Prediction of the Hemp Yield Using Artificial Intelligence Methods

References

• Aghajani, N., M. Kashaninejad, A. A. Dehghani, and A. Daraei Garmakhany. 2012. Comparison between artificial neural networks and mathematical models for moisture ratio estimation in two varieties of green malt. Quality Assurance and Safety of Crops & Foods 4 (2):93–101. doi:10.1111/j.1757-837X.2012.00125.x.
   Web of Science ®Google Scholar
• Alvarez, R., and H. S. Steinbach. 2011. Modeling apparent nitrogen mineralization under field conditions using regressions and artificial neural networks. Agronomy Journal 103 (4):1159–68. doi:10.2134/agronj2010.0254.
   Web of Science ®Google Scholar
• Apostol, L., M. Popa, and G. Mustatea. 2015. Cannabis sativa L. partially skimmed flour as source of bio-compounds in the bakery industry. Romanian Biotechnological Letters 20:10835–44.
   Web of Science ®Google Scholar
• Boniecki, P. 2008. Elementy modelowania neuronowego w rolnictwie. Poznań: Wydawnictwo Uniwersytetu Przyrodniczego.
   Google Scholar
• Burczyk, H., and J. Frankowski. 2018. Henola – Pierwsza polska odmiana konopi oleistych. Zagadnienia Doradztwa Rolniczego 93:89–101.
   Google Scholar
• Cassano, R., S. Trombino, F. P. Nicoletta, M. V. Mauro, N. Picci, N. Picci, and N. Picci. 2013. Hemp fiber (Cannabis sativa L.) derivatives with antibacterial and chelating properties. Cellulose 20 (1):547–57. doi:10.1007/s10570-012-9804-3.
   Web of Science ®Google Scholar
• Citterio, S., A. Santagostino, P. Fumagalli, N. Prato, P. Ranalli, and S. Sgorbati. 2003. Heavy metal tolerance and accumulation of Cd, Cr and Ni by Cannabis sativa L. Plant and Soil 256 (2):243–52. doi:10.1023/A:1026113905129.
   Web of Science ®Google Scholar
• Clement, J., N. Novas, J. Gazquez, and F. Manzano-Agugliaro. 2012. High speed intelligent classifier of tomatoes by colour, size and weight. Spanish Journal of Agricultural Research 10 (2):314–25. doi:10.5424/sjar/2012102-368-11.
   Web of Science ®Google Scholar
• Demir, B., I. Eski, Z. A. Kus, and S. Ercislki. 2017. Prediction of physical parameters of pumpkin seeds using neural network. Notulae Botanicae Horti Agrobotanici Cluj-Napoca 45 (1):22–27. doi:10.15835/nbha45110429.
   Web of Science ®Google Scholar
• Emamgholizadeh, S., M. Parsaeian, and M. Baradaran. 2015. Seed yield prediction of sesame using artificial neural network. European Journal of Agronomy 68:89–96. doi:10.1016/j.eja.2015.04.010.
   Web of Science ®Google Scholar
• Eriksson, M., and H. Wall. 2012. Hemp seed cake in organic broiler diets. Animal Feed Science and Technology 171 (2–4):205–13. doi:10.1016/j.anifeedsci.2011.10.007.
   Web of Science ®Google Scholar
• Francik, S., Z. Ślipek, J. Frączek, and A. Knapczyk. 2016. Present trends in research on application of artificial neural networks in agricultural engineering. Agricultural Engineering 20 (4):15–25. doi:10.1515/agriceng-2016-0060.
   Google Scholar
• Frankowski, J., A. Wawro, J. Batog, and H. Burczyk. 2021. New polish oilseed hemp cultivar Henola – Cultivation, properties and utilization for bioethanol production. Journal of Natural Fibers published online July 07, 2021: 1–13. doi: 10.1080/15440478.2021.1944439.
   Web of Science ®Google Scholar
• Girgih, A. T., R. He, S. Malomo, M. Offengenden, J. Wu, and R. E. Aluko. 2014. Structural and functional characterization of hemp seed (Cannabis sativa L.) protein-derived antioxidant and antihypertensive peptides. Journal of Functional Foods 6:384–94.
   Web of Science ®Google Scholar
• Gu, X., S. Neubert, N. Stoll, and K. Thurow. 2018. A new method for the indicator of dynamic scheduling in life science laboratories using artificial neural networks. 2018 IEEE International Instrumentation and Measurement Technology Conference (Houston, TX, USA).
