An Instance Segmentation Model for Strawberry Diseases Based on Mask R-CNN.

Abstract

Plant diseases must be identified at the earliest stage for pursuing appropriate treatment procedures and reducing economic and quality losses. There is an indispensable need for low-cost and highly accurate approaches for diagnosing plant diseases. Deep neural networks have achieved state-of-the-art performance in numerous aspects of human life including the agriculture sector. The current state of the literature indicates that there are a limited number of datasets available for autonomous strawberry disease and pest detection that allow fine-grained instance segmentation. To this end, we introduce a novel dataset comprised of 2500 images of seven kinds of strawberry diseases, which allows developing deep learning-based autonomous detection systems to segment strawberry diseases under complex background conditions. As a baseline for future works, we propose a model based on the Mask R-CNN architecture that effectively performs instance segmentation for these seven diseases. We use a ResNet backbone along with following a systematic approach to data augmentation that allows for segmentation of the target diseases under complex environmental conditions, achieving a final mean average precision of 82.43%.

Plant phenotyping relevance

イチゴ病害の症状を画像からインスタンスセグメンテーションし、データセットとMask R-CNN手法を開発・評価しているため、植物状態の取得手法が中心である。

Code and data availability

The paper's strawberry disease dataset is openly available on Kaggle, but that URL is not among the allowed URLs, so it cannot be listed. The only allowed repository URL (matterport/Mask_RCNN) is a generic third-party library, not a paper-specific asset. No qualifying paper-specific public asset can be returned.

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