The agricultural industry is constantly evolving with automation as one of the current main focuses. This thesis involves the automation of a seedbed tine harrow, specifically the control of the tillage depth. The tillage depth is instrumental to farming as it determines the quality of the tilth, how well clods are broken up, and how well the soil aggregates are sorted. Poor control of the tillage depth could result in a bad harvest for the farmer. To control the tillage depth, several pulse radar sensors are installed on the harrow. The sensors measure the distance from the tines of the harrow to the ground. This distance is used in a control-loop that controls the hydraulic actuators that lifts and pushes down the frame of the harrow. Because of the rough working conditions of the tine harrow, the pulse radar sensors are in danger of being damaged or disturbed. A sensor not working as intended will lead to poor control of the tillage depth or even an unstable control system. The purpose of this thesis is to develop diagnosis systems to detect and generate an alarm if the output of a sensor is faulty. Four different systems are developed, three machine learning approaches and one model based approach. To be able to test and train models without having to go out on a field with a real harrow, a test rig is available. The test rig emulates a harrow driving on a field and the tests are designed to imitate plausible sensor errors. The models trained on and tuned to the test rig data are validated with data gathered from a real tine harrow.  The validation data from the harrow reveal that the main difference between the field data and test rig data are the vibrations and the sensor heights. The test rig produces negligible amounts of vibrations whereas the vibrations on a real harrow are immense. These differences affect the performances of the models and some tuning have to be done to the models to accommodate for the vibrations. The performance of the model based approach is good and no larger adjustments have to be made to it. The machine learning models created from the test rig data do not work in the field and new models are trained using field data. The new models are accurate and show great potential; albeit, it would be necessary to collect a lot more data for further training. Specifically, training the machine learning models on varying heights. In conclusion, the test rig data is similar to the field data but the vibrations in the system is missing and the heights differ. The missing vibrations results in that the models do not work as intended on field data. The conventional diagnostics approach works, but the generated alarms are binary meaning that the alarm only reveal if the signal is good or bad and does not provide any nuance. The machine learning models does provide nuance, meaning that the model can detect errors, what is causing the error, and warn if an error is about to occur. However, the machine learning models need a lot of data to train on to make this happen.

Machine learning can be utilized in many different ways in the field of automatic manufacturing and logistics. In this thesis supervised machine learning have been utilized to train a classifiers for detection and recognition of objects in images. The techniques AdaBoost and Random forest have been examined, both are based on decision trees. The thesis has considered two applications: barcode detection and optical character recognition (OCR). Supervised machine learning methods are highly appropriate in both applications since both barcodes and printed characters generally are rather distinguishable. The first part of this thesis examines the use of machine learning for barcode detection in images, both traditional 1D-barcodes and the more recent Maxi-codes, which is a type of two-dimensional barcode. In this part the focus has been to train classifiers with the technique AdaBoost. The Maxi-code detection is mainly done with Local binary pattern features. For detection of 1D-codes, features are calculated from the structure tensor. The classifiers have been evaluated with around 200 real test images, containing barcodes, and shows promising results. The second part of the thesis involves optical character recognition. The focus in this part has been to train a Random forest classifier by using the technique point pair features. The performance has also been compared with the more proven and widely used Haar-features. Although, the result shows that Haar-features are superior in terms of accuracy. Nevertheless the conclusion is that point pairs can be utilized as features for Random forest in OCR.

This diploma thesis is focused on object detections through general-purpose computing on graphics processor units. There is an explanation of graphics adapters work and basics of their architecture in this thesis. Based on the adapters, there is the effective work in libraries for general-purpose computing on graphics processor units demonstrated in this thesis. Further, the thesis shows the available algorithms for object detection and which ones from them are possible to be effectively parallelized. In conclusion of this thesis, there is a comparison of the object detections speeds to common implementations on classical processors.