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ABSTRACT
In Indian economy and employment agriculture plays major role. The most common problem faced by the Indian farmers is they do not opt crop based on the necessity of soil, as a result they face serious setback in productivity. This problem can be addressed through precision agriculture. This method takes three parameters into consideration, viz: soil characteristics, soil types and crop yield data collection based on these parameters suggesting the farmer suitable crop to be cultivated. Precision agriculture helps in reduction of non-suitable crop which indeed increases productivity, apart from the following advantages like efficacy in input as well as output and better decision making for farming. Crop yield prediction incorporates forecasting the yield of the crop from past historical data which includes factors such as temperature, relative humidity, ph., rainfall and area (Hectares). This method gives solutions like proposing a recommendation system through an ensemble model with majority voting techniques using Random Forest and K Nearest Neighbor as learner to recommend suitable crop based on soil parameters with high specific accuracy and efficiency
INTRODUCTION
Agriculture is the one amongst the substantial area of interest to society since a large portion of food is produced by them. Currently, many countries still experience hunger because of the shortfall or absence of food with a growing population. Expanding food production is a compelling process to annihilate famine. Developing food security and declining hunger by 2030 are beneficial critical objectives for the United Nations. Hence crop protection; land assessment and crop yield prediction are of more considerable significance to global food production. This project uses Python 3.6 for the programming in a scientific development environment called the PyCharm. Various data manipulation, machine learning and visualization packages are used to create and analyze the dataset using a traditional machine learning model. A Data Visualization tool called Tableau is used to interpret the results provided by the model after analysis to represent and act as a proof for the intended result.
