Proposal of model for prediction of grape processing and spraying time by using IoT smart agriculture sensor data

Abstract

The grape industry's impact on agriculture and the economy requires precise forecasting for processing and spraying schedules to optimize production. This article introduces an innovative IoT-based model for predicting optimal timings in grape processing and spraying. By integrating real-time environmental and viticultural data, the model improves decision-making, enhancing product quality, reducing energy consumption, and increasing operational efficiency. Crucially, SARIMA predictive algorithms forecast parameters like temperature, humidity, wind speed, and air pressure. This comprehensive model transforms the grape industry, offering advanced decision support and promoting sustainable, resource-efficient production. The research signals a potential shift to precision agriculture, balancing economic viability with environmental stewardship in grapes.

Low-Traffic Aware Hybrid MAC (LTH-MAC) Protocol for Wireless Sensor Networks

“…This includes data transmission models leveraging cross-layer design for IoT, leading to significant energy savings and reduced overhead [9]. In smart agriculture, sensor data optimization improves efficiency [10]. Furthermore, research on multi-channel scheduling and routing in wireless mesh networks demonstrates reduced interference and lower energy consumption [11].…”