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.”

May is Asian American Pacific Islander Heritage Month — a time for us to come together to celebrate and remember the important people and history of Asian and Pacific Island heritage. This feature highlights founder Georgene Huang and how her startup Fairygodboss uses easy to use Google Cloud tools to grow and innovate. 

Coming from a male-dominated industry like Wall Street, I never thought much about my gender. I was so focused on work that I didn’t feel like I could focus on my identity as a woman of color. That all changed when I found myself thrust into the job market while two months pregnant.

Traditionally, job-seeking sites are built by – and geared towards – men and their professional needs. There was no recruiting site that broke down what candidates who identify as women might expect from a company, let alone women of color like myself. I founded Fairygodboss to create a space where women can crowdsource information about opportunities and employers in order to make informed career decisions. 

Fairygodboss 

Fair•y•god•boss (noun): A person who elevates women at work.

Fairygodboss is the largest career community for women. In researching potential employers, I had many questions surrounding company culture, maternity leave, and women in leadership positions. While sites like Glassdoor provide high-level details for job seekers, I wanted more concrete examples and data than general salary information and anecdotes about work-life balance. 

Fairygodboss began as a job review site geared towards women, but has expanded significantly since our initial launch. In addition to providing free resources like career connections, job listings, virtual recruiting events, community advice, and real research on how companies treat women, we also proactively help companies improve gender diversity. Fairygodboss partners with hundreds of top employers to create a 360-degree recruiting and employer branding program designed to drive female applicants to job postings by showcasing a company’s track record of women in leadership within a company, and their diversity investments. 

Google Cloud + Fairygodboss: Data defined 

At our core, Fairygodboss is a data-driven company, and we rely on Google Cloud to help us process, store, and analyze user-driven events occurring on the site. As a highly scalable, serverless, and cost-effective data warehousing solution, BigQuery collect millions of events each day, helping us identify where traffic is coming from (and where it goes next), and it produces actionable insights to provide better recommendations, information, and opportunities for women visiting our platform. 

After seamlessly connecting our BigQuery data to Data Studio, we have been able to visualize and make sense of all the data we receive on our platform. Unlike some other data visualization tools, Data Studio allows us to analyze data in real time. With tools like BigQuery, we are now able to identify trends and gain a deep understanding for how our users interact with our site. 

Google for Startups Accelerator: Women Founders 

In order to provide more accurate job recommendations for our users, the Fairygodboss team wanted to build a machine learning prototype to classify our users and jobs into categories to facilitate more effective matching. So we applied for the Google for Startups Accelerator: Women Founders, a three-month digital accelerator program for high-potential Seed to Series A tech startups based in the U.S. and Canada. Along with tailored mentorship and product support from Googlers and industry experts, the Google for Startups Accelerator provided $100K in Google Cloud credits to scale our businesses. The mentorship we received from the Google Cloud technical experts as part of the Accelerator – special shout out to Peter Novig! – empowered us to integrate Cloud AutoML into our systems and ultimately curate more accurate job suggestions for our users.  

Not only did the Google for Startups Accelerator program help to achieve our business goals for the year, it was also extremely beneficial to be connected with a cohort of other women founders of color. While we are all building different kinds of businesses across different industries, the guidance around fundraising, scaling teams, and coping with the struggles of being a founder rang true for all of us. 

Advancing the community

It can be daunting to launch your own business – and even more so as an AAPI woman. Throughout my career, I am certain I have experienced unconscious bias around my race. Specifically, there have likely been “model minority” stereotypes about my demeanor and math abilities, assumptions that I may be mild-mannered or agreeable. While frustrating, these unfair tropes actually inspired me to see myself beyond others’ perceptions. Why be either analytical or creative, meek or brash, inspiring or agreeable? It’s the ors of life that prevent us from seeing ourselves as truly multi-dimensional individuals. I aspired to be more instead of or, and want my company to be as well. 

As an inclusion-focused business, it is extremely important that Fairygodboss mindfully engage with all underrepresented groups. Diversity can be sliced in many different ways, and these intersections lead to great opportunities for change. We have a team of volunteer ‘culture wizards’ at work that provide educational resources and videos for those wanting to learn more about different cultures and identities from members of under-represented groups. We also practice what we preach at Fairygodboss by prioritizing diverse slates and diversity in our own hiring process. We aim for the talent we hire to be representative of the diverse communities we support. It is imperative to take an intentional approach to hiring diverse talent, as it falls on all employers to promote equity and inclusion—there are always ways in which we can all improve. Fairygodboss looks forward to evolving with Google Cloud in 2021 and beyond as we work together to foster inclusive teams around the world. 

If you want to learn more about how Google Cloud can help your startup, visit our Startup Program application page here and sign up for our monthly startup newsletter to get a peek at our community activities, digital events, special offers, and more.