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The rapid advancement in communication technology has sparked a transformative wave across various domains, significantly enhancing comfort and convenience in daily life. Addressing the escalating global demand for food, coupled with the need to alleviate the efforts of farmers, technology, particularly the Internet of Things (IoT), has emerged as a pivotal force. Precisely predicting variations in climatestrictures, ground conditions, and dirt attributes has emerged as a formidable challenge in the realm of agricultural IoT. In this paper, we introduce a smart optimal prediction model for sensors based on IoT-enabled precision agriculture. Initially, we enhance the THAM index (temperature, humidity, air- and water-quality measurement) by using the modified Wild Geese (MWG) algorithm to predict environmental conditions accurately. The deployment of IoT sensor nodes using quantum deep reinforcement learning (QDRL) to determine the idealamount of devices required for effective coverage of the target agricultural field to improving communication. Furthermore, we compute the production yield rate, consider various attributes such as fertilizer regulatory measures, temperature quotient, and agronomy by using the improved prairie dog optimization (IPDO) algorithm. Finally, we assess the performance of MWG-QDRL-IPDO model using test samples collected from the Meteorology Bureau through the related sensor middleware. Our findings reveal a checking efficacy of 96.35 %, even with a reduced amount of devices covering a hugezone. Similarly, the accuracy of IoT sensor node deployment reaches 91.47 %, contributive to reduce the irrelevant data generation and processing time.
Praveen Sankarasubramanian (Sun,) studied this question.