In-Field Nondestructive Detection of Nitrogen Status on 'Yotsuboshi' Strawberry Using Deep Learning Algorithm.

Abstract

Nitrogen (N) management is critical for optimizing growth and fruit quality in open-field strawberry cultivation, demanding advanced technological solutions for reliable nutrient assessment. However, visual symptom diagnosis, though widely utilized for nutrient monitoring, is inherently subjective and prone to observer bias, resulting in inconsistent and often unreliable assessments. While available accurate tissue analysis is destructive and costly. Nondestructive, in-field imaging techniques such as the normalized difference vegetation index (NDVI) exist but require expensive multispectral imaging systems. To address these limitations, this study developed a streamlined methodology for in-field N status detection using deep learning on standard RGB images. The experiment utilized 'Yotsuboshi' strawberries in a randomized complete block design with sufficient nitrogen (T1) and deficient nitrogen (T2) treatments. To mitigate ambient light variability, a key challenge in open-field phenotyping, a low-cost phenotyping cylinder was developed for standardized smartphone image acquisition. Rigorous four-stage annotation criteria were also introduced to classify the nitrogen status in strawberry leaves as NormalN, LowN, or AdvancedLowN, ensuring a high-quality novel dataset. A YOLO11 model trained on this dataset achieved precision, recall, and mAP50 values exceeding 99%. Subsequent testing using the phenotyping cylinder yielded a mAP50 of 87%. In-field validation without a phenotyping cylinder also demonstrated robust performance under diffuse cloudy conditions (82.7% mAP50), outperforming direct sunlight (79% mAP50). Moreover, the model's classifications of 'NormalN' and 'LowN' statuses strongly corresponded with NDVI measurements, validating the accuracy of the RGB-based approach. This research demonstrates the significant potential of combining deep learning and phenotyping cylinder to create a rapid, low-cost, nondestructive and reliable tool for in-field nitrogen detection, with possible application across different crops and environmental conditions.

Plant phenotyping relevance

植物の窒素状態をRGB画像から推定する深層学習法、標準化撮像用デバイス、アノテーション基準、データセットを開発し、NDVIおよび圃場条件で検証しており、表現型取得・推定手法が中心である。

Code and data availability

The paper's strawberry nitrogen-status image dataset (936 RGB images plus annotations, SPAD/N ground truth) and YOLO11 model are paper-specific phenotyping assets, but the Data Availability Statement says the dataset is available only upon request from the corresponding author, with restrictions. No public URL, deposit

No evidence-backed public reproduction asset is currently recorded.