ABSTRACT
Segregation of seeds of different crops grown in the mixed cropping is a major cause of concern for the farmers as well as the food industry. Also, the classification and packaging of seeds based on their quality is a challenging task for farmers and agro-industries. Moreover, Post-thrashing separation of seeds by the traditional techniques such as sieving, hand-picking, etc. is a time-consuming and tedious task. Thus, there is a need to automate seed segregation. The potential of deep learning and machine learning techniques in object detection, classification, and pattern recognition motivated the researchers to employ these techniques for the automatic segregation of seeds at the harvesting site. The techniques proposed so far focus on the classification of seeds of different crops. Limited research work is observed that focuses on the classification of seeds of crops grown as a part of mixed cropping as well as seeds of different quality standards. Also, there is a huge scope to improve the classification performance of the proposed models. The purpose of this research to develop the deep learning-based system 'Mixed Cropping Seed Classifier and Quality Tester (MCSCQT)' for accurate classification and quality testing of seeds based on their shape, color, and texture. The system is trained on the dataset comprising labelled images of healthy and diseased seeds of pearl millet and maize. It reports the highest precision and recall of 99%. The efficacy of the system in discriminating the seeds of pearl millet and maize may prove a game-changer for the food industry. Also, its capability in recognition of diseased and healthy seeds of maize enhances its utility in the food industry.
- Yaser S. Abu-Mostafa. 1992. Neural networks and learning.Google Scholar
- Ouiza Adjemout, Kamal Hammouche, and Moussa Diaf. 2007. Automatic seeds recognition by size, form and texture features. 2007 9th Int. Symp. Signal Process. its Appl. ISSPA 2007, Proc. (2007), 2--5. DOI:https://doi.org/10.1109/ISSPA.2007.4555428Google ScholarCross Ref
- Monika Agarwal, Geeta Rani, and Vijaypal Singh Dhaka. 2020. Optimized contrast enhancement for tumor detection. Int. J. Imaging Syst. Technol. 30, 3 (2020), 687--703. DOI:https://doi.org/10.1002/ima.22408Google ScholarCross Ref
- Alex Krizhevsky. 2010. ImageNet Classification with Deep Convolutional Neural Networks. (2010), 1--1432. DOI:https://doi.org/10.1201/9781420010749Google ScholarCross Ref
- Aqib Ali, Salman Qadri, Wali Khan Mashwani, Samir Brahim Belhaouari, Samreen Naeem, Sidra Rafique, Farrukh Jamal, Christophe Chesneau, and Sania Anam. 2020. Machine learning approach for the classification of corn seed using hybrid features. Int. J. Food Prop. 23, 1 (2020), 1097--1111. DOI:https://doi.org/10.1080/10942912.2020.1778724Google ScholarCross Ref
- Yahya Altuntaş, Zafer Cömert, and Adnan Fatih Kocamaz. 2019. Identification of haploid and diploid maize seeds using convolutional neural networks and a transfer learning approach. Comput. Electron. Agric. 163, 40 (2019), 1--11. DOI:https://doi.org/10.1016/j.compag.2019.104874Google ScholarDigital Library
- Xiulin Bai, Chu Zhang, Qinlin Xiao, Yong He, and Yidan Bao. 2020. Application of near-infrared hyperspectral imaging to identify a variety of silage maize seeds and common maize seeds. RSC Adv. 10, 20 (2020), 11707--11715. DOI:https://doi.org/10.1039/c9ra11047jGoogle ScholarCross Ref
- V. Betina. 2016. The Unreasonable Effectiveness of Noisy Data for Fine-Grained Recognition. Neoplasma 16, 1 (2016), 23--32. DOI:https://doi.org/10.1007/978-3-319-46487-9Google ScholarCross Ref
- Manish Bhurtel. Maize seed dataset. Retrieved from https://www.kaggle.com/zom8ie99/maize-seedGoogle Scholar
- Manish Bhurtel. 2019. Deep learning based seed quality tester. November (2019).Google Scholar
- Alexey Bochkovskiy, Chien-Yao Wang, and Hong-Yuan Mark Liao. 2020. YOLOv4: Optimal Speed and Accuracy of Object Detection. (2020).Google Scholar
