Agribot: A Natural Language Generative Neural Networks Engine for Agricultural Applications
Bhavika Arora, Dheeraj Singh Chaudhary, Mahima Satsangi, Mahima Yadav, Lotika Singh, Prem Sewak Sudhish
Abstract
In this paper, we present an artificially intelligent chatbot which would help farmers by providing solutions to various farming related problems and facilitate their decision-making process. The bot not only provides answers to frequently asked questions but also lays emphasis on crop disease detection and weather forecasting. We developed an end to end trainable sequence-to-sequence learning model with the objective of achieving conversational task-oriented system based on minimal assumption on its sequence structure. Our approach exploits a multilayered Long Short-Term Memory (LSTM) unit which maps the input sequence to a corresponding output sequence by converting it into a vector of fixed dimensionality in between. To achieve the disease detection, a Convolutional Neural Network architecture is implemented in which a multilayered architecture is developed and trained from scratch which would classify the plant images into different classes. For conversational system module we have used the Kisan Call Center (KCC) dataset which contains logs of calls at KCC by farmers whereas for disease detection module plant village dataset is used. After training 98% accuracy was achieved for conversational system module on training data and 94% for disease detection module on test data.