Field Operations Optimization
Business Problem:
A leading agro-chemical company manages global seed production and distribution. Ensuring timely harvests at optimal moisture levels is crucial to maximize yield and reduce seed loss. However, dynamic factors such as weather, equipment availability, and processing plant capacity make it challenging to create efficient harvest schedules. These dynamic factors also make it extremely difficult to measure the impact of ML model application and effectiveness.
Business Solution:
Diwo Catalyst ingests AI/ML model output to predict moisture levels, optimal harvest timing, and field prioritization to deliver timely recommendations and actionable answers to operators, ensuring crops are harvested within the optimal moisture range. The AI Enterprise Ready platform provides secure, scalable, and integrated operations seamlessly between data scientists and field operations managers for a continuous feedback and improvement loop. By combining structured and unstructured data sources with a Semantic Knowledge Graph, support for decision makers is both highly accurate and relevant. The system continuously learns, providing dynamic recommendations and prompts, improving decision-making efficiency and ultimately enhancing yield.
