Digital Advisory Services for Rural Farmers: Phase III Developing ML Pipeline

Challenge Background

We have previously created a prototype for a single run of training for an LSTM model. This model in a full data set will take about 5TB of data and a large amount of RAM. Rearching a machine that can do this kind of work, lead us to the most affordable solution, Intel “Metacloud” and cnvrg.io machine learning operating system. Not having the funds previously to train the model, we secure funding eventually and want to kick off another phase of this project where we will train a real LSTM on Northern European soil, and begin to be able to pull weather API’s and such for digital advisory services for particular crops and other features in the software.

The Problem

We are looking at attempting to input a GPS coordinate, the system processes and segments the image into fields, and then uses the trained LSTM to identify which crops are being grown in the area to identify particular risks due to fungus, insects, and weather. The NDVI historical reading is also of some use to the farmer because they can monitor the growth of their crops as the weather and advisory conditions changes. This will be useful in fields to identify if weather or plant diseases have been affected by conditions.

Goal of the Project

Make LSTM pipeline, implement NDVI graphs, implement weather advisory, and monitor effects on NDVI for the Area of Interest.

Project Timeline

What you'll learn

Involvement with GIS technologies. Knowledge of SDLC in concerns with ML Life Cycle. Implementation of API's