Machine Learning based Robust Model for Seed Germination Detection and Classification
Keywords:
Germination, Seed quality, Machine learning, Artificial intelligence, classificationAbstract
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.
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Genze, N., Bharti, R., Grieb, M., Schultheiss, S.J. and Grimm, D.G., 2020. Accurate machine learning-based germination detection, prediction and quality assessment of three grain crops. Plant methods, 16(1), pp.1-11.
Colmer, J., O'Neill, C.M., Wells, R., Bostrom, A., Reynolds, D., Websdale, D., Shiralagi, G., Lu, W., Lou, Q., Le Cornu, T. and Ball, J., 2020. SeedGerm: a cost‐effective phenotyping platform for automated seed imaging and machine‐learning based phenotypic analysis of crop seed germination. New Phytologist, 228(2), pp.778-793.
Nehoshtan, Y., Carmon, E., Yaniv, O., Ayal, S. and Rotem, O., 2021. Robust seed germination prediction using deep learning and RGB image data. Scientific reports, 11(1), pp.1-10.
Yang, U., Oh, S., Wi, S.G., Lee, B.R., Lee, S.H. and Kim, M.S., 2021. Classification of Germination Images of Pear Pollen Using Random Forest and Convolution Neural Network Models. IEEE Access, 9, pp.45993-45999.
Durai, S. and Mahesh, C., 2021. Research on varietal classification and germination evaluation system for rice seed using hand-held devices. Acta Agriculturae Scandinavica, Section B—Soil & Plant Science, 71(9), pp.939-955.
Yasam, S., Nair, S.A.H. and Kumar, K.P., 2022. Supervised learning-based seed germination ability prediction for precision farming. Soft Computing, pp.1-12.
Aasim, M., Katırcı, R., Akgur, O., Yildirim, B., Mustafa, Z., Nadeem, M.A., Baloch, F.S., Karakoy, T. and Yılmaz, G., 2022. Machine learning (ML) algorithms and artificial neural network for optimizing in vitro germination and growth indices of industrial hemp (Cannabis sativa L.). Industrial Crops and Products, 181, p.114801.
Valente, J., Kooistra, L. and Mücher, S., 2019. Fast classification of large germinated fields via high-resolution UAV imagery. IEEE Robotics and Automation Letters, 4(4), pp.3216-3223.
Durai, S., 2021. Labelled Image Dataset Preparation for Rice Seed Germination Prediction and Variety Classifictaion using Low Cost Devices. Turkish Journal of Computer and Mathematics Education (TURCOMAT), 12(9), pp.245-249.
Awty-Carroll, D., Clifton-Brown, J. and Robson, P., 2018. Using k-NN to analyse images of diverse germination phenotypes and detect single seed germination in Miscanthus sinensis. Plant methods, 14(1), pp.1-7.
de Medeiros, A.D., Pinheiro, D.T., Xavier, W.A., da Silva, L.J. and dos Santos Dias, D.C.F., 2020. Quality classification of Jatropha curcas seeds using radiographic images and machine learning. Industrial Crops and Products, 146, p.112162.
Hu, Y., Wang, Z., Li, X., Li, L., Wang, X. and Wei, Y., 2022. Nondestructive Classification of Maize Moldy Seeds by Hyperspectral Imaging and Optimal Machine Learning Algorithms. Sensors, 22(16), p.6064.
Jin, B., Qi, H., Jia, L., Tang, Q., Gao, L., Li, Z. and Zhao, G., 2022. Determination of viability and vigor of naturally-aged rice seeds using hyperspectral imaging with machine learning. Infrared Physics & Technology, 122, p.104097.
Yang, J., Sun, L., Xing, W., Feng, G., Bai, H. and Wang, J., 2021. Hyperspectral prediction of sugarbeet seed germination based on gauss kernel SVM. Spectrochimica Acta Part A: Molecular and Biomolecular Spectroscopy, 253, p.119585.
Zhou, S., Sun, L., Xing, W., Feng, G., Ji, Y., Yang, J. and Liu, S., 2020. Hyperspectral imaging of beet seed germination prediction. Infrared Physics & Technology, 108, p.103363.
Medeiros, A.D.D., Silva, L.J.D., Ribeiro, J.P.O., Ferreira, K.C., Rosas, J.T.F., Santos, A.A. and Silva, C.B.D., 2020. Machine learning for seed quality classification: An advanced approach using merger data from FT-NIR spectroscopy and X-ray imaging. Sensors, 20(15), p.4319.
Ahmed, M.R., Yasmin, J., Park, E., Kim, G., Kim, M.S., Wakholi, C., Mo, C. and Cho, B.K., 2020. Classification of watermelon seeds using morphological patterns of X-ray imaging: a comparison of conventional machine learning and deep learning. Sensors, 20(23), p.6753.
Bosakova-Ardenska, A., Kutryanska, M., Boyanova, P. and Panayotov, P., 2022, August. Application of images segmentation and median filter for white brined cheese structure evaluation. In AIP Conference Proceedings (Vol. 2570, No. 1, p. 020014). AIP Publishing LLC.
Lokku, G., Reddy, G.H. and Prasad, M.G., 2022. Optimized scale-invariant feature transform with local tri-directional patterns for facial expression recognition with deep learning model. The Computer Journal, 65(9), pp.2506-2527.
Singh, B., Sihag, P. and Singh, K., 2017. Modelling of impact of water quality on infiltration rate of soil by random forest regression. Modeling Earth Systems and Environment, 3(3), pp.999-1004.
Abpeykar, S. and Ghatee, M., 2019. An ensemble of RBF neural networks in decision tree structure with knowledge transferring to accelerate multi-classification. Neural Computing and Applications, 31(11), pp.7131-7151.
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