The fields of optical communications, fiber optics, and sensors and laser applications have undergone significant evolution, revolutionizing the way we transmit and receive data and having a profound impact on various industries. With ongoing advancements and research, these fields hold immense potential for future developments. In-depth information on optical communications, fiber optics, and sensors may be found in this study. The constraints and limits of optical communications as well as the qualities of optical fibers and the many kinds of optical fibers utilized in optical communications are discussed. Additionally, it also covers the use of fiber optics in sensing applications, different types of fiber-optic sensors, and recent developments and future trends in the field. The article provides a comprehensive overview of the current state of the field, highlighting the significance of technology and its impact on various industries. The article also aims to give readers a better understanding of the current state of the field and its potential for future developments.

Information communication technology (ICT) breakthroughs have boosted global social and economic progress. Most rural Indians rely on agriculture for income. The growing population requires modern agricultural practices. ICT is crucial for educating farmers on how to be environmentally friendly. It helps them create more food by solving a variety of challenges. India’s sugarcane crop is popular and lucrative. Long-term crops that require water do not need specific soil. They need water; the ground should always have adequate water due to the link between cane growth and evaporation. This research focuses on forecasting soil moisture and classifying sugarcane output; sugarcane has so many applications that it must be categorized. This research examines these claims: The first phase model predicts soil moisture using two-level ensemble classifiers. Secondly, to boost performance, the proposed ensemble model integrates the Gaussian probabilistic method (GPM), the convolutional neural network (CNN), and support vector machines (SVM). The suggested approach aims to correctly anticipate future soil moisture measurements affecting crop growth and cultivation. The proposed model is 89.53% more accurate than conventional neural network classifiers. The recommended models’ outcomes will assist farmers and agricultural authorities in boosting production.