About AgroStar

Launched as an on-premises ecommerce platform selling farm supplies in 2008, AgroStar turned to Google Cloud to expand its offering. Today one million small farmers in India use its mobile app to connect to a full-service platform that combines agronomy, data science, machine learning, and analytics to boost yields and improve income.

Industries: Industries: Agriculture, Forestry & Fishing

Location: India

AgroStar launched a multilingual mobile app using Google Cloud that is helping to boost crop yields and increase income for small farmers in India.

Google Cloud results

• Enables technology modules to speed loan processing, identify crop diseases, and enhance supply chain logistics
• Delivers high rates of analytics processing to serve more customers while improving response times by 85%
• Streamlines deployments with no downtime to free time and resources for new verticals
• Supports knowledge center updates for 1 million mobile devices in near real time by implementing Firebase

An app to boost crop yields and raise income
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 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 Google Cloud, 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.

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 Google Cloud 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 Google Cloud.

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 Google Cloud 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. Google Cloud 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 Google Cloud, 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 Google Cloud 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.”

Contributors to this story

Pritesh Gudge: AgroStar Software Engineer. Pritesh was formerly a founder of Bauersafe, a farm protection solutions startup, and First Fit, a startup that focuses on mobility and functional techniques to promote general fitness and recovery. Pritesh earned a B.S. in Computer Science and Engineering at the Birla Institute of Technology and Science as well as degrees in Robotics and Deep Learning from Udacity.

At Google Cloud, we’re focused on making it easy for organizations to build solutions quickly and securely. Identity and Access Management (IAM) is the core security control for establishing who has access to which cloud resources and making sure access permissions are aligned to your company’s business and security policies.

We are excited to announce the general availability of IAM Deny policies. This new capability helps you easily create access guardrails for Google Cloud resources. With IAM Deny policies, you can create rules that broadly restrict resource access. It provides a powerful, coarse-grained access control to help implement security policies at scale.

Control with IAM Policy – Allow and Deny

IAM Deny policies complement IAM Allow policies as you define access to your resources. Google Cloud’s IAM Allow policy lets you grant granular access to Google Cloud resources. The more coarse-grained Deny policies let you explicitly prohibit access to certain resources regardless of existing Allow rules. IAM Deny policies always supersede IAM Allow policies and override conflicting IAM Allow rules.

IAM Deny policies can be applied across many Google Cloud resources, such as Compute Engine, Cloud Storage, and Google Cloud Kubernetes Engine. These policies can help reduce toil on administrators as they can set up deny rules that will be enforced at scale without requiring reviews and changes of existing access rules. This makes resource governance simpler.

How to strengthen your security posture with IAM Deny

There are multiple use cases where IAM Deny policies can be used to help strengthen posture. Some of these are:

• Establish a default security baseline: IAM Deny can be used to set base policies at the organization level, folder level, and project level to deny access to resources. For example, you can attach a deny policy at the organization level to deny all users access to sensitive data storage buckets, making an exception for a specific user group.
• Prevent backdoors: IAM Deny rules override any IAM Allow rules. This can help you ensure that no “backdoor” access can be granted. For example, an organization that wants to ensure only central-admin can create projects in a folder can add an IAM Deny policy that restricts all users from creating projects except central-admin. This can help ensure no backdoor users will be added using allow policy rules.
• Prevent data exfiltration: Controlling access to data is a key measure to prevent exfiltration. For example, an organization that wants to restrict access to their personally identifiable information (PII) can set an IAM Deny policy on resources containing PII that denies access to all users except those in a group called PII-admin.
• Demonstrate compliance: Many industry regulations and compliance frameworks require organizations to demonstrate least-privilege access to sensitive resources. IAM Deny policies can create a baseline access restriction that always takes precedence when applied to resources.

Simplify IAM administration

IAM Deny can also help simplify common administrative tasks. It provides a list of permissions for Google Cloud services that you can readily use in your deny policies. Here are a few common situations where you can use IAM Deny to help streamline your access management.

• Centralize administrative privileges: You can use deny policies to restrict certain types of administrative activities to specific users or user groups. For example, if you want to limit custom role management to a single central team, you can create a deny rule that denies the permissions required for custom role management to all users, except users in the central-admin group. This will only let members of the central-admin group manage custom roles, even if other users have the required permissions.
• Create exceptions to access grants: You can use deny policies to deny inherited permissions. For example, you can grant a role at a high level in the resource hierarchy, and then deny the role’s permissions on individual lower-level resources if necessary.
• Block access based on tags: Google Cloud supports key-value based tags and you can use deny policies to deny permissions based on tags without adding an IAM Condition to every role grant. For example, you can create a rule to deny delete permissions for resources tagged as “production” for everyone except project-admins.

