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Estimation of kiwifruit yield by leaf nutrients concentration and artificial neural network

Published online by Cambridge University Press:  08 June 2020

Ali Mohammadi Torkashvand ,
Afsoon Ahmadipour  and
Amin Mousavi Khaneghah Open the ORCID record for Amin Mousavi Khaneghah [Opens in a new window]
Ali Mohammadi Torkashvand*
Affiliation:
Department of Soil Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
Afsoon Ahmadipour
Affiliation:
Department of Soil Science, Science and Research Branch, Islamic Azad University, Tehran, Iran
Amin Mousavi Khaneghah
Affiliation:
Department of Food Science, Faculty of Food Engineering, State University of Campinas (UNICAMP), Rua Monteiro Lobato, 80. Caixa Postal: 6121, CEP: 13083-862 Campinas, São Paulo, Brazil
*
Author for correspondence: Ali Mohammadi Torkashvand, E-mail: m.torkashvand54@yahoo.com; Amin Mousavi Khaneghah, E-mail: mousavi@unicamp.br
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Abstract

There is a fundamental concern regarding the prediction of kiwifruit yield based on the concentration of nutrients in the leaf (2–3 months before fruits harvesting). For this purpose, the current study was designed to employ an artificial neural network (ANN) to evaluate the kiwi yield of Hayward cultivar. In this regard, 31 kiwi orchards (6–7 years old) in different parts of Rudsar, Guilan Province, Iran, with 101 plots (three trees in every plot) were selected. The complete leaves of branches with fruits were harvested, and the concentration of nitrogen, potassium, calcium, and magnesium measured. After fruit harvesting in late November, the fruit yield of each plot was evaluated along with the fresh and dry weights of the fruit. The ANN analyses were carried out using a multi-layer perceptron with the Langburge-Marquardt training algorithm. Using calcium (Ca) as input data (Ca-model) was more accurate than using nitrogen (N-model). The maximum R2 and the lowest root mean square error was obtained when all nutrients and related ratios were considered as input variables. Since the difference between the proposed model and the model fitted by the calcium variable (Ca-model) was only about 6%, the Ca-model is recommended.

Keywords

Fruitmodellingmultilayer perceptronnutrientsyield

Information

Type
Crops and Soils Research Paper
Information
The Journal of Agricultural Science , Volume 158 , Issue 3 , April 2020 , pp. 185 - 193
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Copyright © The Author(s), 2020. Published by Cambridge University Press

