Predicting the pepper harvest

We developed a regression ML model based on historical data and weather data.




Feb 15, 2024





The Brief

In the agricultural sector, precision and foresight are paramount. Our latest endeavor, "Predicting the Pepper Harvest," exemplifies our commitment to innovation in aiding farmers to optimize their yields. Leveraging machine learning (ML) techniques, we developed a robust regression model within an astonishingly brief span of two weeks. This model, fueled by historical data and enriched with external weather variables, revolutionizes the predictive capacity of pepper harvests, elevating certainty levels from a previous standard of under 50% to an impressive 70%.

Developing a Data-Driven Solution:

Our journey commenced with a clear mission: to redefine pepper harvest predictions through data-driven insights. Recognizing the critical role of data, we meticulously curated historical harvest records and supplemented them with external weather data, including factors such as light hours outside the greenhouse. This comprehensive dataset served as the foundation for our regression ML model, empowering it with a holistic understanding of the variables influencing pepper yields. Through agile development methodologies and a relentless focus on efficiency, we expedited the model's development, delivering a robust solution within a mere two weeks.

Visualizing Performance with Sagemaker Canvas:

Integral to our approach was the integration of Sagemaker Canvas, a powerful tool that provided us with a visual interface to monitor and analyze the performance of our ML model. Through intuitive graphs and charts, we gained valuable insights into the model's predictive capabilities, enabling us to fine-tune its parameters and enhance accuracy further. This visual representation not only facilitated informed decision-making but also empowered our clients to grasp the intricacies of our predictive model effortlessly. With Sagemaker Canvas, we transcended traditional data analysis boundaries, offering our clients unparalleled transparency and confidence in our solutions.

Enriching Predictive Capabilities:

A distinguishing feature of our approach was the integration of external weather data, such as light hours outside the greenhouse. By expanding the scope of our model to encompass environmental factors beyond the growing environment, we achieved a more comprehensive understanding of the conditions impacting pepper growth. This holistic data enrichment not only bolstered the accuracy of our predictions but also underscored our commitment to leveraging cutting-edge technology to drive agricultural innovation. Ultimately, our regression ML model emerged as a beacon of reliability, transforming pepper harvest forecasting and paving the way for sustainable agricultural practices.

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