Image Processing
S. Latifaltojar; A. A. Jafari; S. M. Nassiri; H. Sharirfi
Abstract
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 ...
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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
Design and Construction
S. M. R. Nazemsadat; M. Loghavi
Abstract
In grain yield monitoring system, the amount of clean grain mass flow rate to the storage bin is the most important yield property. In this research, an impact-plate type grain mass flow sensor was designed, developed and evaluated. After construction of the impact sensor, it was calibrated by loading ...
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In grain yield monitoring system, the amount of clean grain mass flow rate to the storage bin is the most important yield property. In this research, an impact-plate type grain mass flow sensor was designed, developed and evaluated. After construction of the impact sensor, it was calibrated by loading the impact plate with static weights ranging from 0.5 to 4.5 kg every 0.5 kg and its linear response to the applied loads was proved with a correlation coefficient of 0.99. Then, grain mass flow measurement tests and data collection were conducted according to the ASABE standard S587, developed for grain mass flow sensors. The tests were conducted in three phases: 1- constant and steady state flow, 2- linear variation of flow, 3- oscillating flow. The results showed that the output of impact plate sensor varies proportionally and linearly with increasing wheat grain (Rowshan cultivar) mass flow rate. The error in prediction of actual flow rate was decreased by increasing the mass flow rate such that the calculated errors at 25%, 50%, 75% and 100% of flow capacity (4.25 kg s-1) were 8.3%, 6.3%, 5.2% and 4.9%, respectively. The high coefficient of determination (R2 = 0.9975) between accumulated mass flow data of impact plate sensor and the reference scale data indicated high accuracy and sensitivity of impact plate sensor in prediction of mass flow variations. The average percent error of impact sensor in variable flow rate in “ramp-up-ramp-down”, “ramp-down-ramp-up” and oscillating flows were 7.4%, 8.6% and 8.3%, respectively.
