yield estimation of sugar beet based on plant canopy using machine vision methods

crop yield estimation is one of the most important parameters for information and resources management in precision agriculture. this information is employed for optimizing the field inputs for successive cultivations. in the present study, the feasibility of sugar beet yield estimation by means of machine vision was studied. for the field experiments stripped images were taken during the growth season with one month intervals. the image of horizontal view of plants canopy was prepared at the end of each month. at the end of growth season, beet roots were harvested and the correlation between the sugar beet canopy in each month of growth period and corresponding weight of the roots were investigated. results showed that there was a strong correlation between the beet yield and green surface area of autumn cultivated sugar beets. the highest coefficient of determination was 0.85 at three months before harvest. in order to assess the accuracy of the final model, the second year of study was performed with the same methodology. the results depicted a strong relationship between the actual and estimated beet weights with r2=0.94. the model estimated beet yield with about 9 percent relative error. it is concluded that this method has appropriate potential for estimation of sugar beet yield based on band imaging prior to harvest