Crop Identification and Growth Stage Determination for Autonomous Navigation of Agricultural Robots

Abstract

This study introduces two methods for crop identification and growth stage determination, focused primarily on enabling mobile robot navigation. These methods include a two-phase approach involving separate models for crop and growth stage identification and a one-phase method employing a single model capable of handling all crops and growth stages. The methods were validated with maize and sugar beet field images, demonstrating the effectiveness of both approaches. The one-phase approach proved to be advantageous for scenarios with a limited variety of crops, allowing, with a single model, to recognize both the type and growth state of the crop and showed an overall Mean Average Precision (mAP) of about 67.50%. Moreover, the two-phase method recognized the crop type first, achieving an overall mAP of about 74.2%, with maize detection performing exceptionally well at 77.6%. However, when it came to identifying the specific maize growth state, the mAP was only able to reach 61.3% due to some difficulties arising when accurately categorizing maize growth stages with six and eight leaves. On the other hand, the two-phase approach has been proven to be more flexible and scalable, making it a better choice for systems accommodating a wide range of crops.

Plant phenotyping relevance

作物の生育段階という植物状態を画像から推定するモデルを開発・検証しており、単なる位置検出を超えた表現型推定が中心。ただし自律走行支援が主目的のため、適用範囲は限定的。

Code and data availability

The paper's maize and sugar beet field image dataset used for crop identification and growth stage detection is not publicly available; the Data Availability Statement says it is available only on request from the corresponding author. No public code, models, or dataset URLs are provided.

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