Crop For Change!

Team Friesenberger

Kumar Ayush and Kalpesh Krishna

IIT Bombay

What's the idea?

  • Every piece of land in a village prefers certain crops over the others.
  • Farmers tend to specialize in growing a certain crop over others, over years of experience.
  • Why can't farmers pool in their resources, plant crops in the most optimal manner, and share profits?
  • We aim at predicting an optimal distribution using Deep Learning.

Deep Learning Model

  • All code was written in TensorFlow.
  • The network is a feed-forward neural network with one hidden layer having 1000 neurons.
  • Training is done using AdamOptimizer and exponentially decaying learning rate.
  • 16 softmax layers have been added as the final layer, each operating on a part of logits.
  • Average cross entropy has been used for training.
  • Trained for 25 epochs on generated data with batch size of 100. (Demo)

Data Generation

  • An inherent distribution of soil yield is generated.
  • A distribution of available seeds is generated randomly.
  • Greedy algorithm used to generate the data.
  • Algorithm on next slide.


  • Our webapp has 2 major components.
  • Crowd Sourcing - The first interface allows users to enter farming data. This is supposed to append the training data. 


  • Our webapp has 2 major components.
  • Optimal Yield - This file executes a TensorFlow evaluation script and prints out the distribution for each plot of land.
  • There is an interface to choose input parameters to be fed to the evaluation script. (Demo)


What's Left?

  • Server to host this app to make it deployable.
  • Convolutional Neural Network for training.
  • Login / Signup - Multiple users
  • Dynamic Number of Crops / Plots
  • SMS web service to make it easy for villagers.

Other Applications

  • These core ideas can be applied to any form of resource distribution in villages.
  • In a futuristic world, when urban terrace farming becomes popular.

Thank You!


By Kalpesh Krishna


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