Rapid detection of soybean nutrient deficiencies with YOLOv8s for precision agriculture advancement.

Abstract

Early detection of nutrient deficiencies is crucial for optimizing crop yields and ensuring sustainable agricultural practices. This study presents a novel application of the YOLOv8s object detection model for identifying nitrogen, phosphorus, and potassium deficiencies in soybean plants. Employing a unique dataset from a long-term nutrient-deficient field maintained for over 40 years, we trained and evaluated the model on 6,020 red, green, and blue images of soybean leaves exhibiting nutrient stress conditions. The YOLOv8s model achieved exceptional performance, with a mean average precision (mAP@0.5) of 99.18% during training and 98.51% for validation. Precision rates for individual nutrient deficiencies ranged from 90.03 to 96.54%, with highly accurate potassium deficiency detection. The model demonstrated robust generalization across diverse field conditions, processing images in 3.46 ms each, making it suitable for real-time applications. This research significantly advances the field of precision agriculture by providing a fast, accurate, and scalable method for detecting early nutrient deficiency in soybean crops, potentially revolutionizing fertilizer management practices and contributing to more sustainable farming systems.

Plant phenotyping relevance

大豆葉の栄養欠乏状態を画像から検出するYOLOv8s手法を開発・評価しており、表現型取得とモデル性能検証が研究の中心である。

Code and data availability

The paper's soybean nutrient-deficiency RGB image dataset (6,020 images from a 40-year nutrient-deficient field) and YOLOv8s analysis are paper-specific, but the authors state the data are only available from the corresponding author upon request; no public repository, code deposit, or trained model checkpoint URL is提供

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