Risce - An Explainable ML Chain for Practical Sustainable Agriculture

Abstract

Knowledge systems in sustainable agriculture see a big gap with end users due to lack of easy-to-use interfaces with existing knowledge. Adding to the problem, decisions coming from black-box models are not understandable for most users. We try to bridge the gap with an integrated chain of explainable ML models to address the most useful applications in the agri-food industry. To make the integrated model available to users and help them draw benefits out of it, we also propose a novel idea of an explainable ML framework for interaction with human users. This human-in-the-loop approach makes ML models more trustworthy. End-users understand the output from ML models and also improve models with feedback. The application interface is also proposed to have features for multilingual communication among users to build communities. Feedback from communities help further refine ML models. The proposed system is named as Reusable Intelligent solution for Cultivation Enhancement (RISCE). In this article, we provide a demonstration of our system with an intrinsically explainable model for crop vigor analysis.