IAM Deny in action

The following example illustrates how a deny policy can be used to block all users from deleting projects unless the user is a member of a project-admins group or the project being deleted is tagged as a “test” project. Without a deny policy, you would have to manually track all members who have the permission to delete projects, and ensure that no undesired user has access to this permission. The deny policy makes it easy for the administrator to build this guardrail.

The below deny rule denies permission to everyone except project-admins@example.com for projects not tagged as test. Add this deny rule to a deny policy and attach the policy at the org level. That’s it!

(Note that project-admins@example.com is a security group. Using security groups is a best practice that should be followed to help keep your resources secure.)

{
"name": "policies/cloudresourcemanager.googleapis.com%2Fprojects%2F253519172624/denypolicies/limit-project-deletion",
"uid": "06ccd2eb-d2a5-5dd1-a746-eaf4c6g3f816",
"kind": "DenyPolicy",
"displayName": "Only project admins can delete projects.",
"etag": "MTc1MTkzMjY0MjUyMTExODMxMDQ=",
"createTime": "2021-09-07T23:15:35.258319Z",
"updateTime": "2021-09-07T23:15:35.258319Z",
"rules": [
{
"denyRule": {
"deniedPrincipals": [
"principalSet://goog/public:all"
],
"exceptionPrincipals": [
"principalSet://goog/group/project-admins@example.com"
],
"deniedPermissions": [
"cloudresourcemanager.googleapis.com/projects.delete"
],
"denialCondition": {
"title": "Only for non-test projects",
"expression": "!resource.matchTag('12345678/env', 'test')"
}
}
}
]
}

Getting started with IAM Deny Policies

With IAM Deny, you now have a powerful new capability to build security guardrails and more effectively control access to your Google Cloud resources. IAM Deny is now generally available for all customers through gCloud and APIs. We offer IAM Deny to Google Cloud customers at no additional cost. You can learn more about IAM Deny by visiting our documentation page.

Businesses that operate in complex cloud environments, large fleets, or sophisticated security operations all require visibility into their cloud assets in order to keep their teams nimble and their data secure. Cloud Asset Inventory (CAI) helps these teams understand their Google Cloud  and Anthos environments by providing complete visibility, real-time monitoring, and powerful asset analysis capabilities. Today, Cloud Asset Inventory gets four new capabilities that help you understand your environment more clearly and easily than ever before.

New user interface eases asset and insight discovery 

Cloud Asset Inventory console preview is now publicly available for GCP and Anthos customers. This preview provides insights into your cloud footprint, history and details of resource usage with powerful filtering and search capabilities. For example, you can view your global distribution of resources and policies, how your GCE VM footprint has been changing over time, as well as full metadata and change history for all your assets. The CAI console can be filtered at the organization, folder, or project-level, so each user can view the resources they have permissions for down to project level granularity.

Asset discovery and Datadog integration

A new asset list service in CAI provides quick and comprehensive asset discovery, including asset history, without needing to export the data to a storage destination. Datadog, a leading multi-cloud monitoring and security service provider, relies on deep integration with CAI for service and asset discovery. Datadog has been piloting and taking full advantage of the newly released asset list service. Datadog Product Manager, Steve Harrington, commented: 

“Google’s new Cloud Asset Inventory API provides us with an immensely valuable, single source of truth for determining the resources present in a given GCP environment. Along with its rich metadata, this enables us to enhance multiple aspects of our integration with GCP, including streamlined metric collection and ingestion of custom labels. We plan to continue building around Cloud Asset Inventory in the future to improve existing features, and are envisioning ways it could help us provide entirely new insights to our customers.”

Answer “who can access what resources?”

Determining authoritative answers to security-related questions like “Who can read data from my storage bucket that contains PII?” or “Does a terminated employee still have any remaining access to my system?” can be difficult and time consuming. This is why access management and identity certification is one of the top security priorities for enterprises running workloads in the cloud. To help alleviate this challenge, the new Policy Analyzer capability in CAI thoroughly analyzes the relationship between IAM policies and resources. The analysis includes powerful and efficient group expansion, service account impersonation, conditional access analysis, resource expansion, and more. You can even export the results to a BigQuery table or Cloud Storage bucket for further analysis and record keeping. CAI’s enhanced UI makes it even easier for you to build your own flexible queries and quickly get to a comprehensive answer.

Create asset posture visibility 

Cloud Asset Inventory now provides seven types of Asset Insights through the Active Assist platform. These new asset insights help proactively detect anomalies within your organization’s IAM policies, which may be opportunities to improve your security posture. The insights can be aggregated at the Organization, Folder or Project level. 

The seven new Asset Insights include: 

• External members in IAM policies. 
• External users that impersonate your service accounts.
• External members as policy editors.
• External users who can view cloud storage buckets.
• Terminated users/groups that are still in IAM policies
• IAM policies containing all users or all authenticated users.
• Projects with only terminated users as owners.

As a Google Cloud customer you can get started and use all the recently released capabilities and features immediately; check out our documentation to see how. We’d love to hear your feedback; email us with any questions or concerns!