ORIGINAL ARTICLE
Artificial neural network potential in yield prediction of lentil (Lens culinaris L.) influenced by weed interference
| 1 | Department of Agronomy and Plant Breeding, Razi University, Kermanshah, Iran |
A - Research concept and design; B - Collection and/or assembly of data; C - Data analysis and interpretation; D - Writing the article; E - Critical revision of the article; F - Final approval of article
CORRESPONDING AUTHOR
Alireza Bagheri
Department of Agronomy and Plant Breeding, Razi university, Emam khomeini, 6715685421, Kermanshah, Iran
Department of Agronomy and Plant Breeding, Razi university, Emam khomeini, 6715685421, Kermanshah, Iran
Submission date: 2020-02-29
Acceptance date: 2020-05-20
Online publication date: 2020-08-12
Journal of Plant Protection Research 2020;60(3):284–295
KEYWORDS
TOPICS
ABSTRACT
This study was conducted to predict the yield and biomass of lentil (Lens culinaris L.) affected by weeds using artificial neural network and multiple regression models. Systematic sampling was done at 184 sampling points at the 8-leaf to early-flowering and at lentil maturity. The weed density and height as well as canopy cover of the weeds and lentil were measured in the first sampling stage. In addition, weed species richness, diversity and evenness were calculated. The measured variables in the first sampling stage were considered as predictive variables. In the second sampling stage, lentil yield and biomass dry weight were recorded at the same sampling points as the first sampling stage. The lentil yield and biomass were considered as dependent variables. The model input data included the total raw and standardized variables of the first sampling stage, as well as the raw and standardized variables with a significant relationship to the lentil yield and biomass extracted from stepwise regression and correlation methods. The results showed that neural network prediction accuracy was significantly more than multiple regression. The best network in predicting yield of lentil was the principal component analysis network (PCA), made from total standardized data, with a correlation coefficient of 80% and normalized root mean square error of 5.85%. These values in the best network (a PCA neural network made from standardized data with significant relationship to lentil biomass) were 79% and 11.36% for lentil biomass prediction, respectively. Our results generally showed that the neural network approach could be used effectively in lentil yield prediction under weed interference conditions.
CONFLICT OF INTEREST
The authors have declared that no conflict of interests exist.
REFERENCES (56)
1.
Adeux G., Vieren E., Carlesi S., Bàrberi P., Munier-Jolain N., Cordeau S. 2019. Mitigating crop yield losses through weed diversity. Nature Sustainability 2 (11): 1018–1026. DOI: https://doi.org/10.1038/s41893....
2.
Ali A., Streibig J.C., Andreasen C. 2003. Yield loss prediction models based on early estimation of weed pressure. Crop Protection 53: 125–131. DOI: http://dx.doi.org/10.1016/j.cr....
3.
Alvarez R. 2009. Predicting average regional yield and production of wheat in the Argentine Pampas by an artificial neural network approach. European Journal of Agronomy 30 (2): 70–77. DOI: https://doi.org/10.1016/j.eja.....
4.
Anysz H., Zbiciak A., Ibadov N. 2016. The influence of input data standardization method on prediction accuracy of artificial neural networks. Procedia Engineering 153: 66–70. DOI: https://doi.org/10.1016/j.proe....
5.
Archibald S. 2019. From the Ground Up: Herbaceous Community Diversity and Management in Coffee Agroforestry Systems. Master of Science. University of Toronto, Toronto, Canada, 78 pp.
6.
Bagheri A., Eghbali L., Sadrabadi Haghighi R. 2019. Seed classification of three species of amaranth (Amaranthus spp.) using artificial neural network and canonical discriminant analysis. The Journal of Agricultural Science 157 (4): 333–334. DOI: http://dx.doi.org/10.1017/S002....
7.
Bagheri A.R., Rashed Mohasel M.H., Rezvani Moghaddam P., Nasiri Mhalati M. 2014. Crop rotation effect on spatial dynamic of Fumaria vaillantti and Polygonum aviculare. Iranian Journal of Field Crops Research 12 (2): 178–188. (in Persian) DOI: https://doi.org/10.22067/gsc.v....
