발간년도 : [2022]
| 논문정보 |
|
| 논문명(한글) |
[Vol.17, No.3] Blueberry Fruit Maturity Rate Measurement System Using Object Detection |
|
| 논문투고자 |
Dokil Pyoun, Jongwoo Baek, Jieun Lee, Hoekung Jung |
|
| 논문내용 |
Abnormal climate at home and abroad causes a problem fruit tree production is reduced due to damage caused by frost in spring, and the production of various agricultural products is rapidly reduced in summer due to the influence of the rainy season and typhoons. In order to improve this, new problem-solving methods must be found in the agricultural environment, and it is necessary to seek a new direction from the center of large-scale production in agriculture to diversity and quality growth. In this paper, we collect data on the environmental information and growth status of orchards using smart farms to increase the production of fruit trees in farms. We measure, analyze, and predict this data based on artificial intelligence to monitor the growth status and propose an algorithm to predict the right time to harvest fruit trees. o this end, the Raspberry Pi collects temperature, soil humidity, soil acidity, sunlight, and image data of fruit trees, which are information on the growth status of fruit trees on the farm, and stores them in the Hadoop distributed system. The collected fruit tree growth environment information is classified and judged as optimal information for growth, and is stored using the One-hot encoding technique, and the image data of the fruit tree is analyzed and classified using an object detection technique. In addition, the entire process of the growth measurement environment can be monitored through the web application, and the optimal harvest prediction time can be indicated. |
|
| 첨부논문 |
|
|
|
|
|
자료실 > 논문지 
