Early Detection of Fusarium oxysporum Infection in Processing Tomatoes ( Solanum lycopersicum ) and Pathogen-Soil Interactions Using a Low-Cost Portable Electronic Nose and Machine Learning Modeling.

Abstract

The early detection of pathogen infections in plants has become an important aspect of integrated disease management. Although previous research demonstrated the idea of applying digital technologies to monitor and predict plant health status, there is no effective system for detecting pathogen infection before symptomatology appears. This paper presents the use of a low-cost and portable electronic nose coupled with machine learning (ML) models for early disease detection. Several artificial neural network models were developed to predict plant physiological data and classify processing tomato plants and soil samples according to different levels of pathogen inoculum by using e-nose outputs as inputs, plant physiological data, and the level of infection as targets. Results showed that the pattern recognition models based on different infection levels had an overall accuracy of 94.4-96.8% for tomato plants and between 94.81% and 96.22% for soil samples. For the prediction of plant physiological parameters (photosynthesis, stomatal conductance, and transpiration) using regression models or tomato plants, the overall correlation coefficient was 0.97-0.99, with very significant slope values in the range 0.97-1. The performance of all models shows no signs of under or overfitting. It is hence proven accurate and valid to use the electronic nose coupled with ML modeling for effective early disease detection of processing tomatoes and could also be further implemented to monitor other abiotic and biotic stressors.

Plant phenotyping relevance

トマトの感染状態と生理形質を、電子鼻と機械学習で推定・分類する方法が研究の中心であり、植物病害フェノタイピング手法に該当する。

Code and data availability

The paper's e-nose measurements, physiological data, and Matlab ML code are not publicly available: the Data Availability Statement restricts sharing to University of Melbourne approval. The only supplementary material is a flowchart figure (Figure S1), not a dataset or code. No paper-specific public asset qualifies.

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