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References

Aitkenhead, MJ, Donnelly, D, Sutherland, L, Miller, DG, Coull, MC and Black, HIJ (2015) Predicting Scottish topsoil organic matter content from colour and environmental factors. European Journal of Soil Science 66, 112–120.CrossRefGoogle Scholar
Amerian, M, Ali-Mohamadian, L and Malekhosini, A (2018) Evaluation the reasons of inattention and ignorance farmers of adverse effects the chemical fertilizers the using focus group discussion. Journal of Environmental Science and Technology 19, 36–47, (In Persian).Google Scholar
Ashoorzadeh, H, Torkashvand, AM and Khomami, AM (2016) Choose a planting substrate and fertilization method to achieve optimal growth of Araucaria excelsa. Journal of Ornamental Plants 6, 201–215.Google Scholar
Ashouri Vajari, M, Ghasemnezhad, M, Sabouri, A and Ebrahimi, R (2015) Correlation between content and ratio of fruits’ mineral elements at harvest and postharvest life of kiwifruit Cv. Hayward in orchards of eastern part of Guilan province. Journal of Crop Production and Processing 4, 87–101.Google Scholar
Awasthi, RP, Bhutani, VP, Sharma, JC and Kaith, NS (1998) Mineral nutrient status of apple orchards of Shimla district of Himachal Pradesh. Indian Journal of Horticulture 55, 314–322.Google Scholar
Ayoubi, S, Shahri, AP, Karchegani, PM and Sahrawat, KL (2011) Application of artificial neural network (ANN) to predict soil organic matter using remote sensing data in two ecosystems. In Atazadeh, A (ed.), Biomass and Remote Sensing of Biomass. London, UK: InTech Open Access, pp. 181–196. DOI: 10.5772/18956.Google Scholar
Bannayan, M and Crout, NMJ (1999) A stochastic modelling approach for real-time forecasting of winter wheat yield. Field Crops Research 62, 85–95.CrossRefGoogle Scholar
Barker, AV and Pilbeam, DJ (2007) Handbook of Plant Nutrition. Boca Raton, FL, USA: CRC Press.Google Scholar
Bartoszek, K (2014) Usefulness of MODIS data for assessment of the growth and development of winter oilseed rape. Zemdirbyste-Agriculture 101, 445–452.CrossRefGoogle Scholar
Besalatpour, AA, Ayoubi, S, Hajabbasi, MA, Mosaddeghi, MR and Schulin, R (2013) Estimating wet soil aggregate stability from easily available properties in a highly mountainous watershed. Catena 111, 72–79.CrossRefGoogle Scholar
Bhargava, BS and Chadha, KL (1993) Leaf nutrient guide for fruit crops. In Chadha, KL and Pareek, OP (eds), Advances in Horticulture 2. New Delhi, India: Malhorta Publishing House, pp. 973–979.Google Scholar
Bocco, M, Willington, E and Arias, M (2010) Comparison of regression and neural networks models to estimate solar radiation. Chilean Journal of Agricultural Research 70, 428–435.10.4067/S0718-58392010000300010CrossRefGoogle Scholar
Carvajal, M, Martinez, V and Cerda, A (1999) Influence of magnesium and salinity on tomato plants grown in hydroponic culture. Journal of Plant Nutrition 22, 177–190.CrossRefGoogle Scholar
Chardonnet, CO, Charron, CS, Sams, CE and Conway, WS (2003) Chemical changes in the cortical tissue and cell walls of calcium-infiltrated ‘Golden Delicious’ apples during storage. Postharvest Biology and Technology 28, 97–111.CrossRefGoogle Scholar
Chia, KS, Abdul Rahim, H and Abdul Rahim, R (2012) Prediction of soluble solids content of pineapple via non-invasive low cost visible and shortwave near infrared spectroscopy and artificial neural network. Biosystems Engineering 113, 158–165.CrossRefGoogle Scholar
Clark, CJ and Smith, GS (1988) Seasonal accumulation of mineral nutrients by kiwifruit. New Physiologist 108, 399–409.CrossRefGoogle Scholar
Crisosto, HC and Kader, AA (1999) Kiwifruit: Postharvest Quality Maintenance Guidelines. Davis, CA, USA: Department of Pomology, University of California.Google Scholar
Dai, F, Zhou, Q, Lv, Z, Wang, X and Liu, G (2014) Spatial prediction of soil organic matter content integrating artificial neural network and ordinary kriging in Tibetan Plateau. Ecological Indicators 45, 184–194.CrossRefGoogle Scholar
Dar, MA, Wani, JA, Raina, SK, Bhat, MY and Malik, MA (2015) Relationship of leaf nutrient content with fruit yield and quality of pear. Journal of Environmental Biology 36, 649–653.Google Scholar
De Freitas, ST and Mitcham, EJ (2012) Factors involved in fruit calcium deficiency disorders. In Janick, J (ed.), Horticultural Reviews 40. Hoboken, NJ, USA: John Wiley & Sons, Inc., pp. 107–146.CrossRefGoogle Scholar
