Imaging Technology for High-Throughput Plant Phenotyping

Zhou, Jing; Vong, Chin Nee; Zhou, Jianfeng

doi:10.1007/978-3-031-03834-1_4

Cited by 2 publications

(1 citation statement)

References 131 publications

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“…In plant science and agriculture, fruit detection and plant segmentation are two high-priority tasks ( Arya et al., 2022 ; Zhou et al., 2022 ). This is because these tasks can estimate growth stages and yields of plants, including crops, and improve plant environmental robustness (e.g., disease resistance, fruit quality, and fruit yields) by collaborating with genomic technologies (e.g., genome-wide association study and expression quantitative trait locus analysis) ( Varshney et al., 2021 ; Xiao et al., 2022 ).…”

Section: Discussionmentioning

confidence: 99%

JustDeepIt: Software tool with graphical and character user interfaces for deep learning-based object detection and segmentation in image analysis

Sun

Cao²,

Yamanaka³

2022

Front. Plant Sci.

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Image processing and analysis based on deep learning are becoming mainstream and increasingly accessible for solving various scientific problems in diverse fields. However, it requires advanced computer programming skills and a basic familiarity with character user interfaces (CUIs). Consequently, programming beginners face a considerable technical hurdle. Because potential users of image analysis are experimentalists, who often use graphical user interfaces (GUIs) in their daily work, there is a need to develop GUI-based easy-to-use deep learning software to support their work. Here, we introduce JustDeepIt, a software written in Python, to simplify object detection and instance segmentation using deep learning. JustDeepIt provides both a GUI and a CUI. It contains various functional modules for model building and inference, and it is built upon the popular PyTorch, MMDetection, and Detectron2 libraries. The GUI is implemented using the Python library FastAPI, simplifying model building for various deep learning approaches for beginners. As practical examples of JustDeepIt, we prepared four case studies that cover critical issues in plant science: (1) wheat head detection with Faster R-CNN, YOLOv3, SSD, and RetinaNet; (2) sugar beet and weed segmentation with Mask R-CNN; (3) plant segmentation with U2-Net; and (4) leaf segmentation with U2-Net. The results support the wide applicability of JustDeepIt in plant science applications. In addition, we believe that JustDeepIt has the potential to be applied to deep learning-based image analysis in various fields beyond plant science.

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