Abstract: Agriculture is the basic source of food supply in all the countries of the world whether under-developed, developing or developed. Besides providing food, this sector has contributions to almost every other sector of a country. According to the Bangladesh Bureau of Statistics (BBS), 2017, about 17% of the country’s Gross Domestic Product (GDP) is a contribution of the agricultural sector, and it employs more than 45% of the total labor force. In light of the decreasing crop production and shortage of food across the world, one of the crucial criteria of agriculture now-a-days is selecting the right crop for the right piece of land at the right time. Therefore, in our research we have proposed a method which would help suggest the most suitable crop(s) for a specific land based on the analysis of the data of previous years on certain affecting parameters using machine learning. In our work, we have implemented Random Forest Classifier, Gaussian Naïve Bayes, Logistic Regression, Support Vector Machine, k-Nearest Neighbor, and Artificial Neural Network for crop selection. We have trained these algorithms with the training data and later these were tested with test dataset. We then compared the performances of all the tested methods to arrive at the best outcome.
My target is focused largely on agriculture. In agriculture, farmers play the most important role. When the price falls after the harvest, farmers face immense losses. A country's GDP is affected by the price fluctuations of agricultural products. Crop price estimation and evaluation are done to take an intelligent decision before farming a specific type of crop. Predicting the price of a crop will help in taking better decisions which results in minimizing the loss and managing the risk of price fluctuations. Therefore, the web app also includes a crop price predictor to predict crop prices for the next 12 months. In this paper, we predicted the price of different crops by analyzing the previous rainfall and WPI data. We used the decision tree regressor (Supervised machine learning algorithm) to analyze the previous data and predict the price for the latest data and estimate the price for the twelve months to come.
Keywords: Agriculture, Crop yield, Logistic Regression, k-Nearest Neighbors, ANN, price prediction, decision tree, crop price, regression, forecasting, machine learning.
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DOI:
10.17148/IJARCCE.2022.11590
