Ontology inspired model for tea yield prediction using machine learning techniques

Abstract

Agriculture is an essential foundation for the economy of a nation. In India, newlinetea is the only extensively consumed beverage. India comes under the top five tea newlineproducing country in the World by grabbing the second largest position globally. newlineThe tea industry in India is a significant source of employment and revenue, and newlineany variation in crop yield can have a major consequence on the economy of India. newlineTea is a staple crop in India, and its yield also affects the food security of the newlinecountry. Accurate crop yield prediction can help researchers and policymakers to newlineidentify areas where improvements can be made in tea cultivation, such as newlinedeveloping new varieties, improving agricultural practices. Tea crop yield newlineprediction can also help farmers to acclimate the influences of changing rainfall newlinepatterns, rising temperature and other variations. newlineNumber of models are used for predicting crop yields. These models can be newlinewell distinguished into two main categories. Crop growth models and the data newlinedriven models. The crop growth models are effective for predicting yields, as they newlineface major limitations because one has to understand the full crop biology in detail. newlineThese models tend to be expensive and often fail to provide reproducible results in newlineactual fields due to varying environmental conditions. Data-driven models, on the newlineother hand, are empirical mathematical models where the values of dependent newlinevariables are determined based on different predictor variables. These models are newlinecost-effective, and the emergence of machine learning algorithms has further newlineimproved their efficiency. Diverse machine learning techniques have been utilized newlineextensively in the field of crop yield prediction. newline