Biospeckle-Based Sensor for Characterization of Charcoal Rot (Macrophomina Phaseolina (Tassi) Goid) Disease in Soybean (Glycine Max (L.) Merr.) Crop - Puneet Singh; Amit Chatterjee; Laxman Singh Rajput; Sanjeev Kumar; Vennampally Nataraj; Vimal Bhatia; Shashi Prakash (IEEE Access, 2021)
Charcoal rot is one of the most destructive fungal diseases of soybean, caused by the pathogen called Macrophomina phaseolina. This disease thrives in warm and dry conditions, affecting the yield of soybean and other important agronomic crops. Existing methods used to screen the disease suffer from several drawbacks including, manual rating, low accuracy, high operating time, and high system complexity. To circumvent these drawbacks, we developed a laser biospeckle based sensor to characterize the charcoal rot in soybean crop. Applicability of the proposed sensor was tested to analyze three major aspects of plant disease management, viz. characterization of disease progression, early identification of disease symptoms, and analysis of genetic resistance of the given cultivar towards the disease. The experiments were conducted during Kharif season for two consecutive years (2019 and 2020) on two cultivars of soybean, namely, JS 90-41 and AMS-MB-5-18. The proposed sensor as well as standard rating protocol (i.e. measuring the length of necrosis) were used to analyze the extent of disease. To characterize the disease progression and the genetic resistance of different cultivars against M. phaseolina, two new metrics, charcoal rot severity index and disease susceptibility index were introduced. Biospeckle activity was found to be strongly correlated with the lesion length of infected plant stems (r = +0.96, p <; .01, two-tailed (for JS 90-41) and (r = +0.95, p <; .01, two-tailed (for AMS-MB-5-18) for the year 2019; and r = +0.97, p <; .01, two-tailed (for JS 90-41) and r = +0.93, p <; .01, two-tailed (for AMS-MB-5-18) for the year 2020). Experimental results clearly indicate that the proposed sensor can be used as an efficient tool to detect the disease in its early stages of pathogen development. This study provides insights into development and implementation of disease control measures for increasing soybean crop production.