- Scott Reed3 Christian Szegedy1, Wei Liu2, Yangqing Jia1, Pierre Sermanet1. 2019. Going Deeper with Convolutions. Des. Track. Knowl. Manag. Metrics (2019), 163--182. DOI:https://doi.org/10.1108/978-1-78973-723-320191012Google ScholarCross Ref
- S Joel Franklin. 2020. k-means clustering as classification algorithm. Medium. Retrieved from https://medium.com/@joel_34096/k-means-clustering-for-image-classification-a648f28bdc47Google Scholar
- Pablo M. Granitto, Pablo F. Verdes, and H. Alejandro Ceccatto. 2005. Large-scale investigation of weed seed identification by machine vision. Comput. Electron. Agric. 47, 1 (2005), 15--24. DOI:https://doi.org/10.1016/j.compag.2004.10.003Google ScholarDigital Library
- Li Haoguang, Pang Yan, Shen Xuefeng, and Yu Yunhua. 2018. Single coated maize seed identification based on deep learning. Proc. 13th IEEE Conf. Ind. Electron. Appl. ICIEA 2018 (2018), 1520--1525. DOI:https://doi.org/10.1109/ICIEA.2018.8397950Google ScholarCross Ref
- Kaiming He, Xiangyu Zhang, Shaoqing Ren, and Jian Sun. 2016. Deep residual learning for image recognition. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2016-Decem, (2016), 770--778. DOI:https://doi.org/10.1109/CVPR.2016.90Google ScholarCross Ref
- FAO Headquater. FAOSTAT 2020. Retrieved from http://www.fao.org/faostat/en/#search/maizeGoogle Scholar
- Young Jin Heo, Se Jin Kim, Dayeon Kim, Keondo Lee, and Wan Kyun Chung. 2018. Super-high-purity seed sorter using low-latency image-recognition based on deep learning. IEEE Robot. Autom. Lett. 3, 4 (2018), 3035--3042. DOI:https://doi.org/10.1109/LRA.2018.2849513Google ScholarCross Ref
- Andrew G. Howard, Menglong Zhu, Bo Chen, Dmitry Kalenichenko, Weijun Wang, Tobias Weyand, Marco Andreetto, and Hartwig Adam. 2017. MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applications. (2017).Google Scholar
- Sheng Huang, Xiaofei Fan, Lei Sun, Yanlu Shen, and Xuesong Suo. 2019. Research on Classification Method of Maize Seed Defect Based on Machine Vision. J. Sensors 2019, 1 (2019). DOI:https://doi.org/10.1155/2019/2716975Google ScholarCross Ref
- Joseph Nelson Jacob Solawetz. 2020. Train YOLOv5 On a Custom Dataset-Roboflow. Retrieved from https://blog.roboflow.com/how-to-train-yolov5-on-a-custom-dataset/Google Scholar
- Shiqiang Jia, Dong An, Zhe Liu, Jiancheng Gu, Shaoming Li, Xiaodong Zhang, Dehai Zhu, Tingting Guo, and Yanlu Yan. 2015. Variety identification method of coated maize seeds based on near-infrared spectroscopy and chemometrics. J. Cereal Sci. 63, (2015), 21--26. DOI:https://doi.org/10.1016/j.jcs.2014.07.003Google ScholarCross Ref
- Peng Jiang, Yuehan Chen, Bin Liu, Dongjian He, and Chunquan Liang. 2019. Real-Time Detection of Apple Leaf Diseases Using Deep Learning Approach Based on Improved Convolutional Neural Networks. IEEE Access 7, (2019), 59069--59080. DOI:https://doi.org/10.1109/ACCESS.2019.2914929Google ScholarCross Ref
- Kantip Kiratiratanapruk and Wasin Sinthupinyo. 2011. Color and texture for corn seed classification by machine vision. 2011 Int. Symp. Intell. Signal Process. Commun. Syst. "The Decad. Intell. Green Signal Process. Commun. ISPACS 2011 (2011), 7--11. DOI:https://doi.org/10.1109/ISPACS.2011.6146100Google ScholarCross Ref
- A. Koirala, K. B. Walsh, Z. Wang, and C. McCarthy. 2019. Deep learning for real-time fruit detection and orchard fruit load estimation: benchmarking of 'MangoYOLO.' Precis. Agric. 0123456789 (2019). DOI:https://doi.org/10.1007/s11119-019-09642-0Google ScholarCross Ref
- Anand Koirala, Kerry B. Walsh, Zhenglin Wang, and Cheryl McCarthy. 2019. Deep learning - Method overview and review of use for fruit detection and yield estimation. Comput. Electron. Agric. 162, March (2019), 219--234. DOI:https://doi.org/10.1016/j.compag.2019.04.017Google ScholarDigital Library
- Nidhi Kundu, Geeta Rani, and Vijaypal Singh Dhaka. 2020. Machine Learning and IoT based Disease Predictor and Alert Generator System. Proc. 4th Int. Conf. Comput. Methodol. Commun. ICCMC 2020 Iccmc (2020), 764--769. DOI:https://doi.org/10.1109/ICCMC48092.2020.ICCMC-000142Google ScholarCross Ref
