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

Abstract

Abstract 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.

Plant phenotyping relevance

リモートセンシング画像からサトウキビ収量を推定する深層学習モデルとハイパーパラメータ最適化を中心に開発・比較しており、植物の収量形質を抽出する方法が主要貢献である。

Code and data availability

The article describes an ODLD-SSCM sugarcane yield-mapping model but contains no data availability statement, no public dataset link, no code deposit, and no trained model release. Only the CC BY license URL is present, which is not a paper-specific asset.

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