8.
Batchelor D.W., Yang B.X., Tschanz T.A. 1997. Development of a neural network for soybean rust epidemics. Transactions of the ASAE 40 (1): 247–252. DOI: https://doi.org/10.13031/2013.....
9.
Bazgeer S. 2005. Land Use Change Analysis in the Submountainous Region of Punjab Using Remote Sensing, GIS, and Agrometerological Parameters. Departement of Agricultural Meteorology, Punjab Agricultural University, Ludhiana, India, 128 pp.
10.
Blackshaw R.E., O’Donovan J.T., Harker K.N., Li X. 2002. Beyond herbicides: new approaches to managing weeds. Proceedings of the International Conference on Environmentally Sustainable Agriculture for Dry Areas: 305–312.
11.
Brim-DeForest W.B., Al-Khatib K., Fischer A.J. 2017. Predicting yield losses in rice mixed-weed species infestations in California. Weed Science 65 (1): 61–72. DOI: http://dx.doi.org/10.1614/WS-D....
12.
Camargo J. 1993. Must dominance increase with the number of subordinate species in competitive interactions? Journal of Theoretical Biology 161 (4): 537–542. DOI: http://dx.doi.org/10.1006/jtbi....
13.
Chang D.-H., Islam S. 2000. Estimation of soil physical properties using remote sensing and artificial neural network. Remote Sensing of Environment 74 (3): 534–544. DOI: http://dx.doi.org/10.1016/S003....
14.
Cierjacks A., Pommeranz M., Schulz K., Almeida-Cortez J. 2016. Is crop yield related to weed species diversity and biomass in coconut and banana fields of northeastern Brazil? Agriculture, Ecosystems & Environment 220 (17): 175–183. DOI: https://doi.org/10.1016/j.agee....
15.
Cilimkovic M. 2015. Neural Networks and Back Propagation Algorithm. Institute of Technology Blanchardstown, Blanchardstown Road North Dublin, Ireland, 12 pp.
16.
Cressman S.T., Page E.R., Swanton C.J. 2011. Weeds and the red to far-red ratio of reflected light: Characterizing the influence of herbicide selection, dose, and weed species. Weed Science 59 (3): 424–430. DOI: http://dx.doi.org/10.1614/WS-1....
17.
Drummond S.T., Sudduth K.A., Joshi A., Birrell S.J., Kitchen N.R. 2003. Statistical and neural methods for site-specific yield prediction. Transactions of the ASAE 46 (1): 5–14. DOI: http://dx.doi.org/10.13031/201....
18.
Elkoca E., Kantar F., Zengin H. 2004. Effects of chemical and agronomical weed control treatments on weed density, yield and yield parameters of lentil (Lens culinaris L. cv. Erzurum-89). Asian Journal of Plant Science 3 (2): 187–192. DOI: https://doi.org/10.3923/ajps.2....
19.
Erman M., Tepe I., Bukun B., Yergin R., Taskesen M. 2008. Critical period of weed control in winter lentil under non-irrigated conditions in Turkey. African Journal of Agricultural Research 3 (08): 523–530. DOI: https://doi.org/10.3906/tar-10....
20.
Erman M., Tepe I., Yazlik A., Levent R., Ipek K. 2004. Effect of weed control treatments on weeds, seed yield, yield components and nodulation in winter lentil. Weed Research 44 (4): 305–312. DOI: https://doi.org/10.1111/j.1365....
21.
FAO. 2017. FAOSTAT. Available on: http://www.fao.org/faostat/en/... [Accessed: 23 September, 2019].
22.
Ferrero R., Lima M., Davis A.S., Gonzalez-Andujar J.L. 2017. Weed diversity affects soybean and maize yield in a long term experiment in Michigan, USA. Frontiers in Plant Science 8: 236. DOI: https://doi.org/10.3389/fpls.2....
23.
Gnanavel I., Natarajan S. 2014. Eco-friendly weed control options for sustainable agriculture – a review. Agricultural Reviews 35 (3): 172–183. DOI: https://doi.org/10.5958/0976-0....
24.
