PhenologyNet: A fine-grained approach for crop-phenology classification fusing convolutional neural network and phenotypic similarity

Abstract

With the application of facility-based agriculture, robotics, and other technologies in agricultural production, the accurate recognition of individual crop phenological stages has become crucial for precision agriculture. However, the slightly noticeable visual disparities between neighboring phenological stages of crops pose a challenge for fine-grained phenological classification based on image features. Phenotypic features of plants, providing a wealth of information about crop-growth patterns, exhibit remarkable similarity within the same phenological stage. This paper presents a framework named PhenologyNet, which leverages the significant role of phenotypic features for fine-grained crop-phenology classification. A novel phenology-classification model was developed by leveraging local crop phenotypic features, assigning dynamic weights based on a phenology-specific matrix. The model calculates overall similarity from nine patch pair similarities, capturing the nuanced importance of local features during different phenological stages. This was combined with the phenology classification based on convolutional neural networks (CNNs) using a late fusion approach, achieving precise fine-grained recognition of crop phenological stages. PhenologyNet was trained and tested on a lettuce-phenology dataset classified according to 28 “Biologische Bundesanstalt, Bundessortenamt und Chemische Industrie” (BBCH) phenological stages, achieving an accuracy of 92.79%. Comparative experiments with other methodologies excluding phenotypic similarity showed that the incorporation of phenotypic similarity notably enhanced the efficiency of the fine-grained crop-phenology classification. PhenologyNet’s performance further improved when the number of BBCH phenological stages decreased, suggesting the framework’s commendable capabilities in tackling the issue of crop-phenology classification in a way that meets precision agriculture’s requirements.

Plant phenotyping relevance

植物画像から生育段階という植物状態を推定するCNN融合モデルを開発・評価しており、表現型抽出と分類手法が研究の中心であるため。

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