AgroStar has launched a cloud-based mobile app that is helping to boost crop yields and encourage best practices for small farmers in India. Launched as an on-premises ecommerce platform selling farm tools in 2008, the firm turned to Google Cloud Platform (GCP) to expand its offering. It now uses cloud-based analytics and is deploying ML models to provide timely advice in five languages on everything from seed optimization, crop rotation, and soil nutrition to pest control.

A 2018 survey underscored the demand for agricultural planning for Indian farmers. While farming remains a dominant sector in India, employing half of its labor force, 70 percent of small farmers – those cultivating fewer than three acres – said their crops are damaged by unforeseen weather and pests. An even higher number – 74 percent – say they lack access to farming-related information.

Widening that gap is the relative lack of access to new, higher yield seeds and improved soil analyses for small farmers, who must otherwise rely on traditional methods. “It could take a few years for innovative information to trickle down from universities to small, grassroots farmers,” says Pritesh Gudge, AgroStar Software Engineer. “Today, just by clicking through our Android application, farmers learn about new, effective farming practices and receive advice customized to their crop and soil.”

Connecting a million farmers in the cloud

Operating in the Indian states of Gujarat, Maharashtra, Rajasthan, Orissa, Bihar, and Karnataka, AgroStar is closing the knowledge gap with a full-service, cloud-based SaaS solution – the only one of its kind in India. It combines agronomy, data science, and analytics to help farmers by providing a variety of resources.

AgroStar has reached over a million farmers through its Android app, the AgroStar Agri-Doctor. The mobile client is available as a web-based or full-featured native app. Both provide access to the firm’s knowledge base hosted on GCP, a Q&A forum that connects farmers to each other to help understand and better solve problems and to learn about innovative practices and products. Farmers can also click through to follow local and national market trends that help forecast crop prices.

In addition to the self-service knowledge base, AgroStar provides access to agronomy experts who use cloud-based analytics tools and historical data to provide season-and locale-specific advice to each farmer. “We are now tracking thousands of calls in 5 languages each day,” says Pritesh.

The AgroStar app also provides links to purchase and then track the delivery of farm tools and supplies such as cultivators and fertilizers. An in-house platform manages fulfillment centers and a doorstep delivery network simplifies the supply chain while giving farmers what they need, when they need it. By procuring directly from the manufacturers and primary distributors of farm supplies, Agrostar is achieving cost savings, which it passes on to farmers.

Build fast, pivot faster

From the start, the human and environmental variables of farming in India, not to mention the volume of AgroStar’s few hundred thousand monthly active users, made a highly scalable cloud-based solution inevitable. Farmers rely on the firm’s Agri-Doctor app to provide advice in multiple languages on topics that range widely throughout three growing seasons, each with distinct crop nutrition and rotation cycles and farm implementation requirements.

“For farmers, the focus keeps changing every month, and every season,” says Pritesh. “To serve our growing community, we needed a platform that could process images at high volume, fulfill tools and seed orders across thousands of miles, and respond to multilingual queries. We quickly moved away from spreadsheets and server-based solutions – we needed to build fast and pivot faster.”

Ending late-night deployments

The firm’s first cloud experience was with an AWS solution. At the time, AWS was the only cloud provider in India, but AgroStar wanted to find a solution that was easier to use and offered better integration with Android devices. “Deployment and processing costs were very high, and the developer tools and documentation were not as intuitive as we needed,” says Pritesh.

When GCP service arrived in India in October 2017, AgroStar embarked on a platform re-implementation that made possible dramatic changes in the way it developed and deployed its solution. Using Google Kubernetes Engine (GKE) for crop advice management and Compute Engine for its production application services, the firm built the backend for the Agri-Doctor discussion forum in only three weeks. The platform’s microservice architecture is implemented in Python and Golang and deployed on GCP.

AgroStar began to realize significant efficiencies in its build, deploy, and test cycles. “We previously needed to work overnight to deploy to production,” says Pritesh. “Now using Google for Kubernetes containers and a rolling update strategy, we can deploy during the day without any problems or interruptions to service.”

The move to GCP streamlined AgroStar’s stack. “We were running 12 independent instances on AWS,” says Pritesh. “With Google Kubernetes Engine, we are deployed on a single cluster at a cost savings of $1,300 per month and growing.”

Improving customer response times by 85 percent

With a managed deployment capability, AgroStar can devote more time and resources to executing on its platform and Agri-Doctor app development plan. A strategic goal was managing customer response times as the firm grew its base. GCP has helped the firm meet that goal, achieving an 85 percent improvement in customer response times even as traffic grew significantly.

