Sugarcane yield prediction using NOA-based swin transformer model in IoT smart agriculture

Abstract

The Internet of things (IoT) empowers precise organization and intelligent coordination for industrial facilities and smart farming, enhancing agricultural efficiency. Sugar production relies on various auxiliary elements, but in labor-intensive smart agriculture, creating accurate forecasts is a formidable challenge. Machine learning emerges as a potential solution, as current convolutional neural network-based phase recognition techniques struggle with long-range dependencies. To address this, a temporal-based swin transformer network (TSTN) is introduced, comprising a swin transformer and long short-term memory (LSTM). The swin transformer employs attention mechanisms for expressive representations, while LSTM excels at extracting temporal data with long-range dependencies. The nutcracker optimizer algorithm (NOA) fine-tunes LSTM weights. TSTN effectively blends these components, providing spatiotemporal data with enhanced context. This model outperforms competitors in accuracy, as demonstrated through testing with data from Uttar Pradesh. The integration of IoT and TSTN marks a significant advancement in optimizing agricultural operations for increased productivity and efficiency. In the comparative analysis, the proposed TSTN-NOA model achieves better performance and results than other existing models.