Crop Yield Prediction using Machine Learning and Deep Learning Techniques

Abstract

Crop yield prediction has been designated as a major predictive analysis technique that increases the potential of the agricultural industry. The utilisation of such a measure has been important for the farmers to understand the yields of crops during the particular season from a data analytical point of view. Such an aspect has fallen under the concept of predictive analysis which allows the farmers, agricultures, and farming businessmen to make strategic decisions in terms of cultivation. The application of predictive analysis has been useful for understanding the specific set of crops to be sown during the season, and the types of fertilisers to be applied to the crops for an increased output. Risk analysis with the help of predictive modelling of crops helps in the improvement of the overall agriculture business and increases the potential of the farmers to improve their revenue collection. Once they have the potential of understanding the specific parameters of agriculture, the decision making for reducing the risks, increasing the overall gain from the crops, and such other aspects can be easily known. Predictive analysis allows the farmers to gain an expansive amount of knowledge regarding the weather conditions in the future, the quality of the soil for growing the crops, the nutrients required which are to be replenished for increasing the crop field, and several such parameters. Machine Learning or ML and Deep Learning or DL methods have been seen to be extremely important for data analysis and predictions. Several kinds of tools and techniques such as neural networking, Bi GRU, Maxout classifiers, and others have been applied within the agricultural industry. The study would lead to an extensive analysis of the different kinds of Machine Learning and Deep Learning techniques used for increasing the crop yields by prediction analysis. Such a measure would prove to be extremely important to make significant decisions regarding the importing and exporting of crops, and the pricing structure for the grains to be sold in the market. The distribution of crops and also making fruitful decisions regarding future crop plantations can also be inspected with the help of the ML and DL tools.