Development of an intelligent laser biospeckle system for early detection and classification of soybean seeds infected with seed-borne fungal pathogen (Colletotrichum truncatum)

• Published in: Biosystems engineering 2021-12, Vol.212, p.442-457
• Main Authors: Singh, Puneet, Chatterjee, Amit, Rajput, Laxman S., Rana, Santosh, Kumar, Sanjeev, Nataraj, Vennampally, Bhatia, Vimal, Prakash, Shashi
• Format: Article
• Language: English
• Subjects: Agriculture; Biospeckle Analysis; Early Detection; Laser; Machine Learning; Seed-Borne Fungi
• ISSN: 1537-5110; 1537-5129
• DOI: 10.1016/j.biosystemseng.2021.11.002

There is a need for developing rapid and non-destructive techniques for the early detection of seed-borne fungal pathogen because they can be an essential step towards adopting effective disease control measures. Existing techniques for detecting seed-borne diseases have poor sensitivity towards early stages of pathogen development (i.e., when seeds are asymptomatic) and they are also expensive, time-consuming, complex, require mycological skills and destructive testing operations. Aiming at overcoming the above limitations of the existing techniques, a novel laser biospeckle based method is proposed for early detection of seed-borne fungal infection in conjunction with machine learning. Soybean seeds infected by low concentrations (102-106 spores ml−1) of Colletotrichum truncatum were analysed by using full field biospeckle analysis to establish the possible relationship between biological activity in early stages of pathogen infection, with and without the use of frequency filtering. The results demonstrate that the biospeckle activity (BA), for both, raw and frequency filtered data was significantly high (p