Dataset Information

Proximal Phenotyping and Machine Learning Methods to Identify Septoria Tritici Blotch Disease Symptoms in Wheat.

ABSTRACT: Phenotyping with proximal sensors allow high-precision measurements of plant traits both in the controlled conditions and in the field. In this work, using machine learning, an integrated analysis was done from the data obtained from spectroradiometer, infrared thermometer, and chlorophyll fluorescence measurements to identify most predictive proxy measurements for studying Septoria tritici blotch (STB) disease of wheat. The random forest (RF) models for chlorosis and necrosis identified photosystem II quantum yield (QY) and vegetative indices (VIs) associated with the biochemical composition of leaves as the top predictive variables for identifying disease symptoms. The RF model for chlorosis was validated with a validation set (R2: 0.80) and in an independent test set (R2: 0.55). Based on the results, it can be concluded that the proxy measurements for photosystem II, chlorophyll content, carotenoid, and anthocyanin levels and leaf surface temperature can be successfully used to detect STB. Further validation of these results in the field will enable application of these predictive variables for detection of STB in the field.

SUBMITTER: Odilbekov F

PROVIDER: S-EPMC5974968 | biostudies-literature | 2018

REPOSITORIES: biostudies-literature

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Publications

Proximal Phenotyping and Machine Learning Methods to Identify Septoria Tritici Blotch Disease Symptoms in Wheat.

Odilbekov Firuz F Armoniené Rita R Henriksson Tina T Chawade Aakash A

Frontiers in plant science 20180523

Phenotyping with proximal sensors allow high-precision measurements of plant traits both in the controlled conditions and in the field. In this work, using machine learning, an integrated analysis was done from the data obtained from spectroradiometer, infrared thermometer, and chlorophyll fluorescence measurements to identify most predictive proxy measurements for studying Septoria tritici blotch (STB) disease of wheat. The random forest (RF) models for chlorosis and necrosis identified photosy ...[more]

PMID: 29875788

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Project description:BackgroundInvestigations on plant-pathogen interactions require quantitative, accurate, and rapid phenotyping of crop diseases. However, visual assessment of disease symptoms is preferred over available numerical tools due to transferability challenges. These assessments are laborious, time-consuming, require expertise, and are rater dependent. More recently, deep learning has produced interesting results for evaluating plant diseases. Nevertheless, it has yet to be used to quantify the severity of Septoria tritici blotch (STB) caused by Zymoseptoria tritici-a frequently occurring and damaging disease on wheat crops.ResultsWe developed an image analysis script in Python, called SeptoSympto. This script uses deep learning models based on the U-Net and YOLO architectures to quantify necrosis and pycnidia on detached, flattened and scanned leaves of wheat seedlings. Datasets of different sizes (containing 50, 100, 200, and 300 leaves) were annotated to train Convolutional Neural Networks models. Five different datasets were tested to develop a robust tool for the accurate analysis of STB symptoms and facilitate its transferability. The results show that (i) the amount of annotated data does not influence the performances of models, (ii) the outputs of SeptoSympto are highly correlated with those of the experts, with a similar magnitude to the correlations between experts, and (iii) the accuracy of SeptoSympto allows precise and rapid quantification of necrosis and pycnidia on both durum and bread wheat leaves inoculated with different strains of the pathogen, scanned with different scanners and grown under different conditions.ConclusionsSeptoSympto takes the same amount of time as a visual assessment to evaluate STB symptoms. However, unlike visual assessments, it allows for data to be stored and evaluated by experts and non-experts in a more accurate and unbiased manner. The methods used in SeptoSympto make it a transferable, highly accurate, computationally inexpensive, easy-to-use, and adaptable tool. This study demonstrates the potential of using deep learning to assess complex plant disease symptoms such as STB.

Dataset Information

Proximal Phenotyping and Machine Learning Methods to Identify Septoria Tritici Blotch Disease Symptoms in Wheat.

Publications

Proximal Phenotyping and Machine Learning Methods to Identify Septoria Tritici Blotch Disease Symptoms in Wheat.

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