- Ferhat Kurtulmuş. 2020. Identification of sunflower seeds with deep convolutional neural networks. J. Food Meas. Charact. 0123456789 (2020). DOI:https://doi.org/10.1007/s11694-020-00707-7Google ScholarCross Ref
- Jun Liu and Xuewei Wang. 2020. Tomato Diseases and Pests Detection Based on Improved Yolo V3 Convolutional Neural Network. Front. Plant Sci. 11, June (2020), 1--12. DOI:https://doi.org/10.3389/fpls.2020.00898Google ScholarCross Ref
- Te Ma, Satoru Tsuchikawa, and Tetsuya Inagaki. 2020. Rapid and non-destructive seed viability prediction using near-infrared hyperspectral imaging coupled with a deep learning approach. Comput. Electron. Agric. 177, April (2020). DOI:https://doi.org/10.1016/j.compag.2020.105683Google ScholarCross Ref
- Tom M. Mitchell. 2019. Machine Learning. DOI:https://doi.org/10.1109/ICDAR.2019.00014Google ScholarCross Ref
- Modi. Maize production in India. Retrieved from https://www.businesstoday.in/magazine/features/cargill-india-ceo-siraz-chaudhury-maize/story/205721.htmlGoogle Scholar
- Chao Ni, Dongyi Wang, Robert Vinson, Maxwell Holmes, and Yang Tao. 2019. Automatic inspection machine for maize kernels based on deep convolutional neural networks. Biosyst. Eng. 178, (2019), 131--144. DOI:https://doi.org/10.1016/j.biosystemseng.2018.11.010Google ScholarCross Ref
- Pengcheng Nie, Jinnuo Zhang, Xuping Feng, Chenliang Yu, and Yong He. 2019. Classification of hybrid seeds using near-infrared hyperspectral imaging technology combined with deep learning. Sensors Actuators, B Chem. 296, May (2019), 126630. DOI:https://doi.org/10.1016/j.snb.2019.126630Google ScholarCross Ref
- Lei Pang, Sen Men, Lei Yan, and Jiang Xiao. 2020. Rapid Vitality Estimation and Prediction of Corn Seeds Based on Spectra and Images Using Deep Learning and Hyperspectral Imaging Techniques. IEEE Access 8, (2020), 123026--123036. DOI:https://doi.org/10.1109/ACCESS.2020.3006495Google ScholarCross Ref
- J. P. Papa, A. X. Falcão, and C. T.N. Suzuki. 2009. Supervised pattern classification based on optimum-path forest. Int. J. Imaging Syst. Technol. 19, 2 (2009), 120--131. DOI:https://doi.org/10.1002/ima.20188Google ScholarCross Ref
- Mina Nath Paudel. 2016. Multiple cropping for Raising Productivity and Farm Income of Small Farmers. J. Nepal Agric. Res. Counc. 2, December (2016), 37--45.Google ScholarCross Ref
- Yu L. Pavlov. 2019. Random forests. Random For. (2019), 1--122. DOI:https://doi.org/10.1201/9780429469275-8Google ScholarCross Ref
- Douglas F. Pereira, Priscila T.M. Saito, and Pedro H. Bugatti. 2016. An image analysis framework for effective classification of seed damages. Proc. ACM Symp. Appl. Comput. 04--08.Apri, (2016), 61--66. DOI:https://doi.org/10.1145/2851613.2851637Google ScholarDigital Library
- Geeta Rani and Monika Agarwal. 2020. Contrast Enhancement Using Optimum Threshold Selection. Int. J. Softw. Innov. 8, 3 (2020), 96--118. DOI:https://doi.org/10.4018/IJSI.2020070107Google ScholarCross Ref
- Joseph Redmon, Santosh Divvala, Ross Girshick, and Ali Farhadi. 2016. You only look once: Unified, real-time object detection. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2016-Decem, (2016), 779--788. DOI:https://doi.org/10.1109/CVPR.2016.91Google ScholarCross Ref
- Joseph Redmon and Ali Farhadi. 2017. YOLO9000: Better, faster, stronger. Proc. - 30th IEEE Conf. Comput. Vis. Pattern Recognition, CVPR 2017 2017-Janua, (2017), 6517--6525. DOI:https://doi.org/10.1109/CVPR.2017.690Google ScholarCross Ref
- Joseph Redmon and Ali Farhadi. 2018. YOLOv3: An Incremental Improvement. (2018).Google Scholar
- Tianwei Ren, Zhe Liu, Lin Zhang, Diyou Liu, Xiaojie Xi, Yanghui Kang, Yuanyuan Zhao, Chao Zhang, Shaoming Li, and Xiaodong Zhang. 2020. Early identification of seed maize and common maize production fields using sentinel-2 images. Remote Sens. 12, 13 (2020), 1--21. DOI:https://doi.org/10.3390/rs12132140Google ScholarCross Ref
- Karen Simonyan and Andrew Zisserman. 2015. Very deep convolutional networks for large-scale image recognition. 3rd Int. Conf. Learn. Represent. ICLR 2015 - Conf. Track Proc. (2015), 1--14.Google Scholar
