Harnessing the Power of Machine Learning and Artificial Intelligence in Seed Science and Technology: A Review
254 / 181
Authors
-
AV BARAD
Junagadh Agricultural University, Junagadh, Gujarat-362001, India
Author
-
JB PATEL
Junagadh Agricultural University, Junagadh, Gujarat-362001, India
Author
-
YASH J CHAUDHARY
Junagadh Agricultural University, Junagadh, Gujarat-362001, India
Author
-
JYOTI SONDARVA
Junagadh Agricultural University, Junagadh, Gujarat-362001, India
Author
Keywords:
Machine learning (ML), Artificial intelligence (AI), image recognition, advanced algorithmsAbstract
Integrating machine learning (ML) and artificial intelligence (AI) into seed science and technology represents a transformative paradigm in agricultural research. This study explores the potential and applications of ML and AI methodologies to enhance various facets of seed-related processes. From seed viability assessment to crop yield prediction, using advanced algorithms enable a more precise and efficient understanding of seed characteristics. The abstract delves into specific applications such as predictive modeling, image recognition, and data-driven decision-making in seed breeding. By harnessing the power of ML and AI, researchers and practitioners in seed science can revolutionize traditional approaches, fostering sustainable agriculture and ensuring food security in an ever-evolving global landscape.
Downloads
References
1. DE MEDEIROS AD, NP CAPOBIANGO, JM DA SILVA, LJ DA SILVA, CB DA SILVA AND DCF DOS SANTOS DIAS (2020a). Interactive machine learning for soybean seed and seedling quality classification. Scientific reports, 10(1): 11267 (https:// doi.org/10.1038% 2Fs41598- 020-68273-y).
2. DE MEDEIROS AD, LJ DA SILVA, JPO RIBEIRO, KC FERREIRA, JTF ROSAS, AA SANTOS AND CB DA SILVA (2020b). Machine learning for seed quality classification: An advanced approach using merger data from FT-NIR spectroscopy and X-ray imaging. Sensors, 20(15): 4319 (https://doi.org/10.3390/s20154319).
3. FAN Y, S MA AND T WU (2020). Individual wheat kernels vigor assessment based on NIR spectroscopy coupled with machine learning methodologies. Infrared Physics and Technology, 105: 103213 (http://dx.doi.org/10.1016/ j.infrared.2020.103213).
4. AKHTER R AND SA SOFI (2022). Precision agriculture using IoT data analytics and machine learning. Journal of King Saud University-Computer and Information Sciences, 34(8): 56025618.
5. KUSUMANINGRUM D, H LEE, S LOHUMI, C MO, MS KIM AND BK CHO (2018). Non destructive technique for determining the viability of soybean (Glycine max) seeds using FT NIR spectroscopy. Journal of the Science of Food and Agriculture, 98(5): 1734-1742.
6. BAEK I, D KUSUMANINGRUM, LM KANDPAL, S LOHUMI, C MO, MS KIM AND BK CHO (2019). Rapid measurement of soybean seed viability using Kernel-based multispectral image analysis. Sensors, 19(2): 271 (http://dx.doi.org/10.3390/s19020271).
7. DA SILVA ANDRE G, PC CORADI, LPR TEODORO AND PE TEODORO (2022). Predicting the quality of soybean seeds stored in different environments and packaging using machine learning. Scientific Reports, 12(1): 8793 (https://doi.org/ 10.1038/s41598-022-12863-5).
8. KHATRI A, S AGRAWAL AND JM CHATTERJEE (2022). Wheat seed classification: utilizing ensemble machine learning approach. Scientific Programming, 2022: Article ID 2626868, 9 pages (https://doi.org/10.1155/2022/ 2626868).
9. YANG L, Z ZHANG AND X HU. (2020). Cultivar discrimination of single alfalfa (Medicago sativa L.) seed via multispectral imaging combined with multivariate analysis. Sensors, 20(22): 6575 (http://dx.doi.org/10.3390/s20226575).
10. WANG X, H ZHANG, R SONG, X HE, P MAO AND S JIA (2021). Non-destructive identification of naturally aged alfalfa seeds via multispectral imaging analysis. Sensors, 21(17):5804 (https://doi.org/10.3390/s21175804).
11. ZHANG S, H ZENG, W JI, K YI, S YANG, P MAO, Z WANG, H YU AND M LI (2022). Non-destructive testing of alfalfa seed vigor based on multispectral imaging technology. Sensors, 22(7): 2760 (https://doi.org/10.3390/s22072760)
12. WILLIAMS PJ AND S KUCHERYAVSKIY (2016). Classification of maize kernels using NIR hyperspectral imaging. Food Chemistry, 209:131-138.
13. BIANCHINI VDJM, GM MASCARIN, LCAS SILVA, V ARTHUR, JM CARSTENSEN, B BOELT AND C BARBOZA DA SILVA (2021). Multispectral and X-ray images for characterization of Jatropha curcas L. seed quality. Plant Methods, 17(1): 9 (doi: 10.1186/s13007-021-00709-6. PMID: 33499879; PMCID:PMC7836195.).
14. AGELET, LE, DD ELLIS, S DUVICK, AS GOGGI, CR HURBURGH AND CA GARDNER (2012). Feasibility of near infrared spectroscopy for analyzing corn kernel damage and viability of soybean and corn kernels. Journal of Cereal Science, 55(2): 160-165.
15. AMBROSE A, LM KANDPAL, MS KIM, WH LEE AND BK CHO (2016a). High speed measurement of corn seed viability using hyperspectral imaging. Infrared Physics and Technology, 75: 173-179.
16. AMBROSE A, S LOHUMI, WH LEE AND BK CHO (2016b). Comparative nondestructive measurement of corn seed viability using Fourier transform near-infrared (FT-NIR) and Raman spectroscopy. Sensors and Actuators B: Chemical, 224: 500-506.
17. KANDPAL LM, S LOHUMI, MS KIM, JS KANG AND BK CHO (2016). Near-infrared hyperspectral imaging system coupled with multivariate methods to predict viability and vigor in muskmelon seeds. Sensors and Actuators B: Chemical, 229: 534-544.
18. OLESEN MH, P NIKNESHAN, S SHRESTHA, A TADAYYON, LC DELEURAN, B BOELT AND R GISLUM (2015). Viability prediction of Ricinus cummunis L. seeds using multispectral imaging. Sensors, 15(2): 4592-4604.
19. SHRESTHA S, LC DELEURAN AND R GISLUM (2017). Separation of viable and non-viable tomato (Solanum lycopersicum L.) seeds using single seed near-infrared spectroscopy. Computers and Electronics in Agriculture, 142: 348-355.
20. ELMASRY G, N MANDOUR, MH WAGNER, D DEMILLY, J VERDIER, E BELIN AND D ROUSSEAU (2019). Utilization of computer vision and multispectral imaging techniques for classification of cowpea (Vigna unguiculata) seeds. Plant methods, 15: 24 (https://doi.org/10.1186/s13007-019-0411-2).
21. GHAMARI S (2012). Classification of chickpea seeds using supervised and unsupervised artificial neural networks. African Journal of Agricultural Research, 7(21): 3193-3201.
22. LOHUMI S, C MO, JS KANG, SJ HONG AND BK CHO (2013). Nondestructive evaluation for the viability of watermelon (citrullus lanatus) seeds using fourier transform near infrared spectroscopy. Journal of Biosystems Engineering, 38(4): 312317.
