Proceedings
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| Filter results4 paper(s) found. |
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1. Development Of Online Soil Profile Sensor For Variable Depth TillageIntroduction First introduced in the early 1990s, precision agriculture technologies, or site-specific management, were considered by many to be perhaps the most significant development in production agriculture focused on improving farm profitability. The initial focus was on fertility, and treating the variability that we all knew existed from our experiences with soil sampling. However, to a large extent this application still... A.B. Tekin, H. Yalcin |
2. Exploiting The Variability In Pasture Production On New Zealand Hill Country.New Zealand has about four million hectares in medium to steep hill country pasture to which granular solid fertiliser is applied by airplane. On most New Zealand hill country properties where cultivation is not possible the only means of influencing pasture production yield is through the addition of fertilizers and paddock subdivision to control grazing and pasture growth rates. Pasture response to fertilizer varies in production zones within the farm which can be modelled... M.Q. Grafton, P.J. Mcveagh, R.R. Pullanagari, I.J. Yule |
3. Robotic Arm Tomato Harvesting System and Next Best View Algorithm DevelopmentReplacing human labor with robots is a trend for future agriculture due to its efficiency and consistency. However, in automatic fruit harvesting tasks, leaf occlusion and the dynamic orientation of fruit make it difficult for robots to directly observe the picking point. To address this problem, this research focuses on tomato harvesting, and proposes a next-best-view (NBV) algorithm based on two main structures: “tomato pose prediction” and a “target-hit-gain function”.... P. Yen |
4. Null Dataset-Based Detection Enhances Robotic Vision in Greenhouse Cherry Tomato HarvestingCluttered cherry tomato greenhouse environments with visually similar distractors often trigger False Positives (FPs) in robotic vision, misguiding the robot’s motion and reducing harvesting success. We introduce a null-dataset strategy that integrates unannotated distractor images into YOLOv8l training, with their proportion tuned through loop refinement to suppress FPs while preserving precision. Optimal null proportions were identified as 12.3% for tomato detection and 8.3% for pedicel... P. Yen |