22:T551, Yield forecasting is important for farm management. In this study, red, green, and blue (RGB), near-infrared (NIR), and depth sensors were implemented in an outdoor machine vision system to determine the number of immature citrus in tree canopies in a citrus grove. The main objective was to compare the performances of three image data types for citrus yield forecasting. The performance comparison was conducted with two machine vision algorithm steps: 1) circular object detection for potential fruit areas and 2) classification of citrus fruit from the background. For circular object detection, circular Hough transform was used in the RGB and NIR images. For the depth images, CHOI‘s Circle Estimation (âCHOICE‘) algorithm was developed using depth divergence and vorticity to find circular objects in the depth images. The classification process was conducted using AlexNet, a deep learning algorithm for all three image types. The implementation of a convolutional neural network allowed the machine vision algorithms to remain bias-free process during feature generation and selection. NIR images performed best with 96% true positive rate for both the circular object detection and classification. A machine vision system using this image type will produce a more objective yield prediction with a higher accuracy than other types.23:T47c, Abstract. To separate kiwifruits and realize nondestructive picking and unloading of clustered kiwifruits in scaffolding cultivation, an end-effector with a grabbing-picking-sliding harvesting method and automatic picking and unloading connecting rod linkage following its trajectory model is presented based on the research on manual picking methods, the fruits growth and their physical properties. Kiwifruit position are detected by optic fiber sensors, clamping force detected by pressure sensors, shape and size of bionic picking fingers set thereby and an integration harvesting prototype following trajectory model from grabbing, picking to unloading designed and evaluated in the lab and orchard. The evaluation test showed that the end-effector of the prototype with optic fiber sensors on the fingers to detect the best position for grabbing, the pressure sensors to guarantee a nondestructive picking within damage threshold and adjusting spring for fruits size and shape tolerance, can integratedly grab and separate adjacent or clustered kiwifruits, then pick and unload individual fruit without destruction.24:T53e, Abstract. Unmanned Aerial Vehicles (UAVs) are gaining popularity with agriculture producers to perform daily activities such as monitoring livestock and plant health. With the expense of a UAV, producers require more than just pictures and video to be cost effective. This project sought to retrofit a device on a UAV to capture temperature data emitted from ear tags on cattle. The questions to be answered were: (1) can the UAV handle the weight of the recorder without negatively affecting flight time, (2) what is the flying height that could be used for data recording, and (3) does the UAV negatively affect cattle behavior when flying over? UAV performance while taking livestock data was recorded daily for two months. Results indicated the UAV could carry the data receiver and its power source with a negligible reduction in flight time. Battery life was reduced from an average of 19 minutes to 17.5 minutes. Also, data was accurately recorded at flying heights of 50-400 feet, with the optimal altitude determined to be 150 feet. At this altitude, a producer could potentially monitor cattle on 38 acres per hour. At all flying heights, the animals appeared unaffected by the UAV. The ability to remotely monitor livestock health adds to the advantages a producer can reap from utilizing UAV technology.25:T5d0,

#	Concept	H-Index	Publications	Citations
1	Engineering	10	10	210
2	Public Health	3	3	45
3	Social Sciences	2	2	27
4	Education	1	1	12
5	Civil Engineering	1	1	11

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