- Deepak Sinwar, Vijaypal Singh Dhaka, Manoj Kumar Sharma, and Geeta Rani. 2020. AI-Based Yield Prediction and Smart Irrigation. 2, (2020), 155--180. DOI:https://doi.org/10.1007/978-981-15-0663-5_8Google ScholarCross Ref
- Christian Szegedy, Vincent Vanhoucke, Sergey Ioffe, Jon Shlens, and Zbigniew Wojna. 2016. Rethinking the Inception Architecture for Computer Vision. Proc. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. 2016-Decem, (2016), 2818--2826. DOI:https://doi.org/10.1109/CVPR.2016.308Google ScholarCross Ref
- Keling Tu, Shaozhe Wen, Ying Cheng, Tingting Zhang, Tong Pan, Jie Wang, Jianhua Wang, and Qun Sun. 2021. A non-destructive and highly efficient model for detecting the genuineness of maize variety 'JINGKE 968' using machine vision combined with deep learning. Comput. Electron. Agric. 182, January (2021), 106002. DOI:https://doi.org/10.1016/j.compag.2021.106002Google ScholarDigital Library
- Tony Vaiciulis. SVM. Retrieved from https://wwwcdf.fnal.gov/physics/statistics/recommendations/svm/svm.htmlGoogle Scholar
- Balaji Veeramani, John W. Raymond, and Pritam Chanda. 2018. DeepSort: Deep convolutional networks for sorting haploid maize seeds. BMC Bioinformatics 19, Suppl 9 (2018). DOI:https://doi.org/10.1186/s12859018-2267-2Google ScholarCross Ref
- Chien Yao Wang, Hong Yuan Mark Liao, Yueh Hua Wu, Ping Yang Chen, Jun Wei Hsieh, and I. Hau Yeh. 2020. CSPNet: A new backbone that can enhance learning capability of CNN. IEEE Comput. Soc. Conf. Comput. Vis. Pattern Recognit. Work. 2020-June, (2020), 1571--1580. DOI:https://doi.org/10.1109/CVPRW50498.2020.00203Google ScholarCross Ref
- Qimei Wang and Feng Qi. 2019. Tomato diseases recognition based on faster RCNN. Proc. - 10th Int. Conf. Inf. Technol. Med. Educ. ITME 2019 (2019), 772--776. DOI:https://doi.org/10.1109/ITME.2019.00176Google ScholarCross Ref
- Jun Zhang, Limin Dai, and Fang Cheng. 2021. Corn seed variety classification based on hyperspectral reflectance imaging and deep convolutional neural network. J. Food Meas. Charact. 15, 1 (2021), 484--494. DOI:https://doi.org/10.1007/s11694-020-00646-3Google ScholarCross Ref
- Guoyang Zhao, Longzhe Quan, Hailong Li, Huaiqu Feng, Songwei Li, Shuhan Zhang, and Ruiqi Liu. 2021. Real-time recognition system of soybean seed full-surface defects based on deep learning. Comput. Electron. Agric. 187, May (2021), 106230. DOI:https://doi.org/10.1016/j.compag.2021.106230Google ScholarDigital Library
- Shaolong Zhu, Jinyu Zhang, Maoni Chao, Xinjuan Xu, Puwen Song, Jinlong Zhang, and Zhongwen Huang. 2020. A rapid and highly efficient method for the identification of soybean seed varieties: Hyperspectral images combined with transfer learning. Molecules 25, 1 (2020). DOI:https://doi.org/10.3390/molecules25010152Google ScholarCross Ref
Index Terms
- Seeds Classification and Quality Testing Using Deep Learning and YOLO v5
Recommendations
Online classification of soybean seeds based on deep learning
AbstractTo quickly evaluate soybean quality, we proposed a deep learning-based method for online classification of soybean seeds. Firstly, images of soybean seeds with uneven illumination were segmented based on the multi-scale Retinex with color ...
Thermal and hyperspectral imaging for Norway spruce (Picea abies) seeds screening
Hyperspectral and thermal lifetime imaging were used to assess spruce seed quality.Viable, empty and infested seeds were resolved with high accuracy with both methods.400-1000nm data was not as informative as 1000-2500nm and thermal decay ...
Deep Learning Model for Automatic Detection of Oil Palm Trees in Indonesia with YOLO-V5
SIET '23: Proceedings of the 8th International Conference on Sustainable Information Engineering and TechnologyIndonesia is one of the world's largest palm oil producers. The area of oil palm plantations in Indonesia is increasing every year. However, large-scale land clearing for oil palm plantations is considered a cause of deforestation, negatively impacting ...
Comments