Crop Yield Analysis Using Machine Learning Algorithms

F. F. Haque, Kumar Yelamarthi, Venkata Prasanth Yanambaka, Ahmed Abdelgawad

Research output: Chapter in Book/Report/Conference proceedingConference contribution


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.
Original languageEnglish
Title of host publication2020 IEEE 6th World Forum on Internet of Things (WF-IoT)
StatePublished - Jun 2020


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