WISRAN
Capture Labor and Other Efficiencies in Agriculture
Time & Duration
12 Months
Platform
RoR,Postgres,Nginx
Industry
Ag Tech
Team Members
7 Members
Time & Duration
12 Months
Platform
RoR,Postgres,Nginx
Industry
Ag Tech
Team Members
7 Members
WISRAN
Wisran is a revolutionary startup idea, selected by MIT boot camp. It uses data science to measure time variations of farming.
CHALLENGES
Limited time and budget
Provided with all of its complexity, we had to prove our idea, by developing a minimum viable product, within a season, i.e. 3 months as we didn’t have enough budget to record data of whole year farming cycle covering all processes and seasons.
Real-time activity mapping
As different industrial farming vehicles, each having different sizes, speed and movement actions were involved even in a single process, identification of current activity was dependent on many variables. We had to develop a rule engine for analyzing a broad set of variables and determining current activity and its relative stage.
Scalability
Scalability was a real challenge as adding a single node in the system adds a huge volume of data. We had to design a decoupled architecture that keeps all software components, along the data pipeline and data aggregation engine in their optimally efficient state and ensures the analysis, aggregation, and computation on a vast set of data provides a real-time picture.
Complex Business Domain
RESULTS
Wisran is live with an Android and an iOS app. It has now successfully automated 56713 acres of industrial agriculture fields and made its way to Australia by winning a grant from the Australian government.
SOLUTIONS
Limited time and budget
Our years-long experience of managing medium and large complex projects helped us in keeping this project on its planned trajectory. We followed Agile processes and divided relevant stories in sprints with complete focus on adding maximum business value in each sprint.
Real-time activity mapping
We used Apache Kafka and Nifi to receive and channelize data streams, being relayed towards our rule engine powered by Apache spark, that analyzed and made aggregations based on complex rules at high speed.
Scalability
The decoupled infrastructure design rescued us. We designed the system in three layers: a data pipeline, a core engine, and a data layer. Each of these layers is a multi-node system, and we can add multiple nodes in each layer according to the system demand, anytime.
Complex Business Domain
Our onsite manager visited the original site and conducted dozens of interviews with clients, business stakeholders and vehicle operators. Various interactive meetings and brainstorming sessions were held. With the well-organized collaborative effort, we were able to convert business requirements to stories and epics.
Equipment Monitoring:
It helps to remotely check the mixed fleet of various models of trucks and other equipment.
Automated Guidance:
Automated guidance related to the inefficiency of the field is provided to offer more jobs in less time.
Analysis of Financial Impact:
WISRAN uses technology for analyzing the financial effects of fieldwork performance.
Maximum Profit:
The actual time and cost per acre are tracked to get the maximum profit.
Technology stack
Frontend
HTML5
CSS
Javascript
Bootstrap
Objective-C
Swift
REST
Testing
Jira
Confluence
Bitbucket
Crashlytics
Server
AWS
S3
Apple Store
Puma
Nginx
Backend
Ruby on Rails
Google Map
GraphQL
Postgres GIS
RSpec
Firebase
Apache Spark
Apache Kafka
Apache Kafka
Database
PostgreSQL
Wisran Review
Arslan Lodhi
C.E.O Wisran
Salman and team at Clustox has always gone beyond what was asked. Their differentiation is that they come back with great ideas to enhance user experience and product improvements.
Do you want to get anything like that?
