Technology Category
- Analytics & Modeling - Machine Learning
- Platform as a Service (PaaS) - Application Development Platforms
Applicable Industries
- Agriculture
- Equipment & Machinery
Applicable Functions
- Logistics & Transportation
- Product Research & Development
Use Cases
- Agriculture Disease & Pest Management
- Farm Monitoring & Precision Farming
Services
- Cloud Planning, Design & Implementation Services
- System Integration
About The Customer
AgroStar's customers are small farmers in India, particularly those operating in the states of Gujarat, Maharashtra, Rajasthan, Orissa, Bihar, and Karnataka. These farmers, who cultivate fewer than three acres, often face challenges such as crop damage due to unforeseen weather and pests, and lack access to farming-related information. They also have limited access to new, higher yield seeds and improved soil analyses, and often have to rely on traditional farming methods. AgroStar's mobile app, AgroStar Agri-Doctor, is designed to help these farmers by providing them with timely advice in five languages on various aspects of farming, including seed optimization, crop rotation, soil nutrition, and pest control.
The Challenge
AgroStar, an ecommerce platform selling farm supplies, was facing challenges in expanding its services to small farmers in India. The company aimed to provide a full-service platform that combines agronomy, data science, machine learning, and analytics to boost crop yields and improve income for these farmers. However, the lack of access to new, higher yield seeds and improved soil analyses for small farmers, who had to rely on traditional methods, was a significant hurdle. Additionally, the dissemination of innovative information from universities to small, grassroots farmers was slow and inefficient. The company also faced the challenge of providing timely advice in five languages on various aspects of farming, including seed optimization, crop rotation, soil nutrition, and pest control.
The Solution
AgroStar turned to Google Cloud to expand its offering and launched a cloud-based mobile app to help boost crop yields and encourage best practices for small farmers in India. The app, AgroStar Agri-Doctor, provides access to the firm's knowledge base hosted on Google Cloud, a Q&A forum that connects farmers to each other, and information about innovative practices and products. It also provides links to purchase and track the delivery of farm tools and supplies. AgroStar used Google Kubernetes Engine (GKE) for crop advice management and Compute Engine for its production application services. The company also used Firebase to implement its Agri-Doctor app, which shares knowledge base updates with one million users in near real time. AgroStar is also developing a variety of machine learning components to improve responsiveness and extend its platform offerings.
Operational Impact
Quantitative Benefit
Case Study missing?
Start adding your own!
Register with your work email and create a new case study profile for your business.
Related Case Studies.
Case Study
Smart Water Filtration Systems
Before working with Ayla Networks, Ozner was already using cloud connectivity to identify and solve water-filtration system malfunctions as well as to monitor filter cartridges for replacements.But, in June 2015, Ozner executives talked with Ayla about how the company might further improve its water systems with IoT technology. They liked what they heard from Ayla, but the executives needed to be sure that Ayla’s Agile IoT Platform provided the security and reliability Ozner required.
Case Study
IoT enabled Fleet Management with MindSphere
In view of growing competition, Gämmerler had a strong need to remain competitive via process optimization, reliability and gentle handling of printed products, even at highest press speeds. In addition, a digitalization initiative also included developing a key differentiation via data-driven services offers.
Case Study
Intelligent Farming with ThingWorx Analytics
Z Farms was facing three challenges: costly irrigation systems with water as a limited resource, narrow optimal ranges of soil moisture for growth with difficult maintenance and farm operators could not simply turn on irrigation systems like a faucet.
Case Study
Predictive Maintenance for Industrial Chillers
For global leaders in the industrial chiller manufacturing, reliability of the entire production process is of the utmost importance. Chillers are refrigeration systems that produce ice water to provide cooling for a process or industrial application. One of those leaders sought a way to respond to asset performance issues, even before they occur. The intelligence to guarantee maximum reliability of cooling devices is embedded (pre-alarming). A pre-alarming phase means that the cooling device still works, but symptoms may appear, telling manufacturers that a failure is likely to occur in the near future. Chillers who are not internet connected at that moment, provide little insight in this pre-alarming phase.
Case Study
Premium Appliance Producer Innovates with Internet of Everything
Sub-Zero faced the largest product launch in the company’s history:It wanted to launch 60 new products as scheduled while simultaneously opening a new “greenfield” production facility, yet still adhering to stringent quality requirements and manage issues from new supply-chain partners. A the same time, it wanted to increase staff productivity time and collaboration while reducing travel and costs.
Case Study
Integration of PLC with IoT for Bosch Rexroth
The application arises from the need to monitor and anticipate the problems of one or more machines managed by a PLC. These problems, often resulting from the accumulation over time of small discrepancies, require, when they occur, ex post technical operations maintenance.