AgTech Case Study: Machine Learning and Cloud Vision

As the farming industry struggles to support the rapidly growing population, crop disease reduces the production and quality of food and biofuel crops. Such losses can be disastrous to the economy and dramatically threaten the global population’s access to nutrition. In extreme cases, diseased crops can produce toxins that create serious health problems for consumers.

This case study describes how one agricultural biotechnology company is using machine learning to treat crop disease and improve the quality of our food.



Farmers spend billions of dollars on crop disease management, often without adequate diagnostic tools, resulting in poor disease control, pollution, and ineffective results. Additionally, crop disease can devastate natural ecosystems, leading to significant environmental problems caused by habitat loss and poor land management. 

Without deep subject matter expertise, crop disease is difficult to diagnose. Accurately describing a crop’s symptoms to a plant pathologist over the phone is nearly impossible. Without a physical examination of the specimen, our client was unable to recommend treatment options to their customers affected by crop disease.



Our client, dedicated to providing farmers with the broadest choice of products and services that address crop disease, turned to OCI to implement a technology solution that would streamline and improve the diagnostic process.

We assisted with the architecture and development of a mobile application that allows farmers to photograph diseased crops and receive accurate diagnostics in near real-time. The diagnostic process occurs through the use of advanced image analytics, and powered by machine learning.


The mobile application uses Google's Cloud Vision API to analyze each crop’s color, size, texture, and decay patterns, then references these data points against a library of 50,000 images. These images are classified into categories and labeled carefully.



Through pattern recognition machine learning techniques, the mobile application is trained to diagnose crop disease in near real-time. 



A reusable platform enables data and model management, along with DevOps support for multi-cloud infrastructure to assist data scientists in performing data cleansing, learning, and service deployment of solutions, utilizing AI analytics techniques at scale.