Machine Learning based Robust Model for Seed Germination Detection and Classification - Yasam, Srinath; H. Nair, S. Anu; Kumar, K.P. Sanal (International Journal of Intelligent Systems and Applications in Engineering, 2023)
Seed germination assessment is a quite difficult for the research team members to evaluate performance and quality. Generally, seed assessment can be performed manually, which is an error-prone, cumbersome, and time-consuming process. The typical image analysis method is not suitable for largescale germination experiments, since they frequently depend on manual adjustment of color-based threshold. Various researcher workers have projected methods to automate these processes to alleviate the manual processes in seed testing, which is extremely error-prone. Lately, image analysis technique has been used for seed detection, as they provide unbiased and quantitative measurements and can be easily automatized with minimal errors. Hence, this study designs a new Machine Learning based Robust Classification Model for Seed Germination (MLRCM-SG). The presented MLRCM-SG technique carries out the automated identification and classification of germination, to evaluate the seed quality. To attain this, the presented MLRCM-SG technique initially undergoes preprocessing in two stages namely CLAHE based contrast enhancement and median filter (MF) based noise removal. In addition, the presented MLRCM-SG technique employs Scale-Invariant Feature Transform (SIFT) technique is used in preprocessed images for the collection of feature vectors. Finally, process of classification takes place using two ML classifiers namely random forest (RF) and decision tree (DT). The experimental validation of the MLRCM-SG technique is tested on seed germination dataset and the outcome shows the remarkable performance of the MLRCM-SG algorithm compared to current approaches.