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.”

Generative AI is a rapidly expanding technology with a wide range of potential applications. Google Cloud Learning is thrilled to offer a new, no-cost Generative AI Fundamentals skill badge. This skill badge is designed for anyone eager to learn about the power of generative AI. No technical skills or prior knowledge required!

For those new to digital credentials, Google Cloud skill badges are digital credentials issued by Google Cloud in recognition of your knowledge of Google Cloud products and services. Individuals can earn skill badges on Google Cloud Skills Boost, and can share their skill badge to their social media profile and resume.

Watch the short videos in the generative AI courses and complete the final quiz to earn the Generative AI Fundamentals skill badge pictured below. In as little as 120 minutes, you will learn the basics of how generative AI works, how Google Cloud AI technology can be used by businesses and individuals, and how the principles of responsible AI lead to ethical decisions about the use of generative AI. 

By earning the skill badge, you will demonstrate your understanding of foundational concepts in generative AI.

The topics covered in the courses include:

1. Introduction to Generative AI 

• Explain how generative AI works
• Describe generative AI model types
• Describe generative AI applications

2. Introduction to Large Language Models

• Define large language models (LLMs)
• Describe LLM use cases
• Explain prompt tuning
• Describe Google’s generative AI development tools

3. Introduction to Responsible AI

• Identify the need for a responsible AI practice within an organization
• Recognize that decisions made at all stages of a project make an impact in Responsible AI
• Recognize that organizations can design an AI infrastructure to fit their own business needs and values

Earn the skill badge and show off your generative AI knowledge today! And for more content to help you stay up to date with generative AI, check out “The Prompt” and our generative AI primer for executives on Transform with Google Cloud.

As I type this blog in a Google Doc, I can’t help but think about how much we rely on digital documents to communicate, collaborate, and do business. Yet most of the data in documents remains un-analyzed. Even when documents are part of customer-facing workflows like processing a mortgage application or a contract, businesses frequently struggle with the data those documents contain. Often, even finding the right document in a haystack of thousands across the organization can be challenging. To resolve these difficulties, most organizations rely on manual, time-consuming, and resource-intensive processes—all of which are incredibly frustrating for employees at the forefront of document workflows.

Because of challenges like these, in 2020, we launched Document AI, an AI agent that lets organizations apply machine learning (ML) to their hardest document automation problems. Since then, we have introduced specialized models to extract data for industry-specific use cases such as mortgage processing and procurement. With the launch of Document AI Workbench and Document AI Warehouse at Google Cloud Next ‘22, we’ve continued to take significant steps in our mission to help organizations simplify and automate document processing. Let’s double click on each of these announcements.

Custom document processing with Document AI Workbench

With Document AI Workbench, organizations can process documents by creating custom ML models that are specific to their business needs and extract unstructured data with a high degree of accuracy. Thanks to the user-friendly interface, even business users who do not have extensive ML skills can get started training or uptraining models.

Moreover, if an organization wants to transfer learning from pretrained models and enhance a model further to, say, include new fields, users can now do so by what we call “uptraining.” The uptraining feature is especially valuable for the most common yet complex use cases because it helps to save time and resources, so businesses don’t have to start from scratch. Uptraining for the invoice, purchase order (PO), contracts, W2, 1099-R, payslip, and 1040 pre-trained models unlocks new possibilities for improving accuracy, adding new language support, and schema customization.

We’re continuing to invest in these pretrained models. At Next’22, we announced an update to our invoice and expense pre-trained models with improvements to normalization and line item entities detection, as well as new ID proofing capabilities via a flexible API designed to spot fake, altered, or doctored ID documents. We’ve also added support for five new languages across invoice and expense models, in addition to the 12 previously-supported languages, and expanded availability in Canada and Australia regions, in addition to previously-supported US, EU, and Singapore regions.

According to Daan De Groodt, Managing Director, Deloitte Consulting LLP, Document AI Workbench “is poised to be a game changer, because we can now uptrain various text documents and forms utilizing powerful Google Machine Learning models to get the desired accuracy creating greater time and resource efficiencies for our clients.”

And customers are already seeing benefits. Libeo used Document AI to uptrain an invoice parser with 1,600 documents and increase its testing accuracy from 75.6% to 83.9%. “Thanks to uptraining, the Document AI results now beat the results of a competitor and will help Libeo save ~20% on the overall cost for model training over the long run,” said Libeo chief technology officer, Pierre-Antoine Glandier.

Google-powered document search with Document AI Warehouse

With Document AI Warehouse we are bringing the best of Google’s semantic search to documents. Document AI Warehouse lets enterprises search, store, govern and manage documents and their AI-extracted data and metadata in a single platform. With Document AI Warehouse’s simple and intuitive web accessible user interface, users can explore, view, bulk update and organize documents into folders. Document AI Warehouse offers robust enterprise control and governance so you can control who has access at the document and folder levels and assign users and groups permissions to view, edit, manage (share, delete) documents. You can migrate, sync, or federate documents from other repositories, such as Microsoft SharePoint, Amazon S3, and IBM FileNet. Or if that’s not an option we simply index the content and any extracted/tagged metadata).

