Automated inter-plant spacing sensing of corn plant seedlings and quantification of laying hen behaviors using 3d computer vision

Within-row plant spacing plays an important role in uniform distribution of water and nutrients among plants, hence affects the final crop yield. While manual in-field manual measurements of within-row plant spacing is time and labor intensive, little work has been carried out to automate the process. An automated system is developed using a state-of-the-art 3D vision sensor that accurately measures within-row corn plant spacing. The system is capable of processing about 1200 images captured from a 61 m crop row containing approximately 280 corn plants in about three and half minutes. Stocking density of laying hens in egg production remains an area of investigation from the standpoints of ensuring hen's ability to perform natural behaviors and production economic efficiency. It is therefore of socio-economic importance to quantify the effect of stocking density on laying hens behaviors and thus wellbeing. In this study, a novel method for automatic quantification of stocking density effect on some natural laying hen behaviors such as locomotion, perching, feeding, drinking and nesting is explored. Image processing techniques are employed on top view images captured with a state-of-the-art time-of-flight (TOF) of light based 3D vision camera for identification as well as tracking of individual hens housed in a 1.2 m × 1.2 m pen. A Radio Frequency Identification (RFID) sensor grid consisting of 20 antennas installed underneath the pen floor is used as a recovery system in situations where the imaging system fails to maintain identities of the hens.