부지화 잎의 화학성분에 기반한 질소결핍 여부 구분 머신러닝 모델 개발
Development of Machine Learning Models Classifying Nitrogen Deficiency Based on Leaf Chemical Properties in Shiranuhi (Citrus unshiu × C. sinensis)
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본 연구에서는 부지화 잎의 무기양분 농도 측정 결과를 바탕으로 질소를 제외한 다른 무기양분의 함량을 통해서 잎의 질소결핍 여부를 구분하는 머신러닝 모델을 개발하였다. 그러기 위해서 부지화의 질소결핍구와 대조구의 잎 샘플을 분석한 36개의 데이터를 부트스트랩핑 방법을 통해서 학습용 데이터셋1,000 여 개로 증량시켰다. 이를 이용해 학습한 각 모델을 테스트한 결과, gradient boosting 모델이 가장 우수한 분류성능을 보여주었다. 본 모델을 이용해 질소함량을 직접적으로 분석할 수 없는 경우, 잎의 무기성분 함량에 기반하여 질소결핍 가능성 여부를 판단해 질소가 부족한 부지화 나무를 분별하고, 정확한질소함량을 측정하게 유도하여 그에 기초한 적정 질소비료 시비를 가능케 하고자 하였다.
Abstract
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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