In Recent time, with the utilization of Artificial Intelligence (AI), AI applications have proliferated across various domains where agricultural consumer electronics are no exception. These innovations have significantly enhanced the intelligence of agricultural processes, leading to increased efficiency and sustainability. This study introduces an intelligent crop yield prediction system that utilizes Random Forest (RF) classifier to optimize the usage of water based on environmental factors. By integrating lightweight machine learning with consumer electronics such as sensors connected inside the smart display devices, this work is aimed to amplify water management and promote sustainable farming practices. While focusing on the sustainable agriculture, the water usage efficiency in irrigation should be enhanced by predicting optimal watering schedules and it will reduce the environmental impact and support the climate resilient farming. The proposed lightweight model has been trained on real-time agricultural data with minimum memory resource in sustainability prediction and the model has achieved 90.1% accuracy in the detection of crop yield suitable for the farmland as well as outperformed the existing methods including AI-enabled IoT model with mobile sensors and deep learning architectures (89%), LoRa-based systems (87.2%), and adaptive AI with self-learning techniques (88%). The deployment of computationally efficient machine learning models like random forest algorithms will emphasis on real time decision making without depending on the cloud computing. The performance evaluation and effectiveness of the proposed method are estimated using the important parameter called prediction accuracy. The main goal of this parameter is to access how the AI model accurately predicts the irrigation needs based on the sensor data.
Dhanaraj et al. (Mon,) studied this question.