Dias, HB and Sentelhas, PC (2017) Evaluation of three sugarcane simulation models and their ensemble for yield estimation in commercially managed fields. Field Crops Research 213, 174–185.CrossRefGoogle Scholar
Domínguez, JA, Kumhálová, J and Novák, P (2015) Winter oilseed rape and winter wheat growth prediction using remote sensing methods. Plant. Soil and Environment 61, 410–416.Google Scholar
Dumenil, L (1961) Nitrogen and phosphorus composition of corn leaves and corn yields in relation to critical levels and nutrient balance. Soil Science Society of America Journal 25, 295–298.CrossRefGoogle Scholar
Egilla, JN, Davies, FT and Boutton, TW (2005) Drought stress influences leaf water content, photosynthesis, and water-use efficiency of Hibiscus Rosa-sinensis at three potassium concentrations. Photosynthetica 43, 135–140.CrossRefGoogle Scholar
Emamgholizadeh, S, Parsaeian, M and Baradaran, M (2015) Seed yield prediction of sesame using artificial neural network. European Journal of Agronomy 68, 89–96.10.1016/j.eja.2015.04.010CrossRefGoogle Scholar
Emami, AS (1996) Methods of Plant Analysis, Volume II. Technical Journal No. 982. Karaj, Iran: Soil and Water Research Institute (In Persian).Google Scholar
Eslami, M, Shadfar, S, Mohammadi Torkashvand, A and Pazira, E (2019) Assessment of density area and LNRF models in landslide hazard zonation (case study: Alamout watershed, Qazvin Province, Iran). Acta Ecologica Sinica 39, 173–180.CrossRefGoogle Scholar
Fageria, VD (2001) Nutrient interactions in crop plants. Journal of Plant Nutrition 24, 1269–1290.CrossRefGoogle Scholar
Fageria, NK, Barbosa Filho, MP, Moreira, A and Guimarães, CM (2009) Foliar fertilization of crop plants. Journal of Plant Nutrition 23, 1044–1064.10.1080/01904160902872826CrossRefGoogle Scholar
Farjam, A, Omid, M, Akram, A and Fazel Niari, Z (2014) A neural network based modelling and sensitivity analysis of energy inputs for predicting seed and grain corn yields. Journal of Agricultural Science and Technology 16, 767–778.Google Scholar
Farkas, I, Reményi, P and Biró, A (2000) Modelling aspects of grain drying with a neural network. Computer and Electronics in Agriculture 29, 99–113.10.1016/S0168-1699(00)00138-1CrossRefGoogle Scholar
Ferguson, IB, Thorp, TG, Brnett, AM, Boyd, LM and Triggs, CM (2003) Inorganic nutrient concentrations and physiological pitting in ‘Hayward’ kiwifruit. The Journal of Horticultural Science and Biotechnology 78, 497–504.CrossRefGoogle Scholar
Francis, C (1989) The recent excitement about neural networks. Nature 337, 129–132.Google Scholar
Gago, J, Martínez-Núñez, L, Landín, M and Gallego, PP (2010) Artificial neural networks as an alternative to the traditional statistical methodology in plant research. Journal of Plant Physiology 167, 23–27.CrossRefGoogle ScholarPubMed
Gee, S, Zhu, Z, Peng, L, Chen, Q and Jiang, Y (2018) Soil nutrient status and leaf Nutrient diagnosis in the main apple producing regions in China. Horticultural Plant Journal 4, 89–93.CrossRefGoogle Scholar
Golmohammadi, M, Rashtari, M and Pile Froush, M (2011) Study of nutritional status of olive gardens and the effect of fertilizer management on some quantitative and qualitative characteristics of fruit and its yield. In 7th Iranian Horticultural Science Congress. September 14–17. Isfahan, Iran: Iranian Society for Horticultural Science, pp. 1705–1707.Google Scholar
Goos, RG (1995) A laboratory exercise to demonstrate nitrogen mineralization and immobilization. Journal of Natural Resources and Life Sciences Education 24, 68–70.CrossRefGoogle Scholar
Halavatau, SM, O'Sullivan, JN, Asher, CJ and Blamey, FPC (1998) Better nutrition improves sweet potato and Taro yields in the south Pacific. Tropical Agriculture (Trinidad) 75, 7–12.Google Scholar
Hargreaves, JC, Adl, MS and Warman, PR (2008) A review of the use of composted municipal solid waste in agriculture. Agriculture, Ecosystems and Environment 123, 1–14.10.1016/j.agee.2007.07.004CrossRefGoogle Scholar
Hernandez-Munoz, P, Almenar, E, Ocio, MJ and Gavara, R (2006) Effect of calcium dips and chitosan coatings on postharvest life of strawberries (Fragaria Xananassa). Postharvest Biology and Technology 39, 247–253.CrossRefGoogle Scholar
Hertz, J, Palmer Richard, G and Krogh, AS (1991) Introduction to the Theory of Neural Computation. Boston, MA: Addison-Wesley.Google Scholar
