Abstract
The positioning of the apple growth cycle plays a very important role in predicting the development of apples and guiding fruit farmers in agricultural operations. The traditional method of manually positioning the apple growth cycle has problems such as low efficiency and poor accuracy. Pattern recognition provides support for continuous and rapid positioning during the apple growth process. Under the natural conditions of the orchard, due to the large differences in the individual colors of the apples during the growth process and the influence of factors such as light changes, the photographed apple images are more complex, which brings certain difficulties to the segmentation and recognition of the apples. In this paper, pattern recognition is used to automatically identify and extract the growth stages of apples, a hue intensity (HI) color segmentation algorithm based on a Gaussian distribution model based on prior knowledge is studied, and then an active shape model (ASM) is used to identify each period of apple growth based on pattern recognition. After a series of experimental verifications, the ASM-based automatic identification method proposed in this paper is feasible and can identify the various growth periods of apples, thereby serving the mechanized production of apples.
Highlights
Apples have always been recognized as a fruit that can prevent a variety of diseases
In order to achieve accurate segmentation of apple images in complex environments, this paper studies an hue intensity (HI) color segmentation algorithm based on Gaussian distribution model based on prior knowledge of apples in different weather conditions and different growth and development stages. e algorithm only needs a small number of training samples to complete the segmentation of the apple image
This paper studies an HI color segmentation algorithm based on Gaussian distribution, which is suitable for apple image segmentation at each period of the growth process in the complex environment of the orchard
Summary
Apples have always been recognized as a fruit that can prevent a variety of diseases. Nondestructive monitoring of crop growth is one of the important links in smart agriculture It requires real-time and accurate acquisition of plant growth information to guide the fine management of crops. Is is followed by the physiological fruit drop period and the fruit expansion period of the apple, and the maintenance work should be done well It should be fertilized and pruned in time during its maturity period to allow it to grow and produce better results. The novelty of this paper is mainly to use the HI color based on Gaussian distribution to perform apple image segmentation, and use ASM to identify the whole apple growth cycle from seedling stage to mature fruit.
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Free) 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a call