Haykin S., Lippmann R. 1994. Neural networks, a comprehensive foundation. International Journal of Neural Systems 5 (3): 363–364. DOI: https://doi.org/10.1142/S01290....
25.
Hooper D.U., Chapin F.S., Ewel J.J., Hector A., Inchausti P., Lavorel S., Lawton J.H., Lodge D.M., Loreau M., Naeem S., Schmid B., Setälä H., Symstad A.J., Vandermeer J., Wardle D.A. 2005. Effects of biodiversity on ecosystem functioning: a consensus of current knowledge. Ecological Monographs 75 (1): 3–35. DOI: https://doi.org/10.1890/04-092....
26.
Irmak A., Jones J., Batchelor W., Irmak S., Boote K., Paz J. 2006. Artificial neural network model as a data analysis tool in precision farming. Transactions of the American Society of Agricultural and Biological Engineers 49 (6): 2027–2037. DOI: https://doi.org/10.13031/2013.....
27.
Jin Y.Q., Liu C. 1997. Biomass retrieval from high-dimensional active/passive remote sensing data by using artificial neural networks. International Journal of Remote Sensing 18 (4): 971–979. DOI: https://doi.org/10.1080/014311....
28.
Joergensen S.E., Bendoricchio G. 2001. Fundamentals of Ecological Modelling. Elsevier, Oxford, UK, 530 pp.
29.
Jost L. 2010. The relation between evenness and diversity. Diversity 2 (2): 207–232. DOI: https://doi.org/10.3390/d20202....
30.
Karimmojeni H., Yousefi A.R., Kudsk P., Bazrafshan A.H. 2015. Broadleaf weed control in winter-sown lentil (Lens culinaris). Weed Technology 29 (1): 56–62. DOI: https://doi.org/10.1614/wt-d-1....
31.
Kaul M., Hill R.L., Walthall C. 2005. Artificial neural networks for corn and soybean yield prediction. Agricultural Systems 85 (1): 1–18. DOI: https://doi.org/10.1016/j.agsy....
32.
Khaki S., Khalilzadeh Z., Wang L. 2020a. Predicting yield performance of parents in plant breeding: a neural collaborative filtering approach. PLoS ONE 15: e0233382. DOI: https://doi.org/10.1371/journa....
33.
Khaki S., Wang L., Archontoulis S.V. 2020b. A cnn-rnn framework for crop yield prediction. Frontiers in Plant Science 10: 1750. DOI: https://doi.org/10.3389/fpls.2....
34.
Knott C.M., Halila H.M. 1988. Weeds in food legumes: problems, effects and control. p. 535–548. In: “World Crops: Cool Season Food Legumes: A Global Perspective of the Problems and Prospects for Crop Improvement in Pea, Lentil, Faba Bean and Chickpea” (R.J. Summerfield, ed.). Springer Netherlands, Dordrecht, The Netherlands.
35.
Liu J., Goering C., Tian L. 2001. A neural network for setting target corn yields. Transactions of the American Society of Agricultural and Biological Engineers 44 (3): 705–713. DOI: https://doi.org/10.13031/2013.....
36.
Menhaj M.B. 2005. Principles of Artificial Neural Network. Amir Kabir University, Tehran, Iran, 716 pp. (in Persian).
37.
Mohamed E.S., Nourai A.H., Mohamed G.E., Mohamed M.I., Saxena M.C. 1997. Weeds and weed management irrigated lentil in northern Sudan. Weed Research 37 (4): 211–218. DOI: https://doi.org/10.1046/j.1365....
38.
Movahedian M., Hosseini S.E., Ghorbanzadeh M. 1386. Estimation of leaf area using neural networks. Third Conference on Information and Knowledge Technology, November 27–29, 2007, Ferdowsi University of Mashhad, Mashhad, Iran, 819 pp. (in Persian).
39.
Niazian M., Sadat-Noori S.A., Abdipour M. 2018. Modeling the seed yield of ajowan (Trachyspermum ammi L.) using artificial neural network and multiple linear regression models. Industrial Crops and Products 117: 224–234. DOI: https://doi.org/10.1016/j.indc....
40.
