agricultural tractor driving cycle extraction using artificial intelligence

Driving cycle assessment is one of common methods to evaluate vehicle’s real-world condition also monitoring fuel consumption and emissions. due to significant importance of extraction of driving cycle in urban and passenger cars, large number of researches are dedicated toward study of light-duty driving cycles. basic challenges in extraction of driving cycle are data analysis for develop and define suitable behavior of device. clustering, classification and recognition of driving pattern are important steps on extraction of suitable driving cycle. generally, the accuracy of modeling and recognition of ai-based methods is indicated more than 90% and other outputs are in compliance with big data. thus, in this research we endeavored to evaluate the effect of using artificial intelligence on driving cycle of off-road vehicles. the major part of off-road vehicles are agricultural vehicles such as tractors which they are divided into three categories based on agriculture operations; light, heavy and extra heavy.in addition, the procedure of agricultural operation is effective on fuel consumption, loading and exhaust emissions. the result of this research illustrates the ability of algorithms based on artificial intelligence can be useful in recognizing and extraction of agricultural vehicle’s driving cycle. furthermore, this means that the ability to identify and classify the methods of machine vision and deep learning creates a suitable ability to extract the optimal driving cycle based on the size of the tractor, the weight of the accessories and the cultivated area. therefore, extracting the intelligent driving cycle for agricultural tractors based on the type of agricultural operation with the help of artificial intelligence methods can reduce fuel consumption, pollution and optimal farm management.