Machine Learning for High-Throughput Stress Phenotyping in Plants

Abstract

Advances in automated and high-throughput imaging technologies have resulted in a deluge of high-resolution images and sensor data of plants. However, extracting patterns and features from this large corpus of data requires the use of machine learning (ML) tools to enable data assimilation and feature identification for stress phenotyping. Four stages of the decision cycle in plant stress phenotyping and plant breeding activities where different ML approaches can be deployed are (i) identification, (ii) classification, (iii) quantification, and (iv) prediction (ICQP). We provide here a comprehensive overview and user-friendly taxonomy of ML tools to enable the plant community to correctly and easily apply the appropriate ML tools and best-practice guidelines for various biotic and abiotic stress traits.

A Big Data Based Edge Detection Method for Image Pattern Recognition - A Survey

“…The author uses the HOG and SVM classifier algorithms [12]. Arti Singh, Baskar Ganapathysubramanian, Asheesh Kumar Singh, Soumik sarkar, describes the various methods about plant phenotyping and how the leaf images are extracted by using various machine learning and data mining techniques [13].…”

Machine Learning for Plant Phenotyping Needs Image Processing

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We found the article by Singh et al [1] extremely interesting since it introduces and showcases the utility of machine learning for high throughput data-driven plant phenotyping.With this letter we want to emphasize the role that image analysis and processing have in the phenotyping pipeline beyond what [1] suggests, both in analyzing phenotyping data (e.g., to measure growth) but also when providing effective feature extraction to be used by machine learning. Key recent reviews have shown that it is image analysis itself (what the authors of [1] consider as part of pre-processing) that has brought a renaissance in phenotyping [2].

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“…We found the article by Singh et al [1] extremely interesting since it introduces and showcases the utility of machine learning for high throughput data-driven plant phenotyping.…”

“…Key recent reviews have shown that it is image analysis itself (what the authors of [1] consider as part of pre-processing) that has brought a renaissance in phenotyping [2]. At the same time, the lack of robust methods to analyze these images is now the new bottleneck [3][4][5].…”

“…There is a need for mechanisms to represent the image data in a way that machine learning algorithms can use, and this process is known as feature extraction (another component bundled under preprocessing in [1]). At present, features need to be designed and extracted carefully by expert supervision requiring specific domain knowledge (a process known as feature engineering), the translation of which to image analysis protocols and image filters (e.g., edge detectors) does require significant image processing expertise and skills.…”