부지화 잎의 화학성분에 기반한 질소결핍 여부 구분 머신러닝 모델 개발

• 영문명: Development of Machine Learning Models Classifying Nitrogen Deficiency Based on Leaf Chemical Properties in Shiranuhi (Citrus unshiu × C. sinensis)
• 발행기관: 한국자원식물학회
• 저자명: 박원표(Won Pyo Park) 허성(Seong Heo)
• 간행물 정보: 『한국자원식물학회지』제35권 제2호, 192~200쪽, 전체 9쪽
• 주제분류: 농수해양 > 기타농수해양
• 파일형태: PDF
• 발행일자: 2022.04.30

영문 초록

Nitrogen is the most essential macronutrient for the growth of fruit trees and is important factor determining the fruit yield. In order to produce high-quality fruits, it is necessary to supply the appropriate nitrogen fertilizer at the right time. For this, it is a prerequisite to accurately diagnose the nitrogen status of fruit trees. The fastest and most accurate way to determine the nitrogen deficiency of fruit trees is to measure the nitrogen concentration in leaves. However, it is not easy for citrus growers to measure nitrogen concentration through leaf analysis. In this study, several machine learning models were developed to classify the nitrogen deficiency based on the concentration measurement of mineral nutrients in the leaves of tangor Shiranuhi (Citrus unshiu × C. sinensis). The data analyzed from the leaves were increased to about 1,000 training dataset through the bootstrapping method and used to train the models. As a result of testing each model, gradient boosting model showed the best classification performance with an accuracy of 0.971.

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키워드

Machine learning Mineral nutrient Nitrogen Tangor (Citrus unshiu × C. sinensis)