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Title: :  Enhancing Maize Seed Quality: Defect Detection using Google Net.
PaperId: :  22058
Published in:   International Journal Of Advance Research And Innovative Ideas In Education
Publisher:   IJARIIE
e-ISSN:   2395-4396
Volume/Issue:    Volume 9 Issue 6 2023
DUI:    16.0415/IJARIIE-22058
Licence: :   IJARIIE is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

Author NameAuthor Institute
Taware Ranjit SuyashSVPM COE Malegoan Bk
Rajesh NaleSVPM COE Malegoan Bk
Mahale Sandip NavnitSVPM COE Malegoan Bk
Pharate Abhishek BhujangraoSVPM COE Malegoan Bk
Pawar Sanket MachindraSVPM COE Malegoan Bk

Abstract

Information Technology Engineering
Deep Learning Techniques, Google Net, Maize Seed Defect Detection, CNNs, Transfer Learning, Dataset Curation, Data Preprocessing, Model Adaptation, Seed Quality Control, Automated Defect Detection, Image Classification
This study explores the application of deep learning techniques, specifically Google Net, to detect maize seed defects. The research uses convolution neural networks (CNNs) and transfer learning to accurately identify and classify defects in maize seeds. The process begins with dataset curation and preprocessing, followed by the adaptation of the Google Net architecture. The model is trained, validated, and tested, providing insights into its efficacy. Maize, a staple crop worldwide, requires meticulous seed quality control to ensure optimal yield. Traditional methods of seed inspection are labor-intensive and susceptible to human error. Deep learning techniques offer a promising avenue for automating seed defect detection. Google Net, with its intricate architecture and remarkable feature extraction capabilities, emerges as a potent tool for accurate and efficient defect identification. The study collects a diverse dataset containing images of maize seeds with various defects, labeled to encompass fungal infections, physical damage, and discoloration. Data preprocessing involves resizing images and applying data augmentation techniques to enhance model generalization. The Google Net architecture, consisting of parallel convolution pathways and filters of varying sizes, excels in capturing intricate patterns and features. The model is initialized with pre-trained weights from Image Net, fine-tuned for the maize seed defect detection task, and customized for effective distinction between healthy seeds and those exhibiting defects. The model's efficacy is evaluated on an independent testing dataset, providing a comprehensive view of its defect detection capabilities. In conclusion, this study demonstrates the viability of using Google Net for maize seed defect detection, capitalizing on the strengths of CNNs and transfer learning. This technology offers an automated and efficient solution to seed quality control, contributing significantly to crop production and global food security.

Citations

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IJARIIE Taware Ranjit Suyash, Rajesh Nale, Mahale Sandip Navnit, Pharate Abhishek Bhujangrao, and Pawar Sanket Machindra. "Enhancing Maize Seed Quality: Defect Detection using Google Net." International Journal Of Advance Research And Innovative Ideas In Education Volume 9 Issue 6 2023 Page 743-746
MLA Taware Ranjit Suyash, Rajesh Nale, Mahale Sandip Navnit, Pharate Abhishek Bhujangrao, and Pawar Sanket Machindra. "Enhancing Maize Seed Quality: Defect Detection using Google Net.." International Journal Of Advance Research And Innovative Ideas In Education 9.6(2023) : 743-746.
APA Taware Ranjit Suyash, Rajesh Nale, Mahale Sandip Navnit, Pharate Abhishek Bhujangrao, & Pawar Sanket Machindra. (2023). Enhancing Maize Seed Quality: Defect Detection using Google Net.. International Journal Of Advance Research And Innovative Ideas In Education, 9(6), 743-746.
Chicago Taware Ranjit Suyash, Rajesh Nale, Mahale Sandip Navnit, Pharate Abhishek Bhujangrao, and Pawar Sanket Machindra. "Enhancing Maize Seed Quality: Defect Detection using Google Net.." International Journal Of Advance Research And Innovative Ideas In Education 9, no. 6 (2023) : 743-746.
Oxford Taware Ranjit Suyash, Rajesh Nale, Mahale Sandip Navnit, Pharate Abhishek Bhujangrao, and Pawar Sanket Machindra. 'Enhancing Maize Seed Quality: Defect Detection using Google Net.', International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 6, 2023, p. 743-746. Available from IJARIIE, https://ijariie.com/AdminUploadPdf/Enhancing_Maize_Seed_Quality__Defect_Detection_using_Google_Net__ijariie22058.pdf (Accessed : ).
Harvard Taware Ranjit Suyash, Rajesh Nale, Mahale Sandip Navnit, Pharate Abhishek Bhujangrao, and Pawar Sanket Machindra. (2023) 'Enhancing Maize Seed Quality: Defect Detection using Google Net.', International Journal Of Advance Research And Innovative Ideas In Education, 9(6), pp. 743-746IJARIIE [Online]. Available at: https://ijariie.com/AdminUploadPdf/Enhancing_Maize_Seed_Quality__Defect_Detection_using_Google_Net__ijariie22058.pdf (Accessed : )
IEEE Taware Ranjit Suyash, Rajesh Nale, Mahale Sandip Navnit, Pharate Abhishek Bhujangrao, and Pawar Sanket Machindra, "Enhancing Maize Seed Quality: Defect Detection using Google Net.," International Journal Of Advance Research And Innovative Ideas In Education, vol. 9, no. 6, pp. 743-746, Nov-Dec 2023. [Online]. Available: https://ijariie.com/AdminUploadPdf/Enhancing_Maize_Seed_Quality__Defect_Detection_using_Google_Net__ijariie22058.pdf [Accessed : ].
Turabian Taware Ranjit Suyash, Rajesh Nale, Mahale Sandip Navnit, Pharate Abhishek Bhujangrao, and Pawar Sanket Machindra. "Enhancing Maize Seed Quality: Defect Detection using Google Net.." International Journal Of Advance Research And Innovative Ideas In Education [Online]. volume 9 number 6 ().
Vancouver Taware Ranjit Suyash, Rajesh Nale, Mahale Sandip Navnit, Pharate Abhishek Bhujangrao, and Pawar Sanket Machindra. Enhancing Maize Seed Quality: Defect Detection using Google Net.. International Journal Of Advance Research And Innovative Ideas In Education [Internet]. 2023 [Cited : ]; 9(6) : 743-746. Available from: https://ijariie.com/AdminUploadPdf/Enhancing_Maize_Seed_Quality__Defect_Detection_using_Google_Net__ijariie22058.pdf
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