An Architecture for Quality Centric Crop Production System

Abstract

Artificial Intelligence (AI) can be applied to find the patterns in historical data and help finding real-time predictions for making data-driven decisions. Digital transformation and automation are required for industries for better growth. AI-predictive analytics software solution that at its core, delivers the ability to recommend or select farm fields, practices, timing and inputs that have a high probability of delivering crops that meet an optimized set of quality attributes. These attributes allow the client to produce food with specific characteristics for competitive advantage and/or financial benefit. The system will allow growers to benefit by allowing “what-if” modeling of farming practices to anticipate desirable crop attributes. In this chapter, we proposed an architecture for developing a recommendation system for helping farmers produce crops with specific quality attributes which are required by the specific consumer. The recommendations can be generated by using the endogenous and exogenous data captured through IoT sensors and AI modeling. The architecture is generic and solutions can be designed to work for a range of foods/crops. The architecture is designed to develop solutions that deliver food producer’s a focused value proposition. It uses an Agcentric methodology in creating the capability to recommend the fields for crop production.