   Google Scholar
• Hakamy, A., F. U. A. Shaikh, and I. M. Lowa. 2014. Characteristics of hemp fabric reinforced nanoclay – Cement nanocomposites. Cement and Concrete Composites 50:27–35. doi:10.1016/j.cemconcomp.2014.03.002.
   Web of Science ®Google Scholar
• Herer, J., and M. Bröckers. 2014. Die Wiederentdeckung der Nutzpflanze Hanf. Solura: Nachtschatten Verlag ss. 526 .
   Google Scholar
• Iraji, M., and A. Tosinia. 2011. Classification tomatoes on machine vision with fuzzy the mamdani inference, adaptive neuro fuzzy inference system based (Anfis-Sugeno. Australian Journal of Basic and Applied Sciences 5 (11):846–53.
   Google Scholar
• Jarabo, R., E. Fuentea, M. C. Montea, H. Savastano Jr, P. Mutjé, and C. Negro. 2012. Use of cellulose fibers from hemp core in fiber-cement production. Effect on flocculation, retention, drainage and product properties. Industrial Crops and Products 39:89–96. doi:10.1016/j.indcrop.2012.02.017.
   Web of Science ®Google Scholar
• Khan, B. A., P. Warner, and H. Wang. 2014. Antibacterial properties of hemp and other natural fibre plants: A review. BioResources 9 (2):3642–59. doi:10.15376/biores.9.2.3642-3659.
   Web of Science ®Google Scholar
• Kraszkiewicz, A., M. Kachel, S. Parafiniuk, G. Zając, I. Niedziółka, and M. Sprawka. 2019. Assessment of the possibility of using hemp biomass (Cannabis Sativa L.) for energy purposes: A case study. Applied Sciences 20 (20):4437–49. doi:10.3390/app9204437.
   Google Scholar
• Kucukonder, H., S. Boyaci, and A. Akyuz. 2016. A modeling study with an artificial neural network: Developing estimation models for the tomato plant leaf area. Turkish Journal of Agriculture and Forestry 40 (2):203–12. doi:10.3906/tar-1408-28.
   Web of Science ®Google Scholar
• Kusz, A., A. Marciniak, and J. Skwarcz. 2015. Implementation of computation process in a Bayesian network on the example of unit operating costs determination. Eksploatacja I Niezawodnosc [Maintenance and Reliability] 17 (2):266–72. doi:10.17531/ein.2015.2.14.
   Web of Science ®Google Scholar
• Łapczyńska-Kordon, B., S. Francik, and Z. Ślipek. 2008. Model neuronowy zmian temperatury podczas konwekcyjnego suszenia zrębków wierzby energetycznej. Inzynieria Rolnicza 11 (109):149–55.
   Google Scholar
• Laurindo, B. S., R. D. F. Aurindo, A. M. Azevedo, F. T. Delazari, J. C. Zanuncio, and D. J. Henriques da Silva. 2017. Optimization of the number of evaluations for early blight disease in tomato accessions using artificial neural networks. Scientia Horticulturae 218:171–176, 0304–4238. doi:10.1016/j.scienta.2017.02.005.
   Web of Science ®Google Scholar
• Leizer, C., D. Ribnicky, A. Poulev, S. Dushenkov, and I. Raskin. 2000. The composition of hemp seed oil and its potential as an important source of nutrition. Journal of Nutraceuticals, Functional & Medical Foods 4 (4):35–53. doi:10.1300/J133v02n04_04.
   Google Scholar
• Łochyńska, M., and J. Frankowski. 2019. Impact of silkworm excrement organic fertilizer on hemp biomass yield and composition. Journal of Ecological Engineering 20 (10):63–71. doi:10.12911/22998993/112858.
   Web of Science ®Google Scholar
• Łochyńska, M., and J. Frankowski. 2021. The effects of silkworm excrement organic fertilizer on the hemp yield. Journal of Natural Fibers 19 (3):847–57. doi:10.1080/15440478.2021.1921665.
   Web of Science ®Google Scholar
• Łuczycka, D., and K. Pentoś. 2010. Zastosowanie sztucznych sieci neuronowych do opisu przenikalności elektrycznej mąki. Inzynieria Rolnicza 2 (120):43–47.
   Google Scholar
• Mańkowski, J., A. Kubacki, J. Kołodziej, M. Mackiewicz-Talarczyk, P. Baraniecki, and I. Pniewska. 2016. Hemp fibre from crops grown on reclaimed land for the production of sanitary mats. In Natural fibres: Advances in science and technology towards industrial applications. from science to market, ed. R. Fangueiro and S. Rana, 371–77. Dordrecht: RILEM Bookseries Springer.
