Optimal Deep Learning Driven Smart Sugarcane Crop Monitoring on Remote Sensing Images

Crop monitoring is a process that involves regular field visits that seem to be difficult since it needs a huge amount of time and manpower. Thus, in modern agriculture, with an extensive range of satellite data such as Landsat, Sentinel-2, Modis, and Palsar, data are readily available. Sugarcane is a tall perennial grass belonging to the genus Saccharum, utilized for producing sugar. These plants were generally 2–6 m tall with fibrous, stout, jointed stalks, rich in sucrose, that will be accumulated in the stalk internodes. Sugarcanes have a different growth pattern and phenology than many other crops; thus, the spectral and temporal features of satellite data are examined by utilizing statistical and machine learning (ML) techniques for optimal discrimination of sugarcane fields with other crops. In this study, we propose an Optimal Deep Learning Driven Smart Sugarcane Crop Monitoring (ODLD-SSCM) model on Remote Sensing Images. The presented ODLD-SSCM model mainly intends to estimate the crop yield of sugarcanes using RSIs. In the presented ODLD-SSCM technique, the sugarcane yield mapping can be derived by the use of the self-attentive deep learning (SADL) model. Besides, an oppositional spider colony optimization (OSCO) algorithm is used for the hyperparameter tuning of the ODLD-SSCM model. A detailed set of experimentations were performed to demonstrate the enhanced outcomes of the ODLDSSCM model. A comprehensive comparison study pointed out the enhancements of the ODLD-SSCM model over other recent approaches.