“With our on-premises solution, we could handle around 100 customers daily, which took 30 to 50 minutes for each customer,” says Pritesh. “We now handle thousands of customers daily, taking only 4 to 5 minutes for each one.”

AgroStar used Firebase to implement its Agri-Doctor app. A real-time cloud database, Firebase provides an API that enables the Agri-Doctor advice forum to be synchronized across all its far-flung mobile clients, effectively sharing knowledge base updates with one million users in near real time.

Using cloud tools to manage and monitor

Cloud Pub/Sub, Kafka, and Cloud Dataflow manage data ingestion and queueing of event and transaction data to the analytics layer. BigQuery fetches and persists data to Cloud Storage. Cloud SQL and dashboards powered by Tableau deliver farmer crop and soil profiles within minutes.

Cloud IAM helps AgroStar control access to all its cloud resources. And Stackdriver, the integrated logging aggregation capability for GCP, helps monitor and speed debugging on every tier of the AgroStar solution.

Machine learning to enhance yields

AgroStar is developing a variety of ML components to improve responsiveness and extend its platform offerings.

To speed up the diagnosis of and treatment for crop blight, AgroStar is building a deep learning pipeline using TensorFlow. The pipeline relies on GoogLeNet models that use multi-layered convolutional visual pattern recognition. It will assess uploaded images to support a disease-detection capability on the mobile app. Based on the commercially successful AI algorithms that automated postal code processing, GoogLeNet offers improved performance and computational efficiencies by using a creative layering technique that distinguishes them from older, sequential recognition engines.

To improve its customer search experience, AgroStar is developing an ML pipeline that shrinks fetch times by suggesting tags mapped to stored data. Processed using TPUs, Cloud Natural Language and Video AI, the tags provide a metadata layer that supports queries in any of the ten natural languages that AgroStar farmers can use.

The AgroStar search pipeline consists of Long Short-Term Memory (LSTM) models of Recurrent Neural Networks. Recurrent networks exhibit “memory” through iterative processing and are distinguished from feedforward networks by a feedback loop connected to their past decisions, ingesting their own outputs moment after moment as input.

Implementing a recommendation engine

The firm is also adapting the Random Forests TensorFlow AI model to develop a crop and product recommendation engine. The model is trained by consuming numerical (rainfall, humidity, water availability per acre) and categorical (soil type, water sources) parameters to suggest appropriate products by season, region, and locale.

To simplify the product suggestion experience, AgroStar developers are testing Cloud Dialogflow, the Google Cloud conversational interface, to build a chatbot capability into its mobile app. The bot will track a farmer’s crop schedules and answer simple questions by linking to the recommendation engine.

AgroStar is also extending its analytics platform with AI-powered sales planning and forecasting. Using linear regression models implemented in TensorFlow and powered by Cloud ML Engine, the capability will enhance supply chain logistics as the company scales its operations across India.

To provide a credit on-demand offering for a range of seed-to-harvest cycle products, AgroStar is attempting to use Vision API to create an AI model that will convert uploaded photos of customer application records into standard data formats. The firm’s credit policy features a grace period in which farmers begin paying back loans after harvested crops go to market.

A versatile and friendly development ecosystem

AgroStar credits the convivial tools and documentation that GCP offers and its incremental, pay-as-you-go pricing model for both the firm’s success and its ability to manage growth.

“What Google Cloud offers is extremely good documentation and extremely simple-to-use tools and interfaces across all services,” says Pritesh. “It helped us initially deploy our platform and at every scale that we have required since then, and its cost effectiveness enabled us to staff up to meet new feature milestones.”

When I reflect back on the past year and the pandemic, I’m struck by how the reliance on remote work and operations has changed the fundamentals of business forever. For the financial services industry, this rings particularly true. Many conversations I’m having right now with organizations revolve around embracing a transformation cloud, and thinking of cloud computing not just as an infrastructure decision, but also as the locus for transformation throughout the company. 

Today, as we welcome the industry to our Financial Services Summit, we’ll demonstrate just how Google Cloud accelerates a financial organization’s digital transformation through app and infrastructure modernization, data democratization, people connections, and trusted transactions. We hope you’ll join us.

How we’re helping financial services firms build their transformation clouds

At Google Cloud, we continue to focus on areas where we can bring the best of our capabilities to banking, capital markets, insurance, and payments customers around the world. Our work with financial services industry customers has given us a deep understanding of the real-world, specific use cases that matter most to them. This groundwork led us to engineer products and solutions that are open and flexible, not ones that force them to rip out existing investments in ERP or other early IaaS cloud implementations. 

