## 1 Introduction  
Sensor Networks (SNs) are experiencing immense development with the effective utilization of Artificial Intelligence (AI). The incorporation of SNs and AI can make a profit in the business and manufacturing sectors [1, 2]. In addition, SNs are extensively used to gather ecological constraints in making appropriate decisions for homes and industrial applications based on the learning experience of day-to-day activities in a real-time manner with AI and Machine Learning (ML). Additionally, with AI, sensor fusion can be carried out more comfortably and precisely than with traditional algorithms [3].  
Moreover, neural networks can handle anonymous circumstances in an intelligent way, as they have the ability to become aware of reimbursement methods for the training data and potentially amplify the value of the consequences to the consumer. However, the advancement of AI has the potential to undo the gesture of new sensor applications and push market requirements for smart sensing with the ability to extract information from sensors [4]. Before the original information is transferred using sensors to a system for storing, the original information from sensors must be focused on for collecting the sequence of information. Furthermore, automatically gathered data with tagged data for the preparation of training ML algorithms are integrated; the AI sensors in robots are similar in their provisions such as observation, listening, and manipulation in the same manner as humans [5].  
Man-made intelligence in horticulture is supporting ranchers by enhancing their production and reducing indifferent manners. The agricultural trade categorically and straightforwardly uses AI in their training to modify the order. The innovation of AI aids in the control and management of any poor-quality circumstances. Most innovative businesses in horticulture are gradually changing to an AI-empowered manner to tackle the improvement of agarian formation. AI facilitated mechanisms can recognize environmental changes faster and respond wisely. Organizations in agribusiness, with the assistance of AI, are treating farming data to reduce aggressive outcomes. Simulated intelligence in a high-level manner is assisting the rancher with the information. The required information helps the land-keeper's high income and profit without being dependent on harvest by understanding and learning AI. In addition, AI is an effective method to recognize potential deformities for DL applications in terms of designs in agribusiness [6].  
On the other hand, the Cooperative communication model has been the best choice in complex underground environments for a high density of scalable sensor nodes but without compromising the greater inter-and intra-communication challenges. The cooperative nodes in the sensor network possibly create a well-organized automated dynamic structure to obtain a strong association in signal propagation amid communication hurdles and millimeter waves. When DL techniques and Cooperative modeling are used together in the right way, they can be used to reduce the effects of underground communication limitations such as transmission delay, packet loss, and throughput [7].  
The objectives of the paper are two-folded. First, the development of a DL-based Cooperative communication channel model for WUSNs is carried out with the proper utilization of cooperative sensor nodes to reduce the unnecessary energy consumption by individual nodes. Second, the proposed Cooperative communication channel model for WUSNs based on Deep Learning is meant to help people share resources.  
The paper is organized into six sections. The literature survey is explained in Section 2. Section 3 elucidates the contributions of DL and ML in the Underground Wireless Sensor Network Environment. The proposed Deep Learning-Based Cooperative Communication Channel Model for WUSNs is explained elaborately. The outcome of the proposed work and experimental results are explained by the DL-Based Transmission Path Selection in WUSN.