Abstract Detail

The Potential of Machine Learning for Plant Biology


Human in the loop machine learning applications in high throughput plant stress phenotyping.

Machine Learning (ML) approaches have emerged as a versatile tool to assimilate large amounts of heterogeneous data and provide reliable predictions of complex and uncertain phenomena. ML tools can work with big data obtained from complex and integrated phenotyping platforms like unmanned aerial vehicles, rovers and smartphones to solve the identification, classification, quantification, and prediction paradigm of plant stress phenotyping to generate insights that were previously not possible. The next step is to implement computer aided stress phenotyping for actionable outcomes in digital agriculture. We demonstrate a deep convolution neural network framework that can identify, classify and quantify diverse set of eight foliar stresses including fungal, bacterial, nutrient deficiency and herbicide injury in soybean [Glycine max (L.) Merr.]. To satisfy the practitioner's confidence in artificial intelligence generated ratings, we present explainability of the model using the top-K high-resolution feature maps that isolate the visual symptoms. This unsupervised identification of visual symptoms provides a quantitative measure of stress severity, allowing for identification (type of foliar stress), classification (low, medium, or high stress), and quantification (stress severity) providing automation. We successfully report transfer learning as the model is able to be deployed in non-soybean species. Overall these approaches are amenable to high throughput and fidelity aerial phenotyping platform as well as mobile platforms facilitating research and crop production solutions and is a current focus of our work.

1 - Iowa State University, 2104 Agronomy Hall , 716 Farm House Lane , Ames, IA , 50011, USA

deep learning.

Presentation Type: Symposium Presentation
Abstract ID:973
Candidate for Awards:Margaret Menzel Award,Edgar T. Wherry award

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