It’s why we’ve engineered solutions such as Lending DocAI, Open Banking with Apigee, and Datashare for financial services to help transform the industry. These solutions were created with our customers’ security and compliance top-of-mind and are built at the intersection of user-optimized experience, technology, and sovereignty.

At their core, financial institutions want to drive growth, reduce costs, mitigate risk, stay compliant, and increase efficiency. As a result, when we partner with them on their transformation journeys, we consider three essential focus areas: 

• Enabling the human experience and connected interactions
• Building an open and intelligent data foundation for better insights
• Providing the most trusted and secure cloud in the industry

Enabling humans and connected interactions

A company’s transformation is about more than technology; people and culture ultimately drive change. HSBC, for example, recognized its business users would benefit from guided answers to common questions around risk policy compliance, and turned to Google Cloud to leverage AI and machine learning bots to assist employees, ease the burden on policy experts, and improve the user experience. Using Dialogflow, a core component of Contact Center AI, HSBC was able to build a conversational platform that quickly and accurately addresses user needs at scale. 

Another example is Equifax, which used Google Workspace to support collaboration not only internally between employees, but also externally with customers. Customers can use Google Cloud solutions for financial services to build these sorts of technology-enabled human interactions quickly and easily—supporting organizational change at scale.

Building an open and intelligent data foundation for smarter, faster insights

The real impact of Google Cloud solutions for financial services comes when the whole company has access to the right information at the right time, and can act more intelligently on that data. Our solutions help businesses safely leverage their data and get a complete 360-degree view of their customers’ information, which can often be scattered across multiple systems (CRM, lending, credit, etc.). This helps financial institutions improve the overall customer experience—and sometimes even develop new products quickly. 

Indeed, all financial institutions are looking for ways to grow revenue and reduce expenses, and data can be a critical ingredient to doing both effectively. As daily transactions rise, so does the volume and complexity of data. But to implement new customer experience innovations (and new revenue streams), financial institutions must first capture data effectively. This is why AXA Switzerland, for example, uses real-time analytics on Google Cloud to gain cross-industry insights about future trends and customer preferences.

Financial services organizations also need the confidence of building on a platform that provides choice, flexibility, and agility to move fast. It’s why we have an open cloud approach that allows Google Cloud services to run in different physical locations such as on-premises, other public clouds, and the edge. Customers can also harness the power of data and AI through our open APIs, machine-learning services, and analytics engines on any major cloud platform. This is why companies like Macquarie Bank are taking advantage of Google Cloud’s open, hybrid architecture to modernize and empower its developers.

Compliant and secure to address risk and regulatory needs

As a highly regulated industry, financial services is focused on security and compliance, risk and regulations, and fraud detection and prevention. Google Cloud offers unique capabilities to earn customers’ trust as part of our continuing work to be the most trusted cloud in the industry. Google Cloud provides a secure foundation that you can verify and independently control. Our cloud technology reduces risk and data loss, because it is built on comprehensive zero-trust architecture. Finally, we offer a shared-fate model built on best practices in risk management via automation, guidance, and insurance. This is why customers like BBVA have confidence anywhere their systems may operate. 

On the regulatory front, global legislators and regulators continue to focus on the stability of the industry that only a decade ago went through one of the biggest liquidity crises in history. With this oversight comes strong expectations of risk mitigation. Google Cloud offers a single, global set of controls, reviewed by financial institutions and regulators around the world, and verified in collaborative audits—making compliance simpler and less costly for our customers.

Finally, Google Cloud allows financial services firms to operate confidently with advanced security tools that help protect data, applications, and infrastructure, as well as their customers from fraudulent activity, spam, and abuse. We help protect your data against threats, using the same infrastructure and security services we use for our own operations, ensuring you never have to trade-off between ease of use and security. Google Cloud encrypts data at-rest and in-transit. And we now also offer the ability to encrypt data-in use, while it’s being processed for customer VM and container workloads.

Let us help you with your transformation cloud journey

We’ve seen leading financial services companies embrace Google Cloud to help them move beyond infrastructure toward the next phase of their cloud evolution. This is an era where no company is better positioned to lead than Google Cloud, and we’re excited to help you with your journey.

Learn more about Google Cloud for financial services.

Do you want to fire up a bunch of techies? Talk about multicloud! There is no shortage of opinions. I figured we should tackle this hot topic head-on, so I recently talked to four smart folks—Corey Quinn of Duckbill Group, Armon Dadgar of Hashicorp, Tammy Bryant Butow of Gremlin, and James Watters of VMware—about what multicloud is all about, key considerations, and why you should (or shouldn’t!) do it.

Five important insights came out of these discussions. If you’re on a multicloud journey or considering one, keep reading.