   Google Scholar
• Martelo-Vidal, M. J., and M. Vázquez. 2015. Application of artificial neural networks coupled to UV–VIS–NIR spectroscopy for the rapid quantification of wine compounds in aqueous mixtures. CyTA - Journal of Food 13 (1):32–39. doi:10.1080/19476337.2014.908955.
   Web of Science ®Google Scholar
• Mutlu, A. C., I. H. Boyaci, H. E. Genis, R. Ozturk, N. Basaran-Akgul, T. Sanal, and A. K. Evlice. 2011. Prediction of wheat quality parameters using near-infrared spectroscopy and artificial neural networks. European Food Research and Technology 233 (2):267–74. doi:10.1007/s00217-011-1515-8.
   Web of Science ®Google Scholar
• Niu, P., B. Liu, X. Wei, X. Wang, and J. Jang. 2011. Study on mechanical properties and thermal stability of polypropylene/hemp fiber composites. Journal of Reinforced Plastics and Composites 30 (1):36–44. doi:10.1177/0731684410383067.
   Web of Science ®Google Scholar
• Oomaha, B. D., M. Busson, D. V. Godfrey, and J. C. G. Drover. 2002. Characteristics of hemp (Cannabis sativa L.) seed oil. Food Chemistry 76 (1):33–43. doi:10.1016/S0308-8146(01)00245-X.
   Web of Science ®Google Scholar
• Przybylak, A., P. Boniecki, K. Koszela, A. Ludwiczak, M. Zaborowicz, D. Lisiak, M. Stanisz, and P. Ślósarz. 2016. Estimation of intramuscular level of marbling among whiteheaded mutton sheep lambs. Journal of Food Engineering 168:199–204. doi:10.1016/j.jfoodeng.2015.07.035.
   Web of Science ®Google Scholar
• Raba, B., K. Nowakowski, A. Lewicki, K. Przybył, M. Zaborowicz, K. Koszela, P. Boniecki, and W. Mueller. 2014. The non-touching method of the malting barley quality evaluation. In Sixth International Conference on Digital Image Processing (ICDIP 2014), Proc. SPIE 9159: 91590H (Athens, Greece) (April 16, 2014), 0277786X, 0277786X, doi:10.1117/12.2064091.
   Google Scholar
• Rafiq, A., H. A. Makroo, and M. K. Hazarika. 2016. Artificial neural network-based image analysis for evaluation of quality attributes of agricultural produce. Journal of Food Processing and Preservation 40 (5):1010–19. doi:10.1111/jfpp.12681.
   Web of Science ®Google Scholar
• Rouison, D., M. Sain, and M. Couturier. 2006. Resin transfer molding of hemp fiber composites: Optimization of the process and mechanical properties of the materials. Composites Science and Technology 66 (7–8):895–906. doi:10.1016/j.compscitech.2005.07.040.
   Web of Science ®Google Scholar
• Singh, K. R., and S. Chaudhury. 2016. Efficient technique for rice grain classification using back-propagation neural network and wavelet decomposition. IET Computer Vision 10 (8):780. doi:10.1049/iet-cvi.2015.0486.
   Web of Science ®Google Scholar
• Szwedziak, K. 2009. Innowacyjna metoda rozpoznawania wybranych cech jakościowych nasion z wykorzystaniem analizy obrazu i sztucznych sieci neuronowych (SSN). Inzynieria Rolnicza 4(113 pp. 7–52). XII
   Google Scholar
• Zaborowicz, M., P. Boniecki, K. Koszela, J. Przybył, R. Mazur, S. Kujawa, and K. Pilarski. 2013. Use of artificial neural networks in the identification and classification of tomatoes. In Fifth International Conference on Digital Image Processing ( ICDIP 2013). Beijing, China: International Society for Optics and Photonics 8878: 88782R
   Google Scholar
• Zaborowicz, M., P. Boniecki, K. Koszela, A. Przybylak, and J. Przybył. 2017. Application of neural image analysis in evaluating the quality of greenhouse tomatoes. Scientia Horticulturae 218:222–29. doi:10.1016/j.scienta.2017.02.001.
   Web of Science ®Google Scholar
• Zimniewska, M., and J. Batog. 2012. Ultraviolet-blocking properties of natural fibres. In Handbook of natural fibres, ed. R. M. Kozlowski, Vol. II, 1 ed., 141–67. Sawston: Woodhead Publishing.
   Google Scholar