A machine vision based approach for classification of tobacco leaves for automatic harvesting in a complex agricultural environment is proposed in this paper. The CIELAB color space model is used to segment the leaf from the background. The segmented leaves are classified into three classes viz., ripe, unripe, and over-ripe. Models based on various textural features such as GLTP (Gray Level Loc...

A need has been identified for more advanced technology to assist harvesting in the macadamia nut industry. An accurate automated method for counting nuts as they are harvested is required to replace manual labour in varietal strain investigation. A machine vision approach of counting the nuts in real time as they are collected has been chosen. This paper presents a machine vision system that i...

Time study is an important research tool used in comparing productivity of forest harvesting systems across varying conditions. Unfortunately, it has been an expensive tool to apply, involving travel and fieldwork by a crew of technicians plus time in the office to reduce raw data to usable information. In the interests of safety and cost reduction, it would be preferable to have a means of per...

A machine learning model is developed to establish wake patterns behind oscillating foils whose kinematics are within the energy harvesting regime. The role of structure particularly important for array deployments foils, since unsteady highly influences performance downstream foils. This work explores 46 foil kinematics, with goal parameterizing based on input kinematic variables and grouping ...