Cotton Crop Disease Detection using Machine Learning via Tensorflow
Nimra Pechuho, Qaisar Khan, Shoaib Kalwar
Abstract
World population is expected to be 10 billion in 2050. With more mouths to feed, agriculture needs to boost up to meet the food requirements. However, developing countries like Pakistan has seen a decline in their production of the crops. One of the main reasons behind declined in the production of the cotton crop is the damage caused by cotton diseases. Our model is giving farmers an easy and efficient method to diagnose cotton diseases and will recommend the usage of pesticides. It is based on machine learning, which learns with every use. Agriculture needs innovative ideas to increase its yield. CottonCare (Cotton Crop Disease Detection using Deep Learning via TensorFlow) is also one of the steps to integrate artificial intelligence into agriculture. The goal of this project is to help the farmers in decreasing the production cost and achieving the higher yield, which is also going to contribute to the country’s economy.