Holdaway-Clarke, TL, Weddle, NM, Kim, S, Robi, A, Parris, C, Kunkel, JG and Hepler, PK (2003) Effect of extracellular calcium, pH and borate on growth oscillations in Lilium Formasanum pollen tubes. Journal of Experimental Botany 54, 65–72.CrossRefGoogle ScholarPubMed
Honarkarian, F and Mohammadi Torkashvand, A (2018) Effect of different calcium chloride application methods and macro elements fertilizers (nitrogen, phosphorus and potassium) on fruit quality and postharvest life of Hayward kiwi fruit. Plant Production Technology 10, 189–199, In Persian with English abstract.Google Scholar
Huang, H and Ferguson, AR (2003) Kiwifruit (Actinidia chinesis and A. deliciosa) plantings and production in China, 2002. New Zealand Journal of Crop and Horticultural Science 31, 197–202.CrossRefGoogle Scholar
Hushmandan Moghaddam Fard, Z and Shams, AS (2016) Effective factors on wheat farmers' attitude in Khodabandeh Province toward organic agriculture. Journal of Agricultural Knowledge and Sustainable Production 26, 155–170, (In Persian).Google Scholar
Ivanyi, I (2011) Relationship between leaf nutrient concentration and the yield of fibre hemp (Canabis sativa L. Research Journal of Agricultural Science 43, 70–76.Google Scholar
Khazaee Poul, Y (2003) Biology of Flowering and Pollination in Kiwifruits. Karaj, Iran: Agricultural Training Publishing Publishers (In Persian).Google Scholar
Khoshnood, Z and Mohammadi Torkashvand, A (2016) Relationship between kiwifruit yield and nutrients concentration in leaves and fruits. 2d National conference of Biology and Horticulture, 22 February 2016, Tehran, Iran.Google Scholar
Kumar, DN, Raju, KS and Sathish, T (2004) River flow forecasting using recurrent neural networks. Water Resources Management 18, 143–161.CrossRefGoogle Scholar
Lahiji, AA, Torkashvand, AM, Mehnatkesh, A and Navidi, M (2018) Status of macro and micro nutrients of olive orchard in northern Iran. Asian Journal of Water, Environment and Pollution 15, 143–148.CrossRefGoogle Scholar
Lee, CH, Kim, SB, Kang, S, Ko, JH, Kim, CS and Han, DH (2001) Changes in cell wall metabolism of kiwifruits during low temperature storage by postharvest calcium application. Journal of the Korean Society for Horticultural Science 42, 91–94.Google Scholar
Malakouti, MJ, MC Karimian, N and Keshavarz, P (2008) Determination Methods for Nutritional Deficiencies and Recommendations for Fertilizer. Tehran, Iran: Office for the Publishing of Scientific Works, (In Persian).Google Scholar
Marashi, M, Mohammadi Torkashvand, A, Ahmadi, A and Esfandyari, M (2017) Estimation of soil aggregate stability indices using artificial neural network and multiple linear regression models. Spanish Journal of Soil Science 7, 122–132.Google Scholar
Marashi, M, Mohammadi Torkashvand, A, Ahmadi, A and Esfandyari, M (2019) Adaptive neuro-fuzzy inference system: estimation of soil aggregates stability. Acta Ecologica Sinica 39, 95–101.CrossRefGoogle Scholar
Maynard, DN (1979) Nutritional disorders of vegetable crops: a review. Journal of Plant Nutrition 1, 1–23.CrossRefGoogle Scholar
Mengel, K and Kirkby, EA (2001) Principles of Plant Nutrition. Dordrecht, The Netherlands: Kluwer Academic Publishers.CrossRefGoogle Scholar
Mermoud, A and Xu, D (2006) Comparative analysis of three methods to generate soil hydraulic functions. Soil and Tillage Research 87, 89–100.CrossRefGoogle Scholar
Mohammadian, MA and Eshaghi Teymoori, R (1999) Farming, Cultivation and the Nutritional Value of Kiwi. Tehran, Iran: Iranian National Publication (In Persian).Google Scholar
Mohammadi Torkashvand, A, Rahpeik, ME, Hashemabadi, D and Sajjadi, SA (2016) Determining an appropriate fertilization planning to increase qualitative and quantitative characteristics of kiwifruit (Actinidia deliciosa L.) in Astaneh Ashrafieh, Gilan, Iran. Air. Soil and Water Research 9, 69–76.Google Scholar
Mohammadi Torkashvand, A, Ahmadi, A and Nikravesh, NL (2017) Prediction of kiwifruit firmness using fruit mineral nutrient concentration by artificial neural network (ANN) and multiple linear regressions (MLR). Journal of Integrative Agriculture 16, 1634–1644.CrossRefGoogle Scholar
Mohiti, M, Ardalan, MM, Mohammadi Torkashvand, A and Shokri Vahed, H (2011) The efficiency of potassium fertilization methods on the growth of rice (Oryza sativa L.) under salinity stress. African Journal of Biotechnology 10, 15946–15952.CrossRefGoogle Scholar
