Stress phenotyping analysis leveraging autofluorescence image sequences with machine learning.

Background: Autofluorescence-based imaging has the potential to non-destructively characterize the biochemical and physiological properties of plants regulated by genotypes using optical properties of the tissue. A comparative study of stress tolerant and stress susceptible genotypes of with respect to newly introduced stress-based phenotypes using machine learning techniques will contribute to the significant advancement of autofluorescence-based plant phenotyping research.

Methods: Autofluorescence spectral images have been used to design a stress detection classifier with two classes, stressed and non-stressed, using machine learning algorithms.

Drought stress prediction and propagation using time series modeling on multimodal plant image sequences.

The paper introduces two novel algorithms for predicting and propagating drought stress in plants using image sequences captured by cameras in two modalities, i.e., visible light and hyperspectral.

Could energy equilibrium and greenhouse gas emissions in agroecosystems play a key role in crop replacement? A case study in orange and kiwi orchards.

The development of agriculture is linked to energy resources. Consequently, energy analysis in agroecosystems could be a useful tool for monitoring some measures in the agricultural sector to mitigate greenhouse gas emissions. The objectives of this study were to (a) evaluate differences of energy indices in orange and kiwi orchards, and (b) point out whether inputs, outputs, efficiency, productivity, and carbon footprint can play a key role in crop replacement.