Abstract: A method for positioning tea-bud picking points based on fused thermal images and RGB images is provided and includes: firstly, image pairs of several tea buds are acquired by using an image acquisition device, and are each labeled to obtain a tea-bud object detection database and a tea-bud keypoint detection database; secondly, in order to obtain a trained object detection model and a trained keypoint detection model, the tea-bud object detection database and the tea-bud keypoint detection database are inputted into an object detection model and a keypoint detection model for training, respectively; finally, the trained object detection model and keypoint detection model are used to sequentially process the tea-bud image pairs to obtain tea-bud keypoint positions, and then tea-bud picking point positions are obtained in combination with the tea-bud growth characteristics. The positioning accuracy and efficiency of the tea-bud picking points can be improved.

Abstract: The present disclosure relates to a gas explosion type intelligent variable-rate fertilization scarifier for a tea garden. The gas explosion type intelligent variable-rate fertilization scarifier includes a walking mechanism, a fertilization-scarifying mechanism, a gas explosion mechanism, a solid fertilizer supply mechanism, and a water fertilizer supply mechanism. The fertilization-scarifying mechanism includes a transmission assembly, a detection assembly, and a fertilization-scarifying assembly. A first fertilizer conveying pipeline is fixedly connected between the solid fertilizer supply mechanism and the fertilization-scarifying assembly, and a second fertilizer conveying pipeline is fixedly connected between the water fertilizer supply mechanism and the fertilization-scarifying assembly. Each of the first fertilizer conveying pipeline and the second fertilizer conveying pipeline is provided with a solenoid valve.

Abstract: A method for positioning tea-bud picking points based on fused thermal images and RGB images is provided and includes: firstly, image pairs of several tea buds are acquired by using an image acquisition device, and are each labeled to obtain a tea-bud object detection database and a tea-bud keypoint detection database; secondly, in order to obtain a trained object detection model and a trained keypoint detection model, the tea-bud object detection database and the tea-bud keypoint detection database are inputted into an object detection model and a keypoint detection model for training, respectively; finally, the trained object detection model and keypoint detection model are used to sequentially process the tea-bud image pairs to obtain tea-bud keypoint positions, and then tea-bud picking point positions are obtained in combination with the tea-bud growth characteristics. The positioning accuracy and efficiency of the tea-bud picking points can be improved.