Abstract
Employing computer vision to extract useful information from images and videos is becoming a key technique for identifying phenotypic changes in plants. Here, we review the emerging aspects of computer vision for automated plant phenotyping. Recent advances in image analysis empowered by machine learning-based techniques, including convolutional neural network-based modeling, have expanded their application to assist high-throughput plant phenotyping. Combinatorial use of multiple sensors to acquire various spectra has allowed us to noninvasively obtain a series of datasets, including those related to the development and physiological responses of plants throughout their life. Automated phenotyping platforms accelerate the elucidation of gene functions associated with traits in model plants under controlled conditions. Remote sensing techniques with image collection platforms, such as unmanned vehicles and tractors, are also emerging for large-scale field phenotyping for crop breeding and precision agriculture. Computer vision-based phenotyping will play significant roles in both the nowcasting and forecasting of plant traits through modeling of genotype/phenotype relationships.
Highlights
Computer vision that extracts useful information from plant images and videos is rapidly becoming an essential technique in plant phenomics [1]
We provide an overview of recent advances in computer vision-based plant phenotyping, which can contribute to our understanding of genotype/phenotype relationships in plants
Multiple Machine learning (ML)-based algorithms, such as k-nearest neighbor (kNN), naive Bayes classifier, and support vector machine (SVM) algorithms, have been examined in segmentation processes for detecting aerial parts of plants, and the findings suggested that different algorithms would be preferable for segmenting images of the visible and near infrared spectra [81]
Summary
Computer vision that extracts useful information from plant images and videos is rapidly becoming an essential technique in plant phenomics [1]. We address recent challenges in computer vision-based plant image analysis and the typical image analysis process (e.g., segmentation, feature extraction, and classification), as well as its applications to large-scale phenotyping in genetic studies in plants, by highlighting ML-based approaches.
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