Employing the agricultural classification and estimation service (ACES) for mapping smallholder rice farms in Bhutan

Abstract

Creating annual crop type maps for enabling improved food security decision making has remained a challenge in Bhutan. This is in part due to the level of effort required for data collection, technical model development, and reliability of an on-the-ground application. Through focusing on advancing Science, Technology, Engineering, and Mathematics (STEM) in Bhutan, an effort to co-develop a geospatial application known as the Agricultural Classification and Estimation Service (ACES) was created. This paper focuses on the co-development of an Earth observation informed climate smart crop type framework which incorporates both modeling and training sample collection. The ACES web application and subsequent ACES modeling software package enables stakeholders to more readily use Earth observation into their decision making process. Additionally, this paper offers a transparent and replicable approach for addressing and combating remote sensing limitations due to topography and cloud cover, a common problem in Bhutan. Lastly, this approach resulted in a Random Forest “LTE 555” model, from a set of 3,600 possible models, with an overall test Accuracy of 85% and F-1 Score of .88 for 2020. The model was independently validated resulting in an independent accuracy of 83% and F-1 Score of .45 for 2020. The insight into the model perturbation via hyperparameter tuning and input features is key for future practitioners.

RETRACTED: Enhanced Crop LAI Estimation via Random Forest Regression: Bayesian Optimization and Feature Selection Approach

“…These methods are useful for analyzing the relationship between target variables and multiple input features and are more efficient and accurate than single-learner models [15][16][17]. Ndikumana et al [18] using satellite data to estimate rice biomass, found that RFR produced better performance compared to other ML algorithms tested (e.g., Support Vector Regression). Zhang et al [19] used UAV imagery to study multiple traits in rice at two fertility stages, finding that the RFR algorithm outperformed SVR and ANN.…”