SMART IRRIGATION USING TRANSFER LEARNING IN IOT-BASED AGRICULTURAL NETWORKS

Abstract

The management of water in agriculture is an important issue in contemporary agriculture, as the lack of water is growing, climatic changes are becoming apparent, and the volume of food production is increasing. The old method of irrigation usually is based on the fixed period or manual regulation resulting in the waste of water and the low optimization of crop production. To overcome these drawbacks, the present paper will suggest a transfer learningbased smart irrigation system to optimize the management of water in agricultural IoT-based networks. The suggested system incorporates distributed IoT sensors to monitor soil moisture, temperature, and humidity, among other environmental conditions continuously, and uses transfer learning to adapt the pre-trained deep learning models in order to make precise irrigation decisions with little agricultural data. Sensor data will be sent to a centralized platform where smart models are applied to compute the water needs of the crops and in real time dynamically turn the irrigation actuators. The experimental findings indicate that the proposed system has a great positive effect on the accuracy of irrigation and the amount of water used and the overall health of crops in comparison to the traditional methods of irrigation. Transfer learning combined with IoT allows converging models more quickly, more accurately, and scale to a wide range of agricultural scenarios, so the system becomes a viable and sustainable remedy to the precision agriculture and smart farming application.