Ali Reshadsedghi; Abolfazl Nasseri; Khosro Mohammadi Ghermezgoli
Abstract
In Iran, salinity is a pervasive issue limiting production of agriculture, so that a large part of the arid and semi-arid regions of the country have saline sodic soils with different levels. Saline soils and waters are among the agricultural resources that can be used for cultivation by using full recognition ...
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In Iran, salinity is a pervasive issue limiting production of agriculture, so that a large part of the arid and semi-arid regions of the country have saline sodic soils with different levels. Saline soils and waters are among the agricultural resources that can be used for cultivation by using full recognition of problem and proper management. This study was performed to evaluate a special grain drill performance which plants wheat seeds into furrows for semi-arid regions with saline soils conditions in margin of Uremia Lake. The experimental treatments of planting method by the grain drill included, (i) planting into the furrows with 60 cm width and furrow irrigation; (ii) planting into the furrows with 100 cm width and furrow irrigation; and (iii) planting on a flat soil and flood irrigation. Statistical analysis was conducted based on randomized complete block design with three replications. Seed emergence rate, crop performance indices, water consumption, water productivity, and soil salinity distribution after each irrigation practice were measured. According to the results, there was no significant difference between the methods of planting in any of the measured parameters at the 5% probability level. Results also showed that reducing the width of the furrow from 100 to 60 cm caused salinity reduction (about 37 percent) from inside the furrows. The water productivity of the planting into the furrows with 60 cm width was about 40 percent higher than those of other treatments. Therefore, wheat planting method by the grain drill into the furrows with 60 cm width can be recommended in semi-arid regions with saline soils.
Ali Reshadsedghi
Abstract
The effects of soil moisture content, forward speed and operation depth during harvesting by a potato digger (without conveyor shaker) on tubers quantitative and qualitative damages were studied and the optimum conditions were determined for treatments. The experiments were arranged as a strip split ...
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The effects of soil moisture content, forward speed and operation depth during harvesting by a potato digger (without conveyor shaker) on tubers quantitative and qualitative damages were studied and the optimum conditions were determined for treatments. The experiments were arranged as a strip split plot test based on complete block design with three factors and three replications. Mechanical damage rates of potatoes due to mechanized and traditional (manual) harvesting methods were compared by t-test. Buried tubers rate (quantitative loss) was increased with increasing of forward speed especially in wet soil probably due to the excessive soil transferred to the conveyor. Peeled tubers rate was reduced by increasing forward speed to 3 km/h in soils with low moisture content. The optimum conditions for mechanized potato harvesting with minimal quantitative loss and damage rate to product quality obtained at soil moisture of 10-15% db (40-60% Field Capacity) and 2-3 km/h forward speed. The cutting damage of tubers in traditional harvesting method was more than mechanized one, while the peeling damage rate was less. Generally, external damage rate of tubers in traditional method was significantly more than that obtained from mechanized method.
Abstract
This study was conducted to achieve effective and low-cost technology for non-destructive grading of unshelled almonds in real time. A laboratory prototype of an intelligent online impact-acoustic system composed of a feeding unit, acoustical recognition unit, and pneumatic separator with an electronic ...
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This study was conducted to achieve effective and low-cost technology for non-destructive grading of unshelled almonds in real time. A laboratory prototype of an intelligent online impact-acoustic system composed of a feeding unit, acoustical recognition unit, and pneumatic separator with an electronic controller unit was constructed and tested. To evaluate system operation according to almond variety and class (hard, semi-soft, and soft), the effect of an acoustic signal generated by dropping the nuts onto a steel plate was captured by microphone and the amplitude, phase, and power spectral density were extracted from analysis of the sound signal in the time and frequency domains by means of fast Fourier transform. A multilayer perceptron neural network with a LM training function were used in all experiments. The classification accuracy using validation data was about 96.2% in the offline mode, but accuracy decreased to 88% in the online mode. This decrease in accuracy was probably the result of a difference in size and mass of the almond samples in the hard and semi-soft classes.
