With a growing population and a changing climate, increasing crop yields in a diversity of environmental conditions is becoming increasingly important. Studying genome-by-environment (GxE) effects is a critical path for such improvements, and high-throughput plant phenotyping is necessary for carrying out such experiments at scale. Image-based phenotyping techniques offer a scalable, non-destructive way of quantifying plants' responses to their environment - however, these techniques can be cumbersome and subjective. Each image dataset is unique, and requires either a hand-crafted image processing pipeline or a large annotated training set, which can be expensive and time-consuming. Additionally, researchers must select what feature is to be used to quantify changes due to the treatment, such as biomass, colour, the number of organs, or some other visual indication of the individual's response to its environment. This dissertation explores image-based plant phenotyping, beginning with a discussion of image processing tools.

Availability of an explainable deep learning model that can be applied to practical real world scenarios and in turn, can consistently, rapidly and accurately identify specific and minute traits in applicable fields of biological sciences, is scarce. Here we consider one such real world example viz., accurate identification, classification and quantification of biotic and abiotic stresses in crop research and production. Up until now, this has been predominantly done manually by visual inspection and require specialized training. However, such techniques are hindered by subjectivity resulting from inter- and intra-rater cognitive variability. Here, we demonstrate the ability of a machine learning framework to identify and classify a diverse set of foliar stresses in the soybean plant with remarkable accuracy. We also present an explanation mechanism using gradient-weighted class activation mapping that isolates the visual symptoms used by the model to make predictions. This unsupervised identification of unique visual symptoms for each stress provides a quantitative measure of stress severity, allowing for identification, classification and quantification in one framework. The learnt model appears to be agnostic to species and make good predictions for other (non-soybean) species, demonstrating an ability of transfer learning.