TY - GEN
T1 - Crop Yield Analysis Using Machine Learning Algorithms
AU - Haque, Fatin Farhan
AU - Abdelgawad, Ahmed
AU - Yanambaka, Venkata Prasanth
AU - Yelamarthi, Kumar
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/6
Y1 - 2020/6
N2 - Agriculture is not only a huge aspect of the growing economy, but it's essential for us to survive. Predicting crop yield is not an easy task, as it depends on many parameters such as water, ultra-violet (UV), pesticides, fertilizer, and the area of the land covered for that region. In this paper, two different Machine Learning (ML) algorithms are proposed to analyze the crops' yield. These two algorithms, Support Vector Regression (SVR) and Linear Regression (LR), are quite suitable for validating the variable parameters in the predicting the continuous variable estimation with 140 data points that were acquired. The parameters mentioned above are key factors affecting the yield of crops. The error rate was measured with the help of Mean Square Error (MSE) and Coefficient of Determination (R2), where MSE gave out approximately 0.005 and R2 gave around 0.85. The same dataset has been used for quick comparison between the algorithms' performances.
AB - Agriculture is not only a huge aspect of the growing economy, but it's essential for us to survive. Predicting crop yield is not an easy task, as it depends on many parameters such as water, ultra-violet (UV), pesticides, fertilizer, and the area of the land covered for that region. In this paper, two different Machine Learning (ML) algorithms are proposed to analyze the crops' yield. These two algorithms, Support Vector Regression (SVR) and Linear Regression (LR), are quite suitable for validating the variable parameters in the predicting the continuous variable estimation with 140 data points that were acquired. The parameters mentioned above are key factors affecting the yield of crops. The error rate was measured with the help of Mean Square Error (MSE) and Coefficient of Determination (R2), where MSE gave out approximately 0.005 and R2 gave around 0.85. The same dataset has been used for quick comparison between the algorithms' performances.
KW - Error
KW - Linear model
KW - Machine Learning
KW - Regression
KW - SVR
UR - http://www.scopus.com/inward/record.url?scp=85095601870&partnerID=8YFLogxK
U2 - 10.1109/WF-IoT48130.2020.9221459
DO - 10.1109/WF-IoT48130.2020.9221459
M3 - Conference contribution
AN - SCOPUS:85095601870
T3 - IEEE World Forum on Internet of Things, WF-IoT 2020 - Symposium Proceedings
BT - IEEE World Forum on Internet of Things, WF-IoT 2020 - Symposium Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 6th IEEE World Forum on Internet of Things, WF-IoT 2020
Y2 - 2 June 2020 through 16 June 2020
ER -