We also will consolidate a number of next-generation product enhancements on Document AI OCR and Form Parser by the end of this year – including deeper insights into document quality & semantics, a unified document OCR experience, expanded language coverage for Form Parser, and advanced tooling for model lifecycle management. Google’s DeepMind team developed a new method that allows the creation of document parsing ML models for utility bills and purchase orders with 50%-70% less training data than what was previously needed for Document AI. We’re working on integrating this method into Document AI Workbench in the coming months.

Getting started

I’m very excited about what the future holds for Document AI as a platform for businesses to simplify document automation. Learn more about all these exciting developments in my session at Next’22 or try out one of our offerings today.

At Google Cloud, our customers are at the forefront of digital transformation—launching entirely new businesses and products built in the cloud, redefining entire industries with data and artificial intelligence, delivering innovative new consumer experiences, or committing to sustainable new ways of doing business.

We’re committed to our customers’ success, and over the past two years we’ve invested significantly in providing ongoing and integrated support through our customer care portfolio, including launching new Premium and Mission Critical Support offerings that allow us to monitor, prevent, and mitigate impacts quickly, while delivering the fastest response times in the industry.

Today, I’m proud to unveil several new resources and offerings for our customers, including a new Custom AI Solutions practice, new Global Delivery Centers, expanded Executive Briefing Centers, and new leaders to help continue to drive our organization forward.

Helping businesses innovate with a new Custom AI Solutions practice

We are investing in services and offerings to help our customers innovate and drive innovative change to business processes, culture, and customer experiences with Google Cloud products and services. Artificial intelligence (AI) and machine learning (ML) technologies are foundational for many such digital transformations, and to help customers create real business value with these technologies, we’re excited to launch a Custom AI offering to address our customers’ most critical innovation needs. 

AI and ML technologies are foundational to digital transformations, yet they are not “one size fits all.” Each customers’ problems and opportunities are unique; a rideshare company will use AI differently than a brick and mortar retailer, or a healthcare company, or an insurance firm. To help organizations deploy AI and ML more effectively, we’re launching a new Custom AI Solutions practice, offering customers custom-built AI and ML solutions built into Vertex AI; access to Google’s engineering expertise; and predictable, subscription-based pricing. 

Our teams are already partnering closely with early customers to build and deploy custom AI solutions. For instance, USAA, the large North American insurer, is using Google Cloud ML to process near-real-time damage estimates based on digital images to create a streamlined operations experience.

Learn more about our Custom AI Solutions practice offering here, and how we work together with our customers here. To get in touch, please contact our sales team.

Launching new Global Delivery Centers

Our drive to digitally transform our customers’ businesses is often manifested through our Global Delivery Centers, which expand our professional consulting and emerging practices capabilities available to customers and partners around the world.

The teams at our Global Delivery Centers help customers get up-and-running on Google Cloud quickly and cost effectively, and consult with customers to rapidly build capacity in areas like data analytics, hybrid and multi-cloud, artificial intelligence, and machine learning. More importantly, they help customers successfully execute projects in support of their most mission-critical business objectives. Critically, these centers also help our global partner ecosystem quickly ramp their Google Cloud practices, and get fast, expert consultation for their customers too.

In 2022, we aim to triple the size of our Global Delivery Center teams in Argentina, Poland, and India. In addition, we’ll invest in building deep Google Cloud talent in Mexico and Portugal, furthering our commitment to the industry’s best expert consultation for customers and partners around the world.

Expanding our Executive Briefing Centers footprint

In addition to our Global Delivery Centers, we’re also pleased to expand our resources and facilities that enable digital and face-to-face meetings and working sessions with our customers. This year, we will launch four new Executive Briefing Centers, located on Google Cloud campuses in London, Paris, Singapore, and Munich. 

These centers provide an opportunity to listen to our customers, share the best of Google Cloud’s solutions, and inspire digital transformation, in conversations facilitated by Google Cloud leadership, engineers, and industry experts. By bringing this experience to our customers in-region we can foster deeper partnerships and develop cloud solutions that meet their requirements for security, privacy, and digital sovereignty without compromising on functionality or innovation. 

Adding new leadership to enable customer success

Finally, I’m excited to welcome two new leaders to Google Cloud on the Customer Experience team, who will help scale our Delivery Centers and deliver exceptional experiences for our customers.

Heading our Global Delivery Center experience is Sunil Rao. Sunil comes to Google Cloud from Accenture, where he spent 18-plus years working with large technology customers across many industry verticals and managed large global teams in Accenture Advanced Technology Center. Sunil will also lead our Technical Onboarding Center, which helps businesses around the world get up to speed with technologies that are critical to understanding customer and business process contexts, and delivering great experiences.

Additionally, Lee Moore is joining Google Cloud to lead Customer Experience in North America. Lee spent nearly 30 years at Accenture in various leadership positions, including services integration for complex problem-solving, product development across a number of industry verticals, and building long-term customer relationships.

You’ll be hearing much more from us in the coming months, as we build out even more powerful and effective cloud-based services and offerings, work with customers to deliver new analytics- and AI-based tools and services, and work with our growing list of partners to help ensure customer success, across the globe.