Niedbała G., Piekutowska M., Weres J., Korzeniewicz R., Witaszek K., Adamski M., Pilarski K., Czechowska-Kosacka A., Krysztofiak-Kaniewska A. 2019. Application of artificial neural networks for yield modeling of winter rapeseed based on combined quantitative and qualitative data. Agronomy 9 (12): 781. DOI: https://doi.org/10.3390/agrono....
41.
Ozesmi S.L., Tan C.O., Ozesmi U. 2006. Methodological issues in building, training, and testing artificial neural networks in ecological applications. Ecological Modelling 195 (1): 83–93. DOI: https://doi.org/10.1016/j.ecol....
42.
Rahmani E., Liaghat A., Khalili A. 2008. Estimating barley yield in eastern Azerbaijan using drought indices and climatic parameters by artificial neural network (ANN). Iranian Journal of Soil and Water Research 39 (1): 47–56. (in Persian).
43.
Ramchoun H., Idrissi M.A.J., Ghanou Y., Ettaouil M. 2017. New modeling of multilayer perceptron architecture optimization with regularization: an application to pattern classification. IAENG International Journal of Computer Science 44 (3): 261–269.
44.
Sajadi S.J., Sabouri H. 2014. Application of artificial neural networks in canola crop yield prediction. Journal of Crop Production and Processing 3 (10): 157–164. (in Persian).
45.
Sarker A., Erskine W. 2006. Recent progress in the ancient lentil. The Journal of Agricultural Science 144 (1): 19–29. DOI: https://doi.org/10.1017/S00218....
46.
Seyed Jalali S.A.R., Sarmadian F., Shorafa M., Mohammad Esmaiel Z. 2016. Application of kriging and cokriging in predicting wheat yield using principle component analysis. Electronic Journal of Crop Production 9 (2): 213–224. (in Persian) DOI: https://dx.doi.org/10.22069/ej....
47.
Shannon C.E., Weaver W. 1964. The Mathematical Theory of Communication. University of Illinois Press, USA, 125 pp.
48.
Shirdeli A., Tavassoli A. 2015. Predicting yield and water use efficiency in saffron using models of artificial neural network based on climate factors and water. Saffron Agronomy and Technology 3 (2): 121–131. (in Persian) DOI: https://dx.doi.org/10.22048/js....
49.
Simpson E.H. 1949. Measurement of diversity. Nature 163 (4148): 688. DOI: https://dx.doi.org/10.1038/163....
50.
Smith B., Wilson J.B. 1996. A consumer’s guide to evenness indices. Oikos 76 (1): 70–82. DOI: https://doi.org/doi:10.2307/35....
51.
Song J.-S., Kim J.-W., Im J.-H., Lee K.-J., Lee B.-W., Kim D.-S. 2017. The Effects of single- and multiple-weed interference on soybean yield in the far-eastern region of Russia. Weed Science 65 (3): 371–380. DOI: https://dx.doi.org/10.1017/wsc....
52.
Storkey J., Neve P. 2018. What good is weed diversity? Weed Research 58 (4): 239–243. DOI: https://doi.org/10.1111/wre.12....
53.
Sun J., Di L., Sun Z., Shen Y., Lai Z. 2019. County-level soybean yield prediction using deep CNN-LSTM model. Sensors 19 (20): 4363. DOI: https://doi.org/10.3390/s19204....
54.
Torrecilla J.S., Otero L., Sanz P.D. 2004. A neural network approach for thermal/pressure food processing. Journal of Food Engineering 62 (1): 89–95. DOI: http://dx.doi.org/10.1016/S026....
55.
Yenish J.P., Brand J., Pala M., Haddad A. 2009. Weed management in lentil. p. 326–342. In: “The Lentil: Botany, Production and Uses” (W. Erskine, F.J. Muehlbauer, A. Sarker, B. Sharma, eds.). CABI, Wallingford, UK.
56.
Zhang Y., Pulliainen J., Koponen S., Hallikainen M. 2002. Application of an empirical neural network to surface water quality estimation in the Gulf of Finland using combined optical data and microwave data. Remote Sensing of Environment 81 (2–3): 327–336. DOI: http://dx.doi.org/10.1016/S003....
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