JIA-2018-09

2005 TU Ke-ling et al. Journal of Integrative Agriculture 2018, 17(9): 1999–2006 and thus, likelihood of germination of pepper seeds, was the best model because the selection rate was the highest at 90%, germination percentage increased to 79.1%, and the model’s stability rate was 99.4%. Similar results were obtained in the validation experiment. 4.2. Analysis method In this study, the Seed Identification software was used to extract the physical features of pepper seed. In addition, another two important features, weight and density of single kernel, were introduced. Using these feature as predictors in a series of selection models, single predictors, multilayer perceptron and binary logistic regression models, we determined that all models had high germination percentages, selection rates, and stability rates. Moreover, the best model was developed by using a multilayer perceptron neural network. Satisfactorily high selection rates for sorting out high quality pepper seeds were achieved. However, classification accuracy was not high enough (usually about 85%) but the results obtained were similar to those from a previous study (Kurtulmus et al. 2016). Thus, our results are promising and encourage more research that focus on applying seed selection methods based on image processing and classifiers to improve the laborious task of selecting for high quality pepper seeds. Application of these technological advances in the agricultural industry could significantly improve the processing time and selection accuracy to meet the demand of pepper seed production and therefore, increase economic profits. We are optimistic that the multilayer perceptron neural network model, with 15 features chosen as covariates, can be implemented within the agricultural industry. An auto-sorting device for pepper seeds is expected to be constructed, similar to that described in the research of Huang and Cheng (2017). The device will collect images of physical attributes of seeds, extract those features and then use proposed algorithms to classify the quality of seeds, accurately and efficiently. However, one limitation in the presented work was inefficiency in the slow process of collecting data on weight and density of each and every seed. Weight and density are two very important features, not only because they highly correlated with seed vigor (seedling fresh weight), but also because they were important covariates in the classifier and thus cannot be omitted, but there is no rapid measurement for single kernels’ weight or density. However, further improvements can be made to feature extraction methods and to increase classification accuracy by using various machine learning classifiers and a variety of features, for example, including an advanced segmentation algorithm. 5. Conclusion The selection of pepper seeds based on the single predictor of each of two features (a* and single-kernel weight), had its advantages and disadvantages, for they can improve the germination rates, but the selection rates were low. Both models with a single feature could not satisfy two important conditions simultaneously: increasing germination percentage while achieving a high selection rate. In order to determine the optimal selection method, this study applied the binary logistic regression network classifier, and established a model based on binary logistic regression, to predict whether a seed would germinate. Models developed by multilayer perceptron and binary logistic regression were the better predictors of germination of pepper seeds compared to the single feature models. Comparisons of all models showed that the multilayer perceptron neural network, with 15 features chosen as covariates, was the best model. Germination percentage rose from the original 59.3 to 79.1%, and the selection rate was as high as 90%, and the model stability was 99.4%. Acknowledgements This study was supported by the Beijing Municipal Science and Technology Project, China (Z151100001015004). References Boniecki P, Koszela K, Piekarska-Boniecka H, Weres J, Zaborowicz M, Kujawa S, Majewski A, Raba B. 2015. Neural identification of selected apple pests. Computers and Electronics in Agriculture , 110 , 9–16. Boz I. 2016. Effects of environmentally friendly agricultural land protection programs: Evidence from the Lake Seyfe area of Turkey. Journal of Integrative Agriculture , 15 , 1903–1914. Chaugule A A, Mali S N. 2016. Identification of paddy varieties based on novel seed angle features. Computers and Electronics in Agriculture , 123 , 415–422. Chen H, Xiong L, Hu X, Wang Q, Wu M. 2007. Identification method for moldy peanut kernels based on neural network and image processing. Transactions of the Chinese Society of Agricultural Engineering , 23 , 158–161. (in Chinese) Chen X, Xun Y, Li W, Zhang J. 2010. Combining discriminant analysis and neural networks for corn variety identification. Computers and Electronics in Agriculture , 711 , S48–S53. Chupawa P, Kanjanawanishkul K. 2014. Sweet pepper seed inspection using image processing techniques. Advanced Materials Research , 931–932 , 1614–1618. ElMasry G, Wang N, Vigneault C. 2009. Detecting chilling injury in red delicious apple using hyperspectral imaging and neural networks. Postharvest Biology and Technology , 52 , 1–8.