{'fraction_non_alphanumeric': 0.02086765513454146, 'fraction_numerical': 0.008786381109280615, 'mean_word_length': 4.993421052631579, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 1, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Trends in the Development of Machinery and Associated Technology ” TMT 2012 , Dubai , UAE , 10-12 September 2012 ANALYSIS OF TRACTOR TRACTION FORCE WITH ARTIFICIAL NEUROL NETWORKS IN CULTIVATION

Fast advancements in agricultural machinery as a result of scientific and technological development have led to socio-economic and cultural improvements in rural areas. This is particularly the case in countries with limited agricultural areas as these countries have to adapt to advance agricultural technologies in order to achieve optimum yields. That’s why it is necessary to improve the efficiencies and effectiveness of the agricultural mechanizations. Tractors are among the agricultural machines that play an important role in the sector. This is so because tractors are not only the hauling equipment in the agricultural fields but can also be involved in a number of tasks in the sector. In this study, a computer controlled testing system has been established and an artificial neural networks (ANN) was used to analyze a tractor’s traction force in soil processing. With this aim, three different ANN models (Model-1, Model-2 and Model-3) were established. Input and output layers for each model were selected and, for given operating speeds and work depths, the data obtained from empirical analysis and those from the ANN measurements were calculated. In order to find the most appropriate model among the three selected ANN models, a crossvalidation test was applied to the ANN results by taking the test data into consideration. From the results, it was found that Model-1 is the best selection. By applying the cross evaluation to the ANN test results, this study has shown that, in agricultural mechanization, it is possible to predict tractor’s speed and soil processing depth based on the soil properties.