An ISOA-ASVM-Based Smart Irrigation System: Integrating Improved Seagull Optimization with Adaptive SVM for AI-IoT Agriculture

Abstract

The dramatic changes in climate, the rising water shortage, and the rising demands of agricultural productivity have resulted in irrigation management as a burning issue in contemporary agriculture. In an attempt to curb these drawbacks, this paper introduces a combined Artificial Intelligence (AI) and Internet of Things (IoT)-based smart agriculture system to manage precision in controlling irrigation measures. The suggested design uses IoT sensors as a continuous monitoring of real-time environmental and soil conditions such as the moisture content of the soil, ambient temperature, humidity, and climatic conditions. These data are sent to a central processing unit and machine learning models are used to find the complex and dynamic patterns that enable a good prediction of the crop water needs. In order to improve the accuracy of the prediction and the efficiency of the system, the Improved Seagull Optimization Algorithm-Adaptive Support Vector Machine (ISOA-ASVM) model is introduced. The optimization algorithm is useful to tune the SVM parameters, enhancing the performance of generalization and minimizing the computational cost. The model is trained and tested using a dataset of 3000 records of the IoT-based sensor on agricultural fields that consisted of the main environmental and soil characteristics such as soil moisture, temperature, humidity, rainfall, light intensity, and soil pH. Minimum performance of the maximum normalization and feature selection are used to increase model stability, and generalization. The evaluation criterion is 10-fold cross-validation and performance is compared to that of the Random Forest, Naïve Bayes and KNN classifiers. The proposed ISOA-ASVM has a high predictive power and strong robustness with high accuracy of 99.3, precision of 96.1, recall of 97.6 and F1-score of 98.6. Low variance and consistent cross fold performance is confirmed by statistical analysis. The acquired AI-IoT system will facilitate automated control of irrigation, remote monitoring, and real-time decision-making and reduce the involvement of humans and operational expenses. The results affirm that the combination of smart machine learning models and IoT sensing infrastructure can be of great help in enhancing the water-use efficiency, crop productivity, and sustainability. This paper offers a scalable and efficient solution to smart irrigation systems and helps to build climate-resilient and resource-efficient smart agriculture.