Vision Based Automated Biomass Estimation of Fronds of Salvinia molesta

Biological sampling for biomass estimation is a highly labor intensive process that can benefit from automation using computer vision. In this paper two algorithms the Green Layer Extraction (GLE) method and Morphology and Marker Controlled Watershed Transformation (MMCWT) method are proposed for the estimation of frond (leaf) area of an invasive aquatic fern Salvinia molesta. The two algorithms are tested using a computer controlled automatic testbed. The mean accuracy for the GLE was found to be 89.66% for estimating the number of fronds and 86.74 % for estimating the frond area. For the MMCWT the respective results are 91.92% and 80.68%. Then the results are subsequently used for developing a mathematical model as growth rate estimation of Salvinia molesta for various concentrations of Sodium di-hydrogen phosphate.