Anomaly Detection

Problem Statement:

With the growing emphasis on sustainable farming, maximizing residual biomass becomes paramount. However, undetected anomalies in agricultural processes can lead to wastage and reduced biomass yields.


By harnessing machine learning algorithms, we've developed a system that continuously monitors various agricultural metrics and flags any irregular patterns. Once detected, the system suggests corrections to enhance efficiency and optimize for higher biomass residuals.


  • Real-time Anomaly Detection: Continuous monitoring and instant alerting on irregularities.
  • Predictive Analysis: Using past data to predict potential future anomalies.
  • Optimization Recommendations: Suggesting process alterations to maximize biomass.
  • Interactive Dashboards: Visual representations of agricultural metrics and anomalies.

Use Cases:

  • Sustainable Farming: Ensuring minimal wastage and maximizing biomass for green energy projects.
  • Quality Control: Maintaining a consistent quality of produce by minimizing anomalies.
  • Efficiency in Farming Operations: Reducing costs related to wastage and sub-optimal processes.

Data Science Specific Points:

  • Data Collection: Data was sourced from multiple farms using IoT devices capturing soil quality, weather conditions, crop health, and more.
  • Data Analysis: Utilized deep learning models to discern patterns and detect anomalies within large datasets. Historical data was used to train the model to recognize typical patterns and thus detect deviations.
  • Results: Achieved a substantial reduction in undetected anomalies, leading to an average increase of 18% in residual biomass across collaborating farms.

Technologies Used:

  • Programming Languages: Python
  • Frameworks: Custom Algorithms
  • Database Systems: PostgreSQL, Kafka