Application of Data Mining in Crop Yield Precision


Authors : Hasi Saha, G C Saha, Masum Billah, Najmul Hossain, Suraiya Yasmin

Volume/Issue : Volume 5 - 2020, Issue 2 - February

Google Scholar : https://goo.gl/DF9R4u

Scribd : https://bit.ly/2Sf8sh6

Abstract : Since Bangladesh is an agrarian nation, its economy for the most part relies upon farming yield growth and social agro industry items. Agriculture is to a great extent affected by some profoundly flighty parameters such as temperature and rainwater in Bangladesh. Growth of agriculture also depends on weather parameters like temperature, rainfall, humidity as well as various soil parameters, soil moisture, surface temperature and crop rotation. Since, now-a day’s Bangladesh is quickly progressing into specialized advancement therefore technology will end up being beneficial to agriculture that will expand crop efficiency which brings about better productions to the farmers. One of those is also an essentially significant task in agricultures’ yield prediction. Before the cultivation process the research suggests different area based beneficial crops. It recommends some crops for a specific territory of land that are financially savvy for farming. Here, the study considered six main crops which names are rice, wheat, maize, potato, pulses, and oil seeds in order to achieve these results. Using Supervised Machine Learning, we find out the prediction through analyzing a static arrangement of information. The static dataset contains previous year’s data of those crops according to the area which are taken from the Yearbook of Agricultural Statics in Bangladesh. To obtain this prediction a comparative analysis between Multiple Linear Regression (MLR) and K-Nearest Neighbor Regression (KNNR) has been done in this research. To guarantee learning and preparing of the algorithm and expanding the exactness pace of expectation, we utilized past ten years (2006- 2015) dataset and for the case of testing we used one year (2016) dataset for computing accuracy.

Keywords : Data Mining; Multiple Linear Regression; K- Nearest Neighbor Regression; Crop Yield; Precision.

Since Bangladesh is an agrarian nation, its economy for the most part relies upon farming yield growth and social agro industry items. Agriculture is to a great extent affected by some profoundly flighty parameters such as temperature and rainwater in Bangladesh. Growth of agriculture also depends on weather parameters like temperature, rainfall, humidity as well as various soil parameters, soil moisture, surface temperature and crop rotation. Since, now-a day’s Bangladesh is quickly progressing into specialized advancement therefore technology will end up being beneficial to agriculture that will expand crop efficiency which brings about better productions to the farmers. One of those is also an essentially significant task in agricultures’ yield prediction. Before the cultivation process the research suggests different area based beneficial crops. It recommends some crops for a specific territory of land that are financially savvy for farming. Here, the study considered six main crops which names are rice, wheat, maize, potato, pulses, and oil seeds in order to achieve these results. Using Supervised Machine Learning, we find out the prediction through analyzing a static arrangement of information. The static dataset contains previous year’s data of those crops according to the area which are taken from the Yearbook of Agricultural Statics in Bangladesh. To obtain this prediction a comparative analysis between Multiple Linear Regression (MLR) and K-Nearest Neighbor Regression (KNNR) has been done in this research. To guarantee learning and preparing of the algorithm and expanding the exactness pace of expectation, we utilized past ten years (2006- 2015) dataset and for the case of testing we used one year (2016) dataset for computing accuracy.

Keywords : Data Mining; Multiple Linear Regression; K- Nearest Neighbor Regression; Crop Yield; Precision.

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