An IOT based Solution for Crop prediction based on Novel Hybrid Deep Learning Techniques

Agriculture is one of the most significant economic sectors in every country. Smart agriculture aims to accomplish exact management of irrigation, fertiliser, disease, and insect prevention in crop farming. In the agricultural area, wireless sensor networks (WSNs) are used to gather data and communicate it to servers over a wireless link. This work presents a multiclass model based on a hybrid deep learning classifier approach (CNN + LSTM). Eighteen input characteristics were utilised to create the model, and crop yield was found and organised into three key components. When creating the multiclass model, the relative significance of the components is taken into account. For categorization of 3 crops: rice, groundnut and sugarcane, an objective function is defined. Furthermore, data visualisation analysis is utilised to identify essential approaches in progress of smart agriculture that may efficiently increase efficacy of production and assure agricultural product quality. Smart agriculture is progressively being incorporated into agricultural production, and advent of the Internet of Things (IoT) is giving it a technological boost. Agricultural tasks may be precisely accomplished using the IoT’ detecting, ID, 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 the 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.