Deep Neural Network Model for Proficient Crop Yield Prediction

Abstract

Crop yield forecasting mainly focus on the domain of agriculture research which has a great impact on making decisions like import-export, pricing and distribution of respective crops. Accurate predictions with well timed forecasts is very important and is a tremendously challenging task due to numerous complex factors. Mainly crops like wheat, rice, peas, pulses, sugarcane, tea, cotton, green houses etc. can be used for crop yield prediction. Climatic changes and unpredictability influence mainly on crop production and maintenance. Forecasting crop yield well before harvest time can help farmers for selling and storage. Agriculture deals with large datasets and knowledge process. Many techniques are there to predict the crop yield. Farmers are benefited commercially by these predictions. Factors such as Geno type, Environment, Climatic conditions and Soil types used in predicting the Yield. For predicting accurately we need to know the fundamental understanding and relationship between the interactive factors and the yield to reveal the relationships between the datasets which are comprehensive and powerful algorithms. Based on the study of various survey papers it has been found that in all the crop predictions, various deep learning, machine learning and ANN algorithms implemented to predict yield forecast and the results are analyzed.

An Innovative Hybrid Approach for Predicting Crop Yields across Diverse States and Crops

“…LSTMs feature feedback connections that may be used to create a more conventional feed-forward neural network. LSTMs excel in processing sequences of data and are hence well-suited for text, voice, and general time series [17].…”

“…The judgments made by a recurrent neural network in the past are remembered and used to inform their present actions. They accept a vector of inputs and provide a vector of outputs [17].…”