Agriculture is very significant economic sectors in every country. Farm Monitoring aims to accomplish exact management of irrigation, fertilizer, disease, and insect prevention in crop farming. In agricultural area, wireless sensor networks (WSNs) are used to gather data and communicate it to servers over a wireless link. Eighteen input characteristics were utilized to create the model, and crop yield was found and organized into three key components. When creating the multiclass model, the relative significance of the components is taken into account. For categorization of three crops: rice, groundnut, and sugarcane, an objective function is defined. A multiclass model based on a hybrid deep learning classifier approach (CNN + LSTM) is used. Furthermore, data visualization analysis is utilized to identify essential approaches in progress of smart agriculture that efficiently increase efficacy of production and assure agricultural product quality. Use of smart agriculture is progressively being incorporated into agricultural production, and advent of Internet of Things (IoT) is giving it a technological boost. Agricultural tasks precisely accomplished using the IoT’ detecting, transmission, observing, and input capacities, which saves farmers’ time and enhances crop yields and advantages them in long run. We installed smart agriculture IoT equipment in farm for monitoring reasons and used the algorithm in our research to do an actual-scenario analysis; the findings show that this suggested scheme is actually practical. The categorization findings are compared to the results acquired from on-the-ground agricultural specialists. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.