MIZOA

Abstract

The survival of the human community depends on the agricultural industry. Various measures have been implemented to boost agricultural productivity. But frequent pest infestations and harsh environmental conditions are the main causes of agricultural loss. In a scenario like this, combining cutting-edge IoT technologies, such as advanced sensors, could boost agricultural productivity and minimize financial loss. Indian agriculture’s problems with low production, wasteful resource use, and climate variability have given rise to smart farming as a potential remedy. An optimized smart farming framework for India is presented in this research, utilizing a Several Strategies enhanced Zebra Optimization Algorithm (MIZOA) in combination with an Internet of Things (IoT)architecture of the Wireless Sensor Network (WSN). An IoT-based smart agriculture monitoring system that uses wireless sensor networks (WSNs) and predictive analytics to track important environmental elements The system makes proactive decisions for the best fertilization, irrigation, and pest control by using machine learning techniques for predictive analytics and WSNs to collect data from dispersed sensor nodes machine learning techniques for predictive analytics and WSNs *-2`to collect data from dispersed sensor nodes. The Improved Zebra Optimization Algorithm with Multiple Strategies (MIZOA), an enhanced version of the ZOA, is proposed in this study to overcome these issues. Furthermore, the predictive analytics component forecasts environmental conditions with a 94 % accuracy rate, which helps with timely interventions and reduces crop stress. Scalability for a range of agricultural applications, such as precision farming and greenhouse monitoring, is provided by this Internet of Things-based technology, which also promotes sustainable farming methods.