Do: Choose to do multicloud for the right reasons

Don’t do multicloud because Gartner says so, implores Corey Quinn. Before embarking on a multicloud, define a “why” focused on business value journey, says Armon Dadger. For example, you might want to use services from each public cloud because of their differentiated services, according to Tammy Bryant Butow. Armon also calls out regulatory reasons, existing business relationships, and accommodating mergers and acquisitions. On the topic of M&A, Corey points out that if you acquire a company that uses another cloud, it’s usually expensive and difficult to consolidate. It can be smarter to stay put.

Don’t: Over-engineer for workload or data portability

Thinking that you’ll build a system that moves seamlessly among the various cloud providers? Hold up, says our group of experts. Armon points out that aspects of your toolchain or architecture may be multicloud—think of some of your workflows or global network routing—but that shifting workloads or data is far from simple. Corey says that trying to engineer for “write once, run anywhere” can slow you down, and ignores the inherent uniqueness that’s part of each platform. Specifically, Corey calls out the per-cloud stickiness of identity management, security features, and even network functionality. And data gravity is still a thing, says James, that causes some to dismiss multicloud outright.

If you’re using multiple public clouds, you take advantage of the distinct value each offers, Armon says. Use native cloud services where possible so that you see the benefits from useful innovations, built-in resilience, and baked-in best practices. The value from that cloud-infused workload may outweigh the benefits of seamless portability.

Do: Recognize different stakeholder interests and needs

James smartly points out that many multicloud debates happen because people are arguing from different perspectives. Context matters. If you’re an infrastructure engineer who invests heavily in a given cloud’s identity and access management model, multicloud looks tricky. Or if you’re a data engineer with petabytes of data homed in a particular cloud, multicloud may look unrealistic. James highlights that many developers default to multicloud because their local tools—where all the work happens—are multicloud. A developer’s IDE and preferred code framework(s) aren’t tied to any given cloud. Be aware that groups within your organization will come at multicloud from distinct directions. And this may impact your approach!

Don’t: Go it alone

Corey talks about the importance of asking others what worked, and what didn’t. Tammy offers her best practices around sharing results from experiments. It’s about sharing knowledge and tapping into it for community benefit. Others have probably tried what you’re trying, and can help you avoid common pitfalls. If you’ve just made an architectural choice that didn’t work out, share it, and help others avoid the pain. 

Read research from analysts, go to conferences or watch videos to observe case studies, and join online communities that offer a safe place to share mistakes and learn from others.

Do: Experiment first using techniques like multi-region deployments

If you think you can operate systems across clouds, how about you first try doing it across regions in a specific cloud, suggests Corey. Getting a system to properly work across cloud regions isn’t trivial, he says, and that experience can help you uncover where you have architectural or operational constraints that will be even worse across cloud providers.

This is great guidance if your multicloud aspirations involve using multiple clouds to power one application—versus the more standard definition of multicloud where you use different clouds for different applications—but can also surface issues in your support process or toolchain that fail when faced with distributed systems. Start with muti-region deployments and chaos engineering experiments before aggressively jumping into multicloud architectures.

The Google Cloud take

Do the things above. It’s great advice. I’ll add three more things that we’ve learned from our customers.

1. Don’t fear multicloud. You’re already doing it. You don’t single-source everything. As Corey mentioned, you probably already have one cloud for productivity tools, another for source code, another for cloud infrastructure. You’ll use software and application services from a mix of providers for a single app. You have that experience in your team and have been doing that for decades. What people do rightly worry about is using more than one infrastructure service beneath an application, as that can introduce latency, security, and logistical hurdles. Make sure you know which model your team is considering.
2. Embrace the right foundational components, including Kubernetes. Will everything run on Kubernetes? Of course not. Don’t try to do that. But it also represents the closest thing we have to a multicloud API. Companies are using Kubernetes to stripe a consistent experience across clouds. And this isn’t just to orchestrate containers, but also to manage infrastructure and cloud-native services. Also, consider where you need other fundamental consistency across clouds, including areas like provisioning and identity federation.
3. Use Google Cloud as your anchor. Here’s a fundamental question you have to decide for yourself: Are you going to bring your on-premises technology and practices to the cloud, or bring cloud technology and practices on-prem? We sincerely believe in the latter. Anchor to where you’re trying to get to. We offer Anthos as a way to build and run distributed Kubernetes fleets in Google Cloud and across clouds. By using a cloud-based backplane instead of an on-prem one, you’re offloading toil, leveraging managed services for scale and security, and introducing modern practices to the rest of your team.

We learned a lot about multicloud through these discussions, and it seems like others did too. That’s why we’re going to do a second round of interviews with a new crop of experts so that we can keep digging deeper into this topic. Stay tuned!