Evaluation of an intelligent device for identifying cane losses from sugarcane harvester primary extractor Service Unavailable

Among the factors that affect the amount of crop losses are the methods and tools used to perform harvesting operations. Machine harvesting of sugarcane is done in two ways: green and burnt, and usually burnt harvest has fewer losses. When harvesting sugarcane, you can clearly hear the sound of sugarcane pieces hitting the wall of the primary extractor hood. Accordingly, it was decided to use the audio system to determine the relationship between these sounds and the losses of the primary extractor. To record sounds in the basic extractor, two models of full-directional and one-way capacitive microphone (cardioid) and cool record edit deluxe and AudaCity software were used. To detect the wavelength of the sounds caused by the collision of different parts of sugarcane with the hood cap and extractor blades by throwing a large volume of straw along with ۲۵ cm pieces of sugarcane billets, a sound record was set. A camera was also installed there to record video of what was happening under the extractor compartment. The results showed that the one-way capacitive microphone installed in the upper part of the primary extractor housing received clearer sounds. Analyظing the recorded sounds and comparing them with the images obtained from the camera under the primary extractor revealed that the audio loss detection system detects the losses in the primary extractor with an accuracy of about ۷۵ to ۸۰%. The loss rate at ۱۲۰۰ rpm was about ۱.۵ times higher than at ۱۱۰۰ rpm.Among the factors that affect the amount of crop losses are the methods and tools used to perform harvesting operations. Machine harvesting of sugarcane is done in two ways: green and burnt, and usually burnt harvest has fewer losses. When harvesting sugarcane, you can clearly hear the sound of sugarcane pieces hitting the wall of the primary extractor hood. Accordingly, it was decided to use the audio system to determine the relationship between these sounds and the losses of the primary extractor. To record sounds in the basic extractor, two models of full-directional and one-way capacitive microphone (cardioid) and cool record edit deluxe and AudaCity software were used. To detect the wavelength of the sounds caused by the collision of different parts of sugarcane with the hood cap and extractor blades by throwing a large volume of straw along with ۲۵ cm pieces of sugarcane billets, a sound record was set. A camera was also installed there to record video of what was happening under the extractor compartment. The results showed that the one-way capacitive microphone installed in the upper part of the primary extractor housing received clearer sounds. Analyظing the recorded sounds and comparing them with the images obtained from the camera under the primary extractor revealed that the audio loss detection system detects the losses in the primary extractor with an accuracy of about ۷۵ to ۸۰%. The loss rate at ۱۲۰۰ rpm was about ۱.۵ times higher than at ۱۱۰۰ rpm.

Keywords:

Acoustic sensor , Closed circuit camera , Harvest losses , Sugarcane primary extractor