Abstract
With the continuous advancement of smart agriculture, the introduction of robots for intelligent harvesting in modern agriculture is one of the crucial methods for the picking of fruits, vegetables, and melons. In this paper, three different illuminations, including front lighting, normal lighting, and back lighting, are first applied to citrus based on the computer vision technology. Secondly, the image data of the fruits, fruit stems, and leaves of the citrus are collected. The color component distributions of citrus based on different color models are analyzed according to the corresponding characteristic values, and an exploratory data analysis process for the image data of citrus is established. In addition, 300 citrus images are selected, and the citrus fruits are segmented from the background through the simulation experiment. The results of the study indicate that the recognition rate for the maturity of citrus has exceeded 98%, which has proved the effectiveness of the method proposed in this paper.
Highlights
With the continuous advancement of smart agriculture, the introduction of robots for intelligent harvesting in modern agriculture is one of the crucial methods for the picking of fruits, vegetables, and melons [1]. It can save a lot of costs in human resources; on the other hand, it can implement intelligent management effectively. e focus of intelligent harvesting is how to identify mature fruits from the wild fruits and vegetables accurately and effectively [2]
Our country ranks first in the yields and planting areas of citrus, the harvesting of citrus is still completed by hand in our country, which often takes up a lot of human resources and results in a certain constrain on the speed of harvesting and the costs
Color is a relatively obvious feature, which can be used to determine whether fruits and vegetables are mature. us, scholars in the industry have tried to carry out research on the scientific classification and determination of the colors of fruits and vegetables, such as the application of exploratory data analysis methods to conduct diversified and multiperspective statistical analysis
Summary
With the continuous advancement of smart agriculture, the introduction of robots for intelligent harvesting in modern agriculture is one of the crucial methods for the picking of fruits, vegetables, and melons. Three different illuminations, including front lighting, normal lighting, and back lighting, are first applied to citrus based on the computer vision technology. E color component distributions of citrus based on different color models are analyzed according to the corresponding characteristic values, and an exploratory data analysis process for the image data of citrus is established. 300 citrus images are selected, and the citrus fruits are segmented from the background through the simulation experiment. E results of the study indicate that the recognition rate for the maturity of citrus has exceeded 98%, which has proved the effectiveness of the method proposed in this paper 300 citrus images are selected, and the citrus fruits are segmented from the background through the simulation experiment. e results of the study indicate that the recognition rate for the maturity of citrus has exceeded 98%, which has proved the effectiveness of the method proposed in this paper
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