Mokhtari Karchegani, P, Ayoubi, SH, Honarju, N and Jalalian, A (2011) Predicting soil organic matter by artificial neural network in landscape scale using remotely sensed data and topographic attributes. Geophysical Research Abstracts 13, article no. EGU2011-1075. Available at https://meetingorganizer.copernicus.org/EGU2011/EGU2011-1075.pdf (accessed 3 December 2019).Google Scholar
Nachtigall, GR and Dechen, AR (2006) Seasonality of nutrients in leaves and fruits of apple trees. Scientia Agricola 63, 493–501.10.1590/S0103-90162006000500012CrossRefGoogle Scholar
Nascente, AS, Carvalho, MCS and Rosa, PH (2016) Growth, nutrient accumulation in leaves and grain yield of super early genotypes of common bean. Pesquisa Agropecuária Tropical 46, 292–300.CrossRefGoogle Scholar
Nayak, PC, Sudheer, KP, Rangan, DM and Ramasastri, KS (2004) A neuro-fuzzy computing technique for modelling hydrological time series. Journal of Hydrology 291, 52–66.10.1016/j.jhydrol.2003.12.010CrossRefGoogle Scholar
Niedbała, G (2019) Simple model based on artificial neural network for early prediction and simulation winter rapeseed yield. Journal of Integrative Agriculture 18, 54–61.CrossRefGoogle Scholar
O'Neal, MR, Engel, BA, Ess, DR and Frankenberger, JR (2002) Neural network prediction of maize yield using alternative data coding algorithms. Biosystems Engineering 83, 31–46.CrossRefGoogle Scholar
Pacheco, C, Calouro, F, Vieira, S, Santos, F, Neves, N, Curado, F, Franco, J, Rodrigues, S and Antunes, D (2008) Influence of nitrogen and potassium on yield, fruit quality and mineral composition of kiwifruit. International Journal of Energy and Environment 2, 9–15.Google Scholar
Park, SJ, Hwang, CS and Vlek, PLG (2005) Comparison of adaptive techniques to predict crop yield response under varying soil and land management conditions. Agricultural Systems 85, 59–81.CrossRefGoogle Scholar
Parvizi, Y, Gorji, M, Omid, M, Mahdian, MH and Amini, M (2010) Determination of soil organic carbon variability of rainfed crop land in semi-arid region (neural network approach). Modern Applied Science 4, 25–33.CrossRefGoogle Scholar
Paulo, EM and Furlani, E Jr (2010) Yield performance and leaf nutrient levels of coffee cultivars under different plant densities. Scientia Agricola 67, 720–726.CrossRefGoogle Scholar
Peng, J, Zhang, YZ, Pang, XA and Wang, JQ (2010) Hyperspectral features of soil organic matter content in South Xinjiang. Arid Land Geography 33, 740–746.Google Scholar
Prasad, A, Prakash, O, Mehrotra, S, Khan, F, Mathur, AK and Mathur, A (2017) Artificial neural network-based model for the prediction of optimal growth and culture conditions for maximum biomass accumulation in multiple shoot cultures of Centella asiatica. Protoplasma 254, 335–341.CrossRefGoogle ScholarPubMed
Saffari, M, Yasrebi, J, Sarikhani, F, Gazni, R, Moazallahi, M, Fathi, H and Emadi, M (2009) Evaluation of artificial neural network models for prediction of spatial variability of some soil chemical properties. Research Journal of Biological Sciences 4, 815–820.Google Scholar
Sauz, M, Heras, L and Montañés, L (1992) Relationships between yield and leaf nutrient contents in peach trees: early nutritional status diagnosis. Journal of Plant Nutrition 15, 1457–1466.CrossRefGoogle Scholar
Sepaskhah, AR, Moosavi, SAA and Boersma, L (2000) Evaluation of fractal dimensions for analysis of aggregate stability. Iran Agricultural Research 19, 99–114, In Persian with English abstract.Google Scholar
Sharma, RR (2002) Growing strawberries. Scientise division of fruit and horticulture techndogg. Indian Agricultural Research. Institute New Delhi Intenational Book Distributing Co, 1–164.Google Scholar
Sharples, RO (1980) The influence of orchard nutrition on the storage quality of apples and pears grown in the United Kingdom. In Atkinson, D, Jackson, JE, Sharples, RD and Walter, WM (eds), Mineral Nutrition of Fruit Trees. Boston: Butterworth, pp. 17–28.CrossRefGoogle Scholar
Shearer, JR, Burks, TF, Fulton, JP and Higgins, SF (2000) Yield prediction using a neural network classifier trained using soil landscape features and soil fertility data. Annual International Meeting, Midwest Express Center. ASAE Paper No. 001084, Milwaukee, Wisconsin. pp. 5–9.Google Scholar
UN Food and Agriculture Organization, Corporate Statistical Database (FAOSTAT) (2018). Kiwifruit production in 2017, Crops/Regions/World list/Production Quantity (pick lists). Visited in Wikipedia Available at https://en.wikipedia.org/wiki/Kiwifruit.Google Scholar
Vandendriessche, HJ (2000) A model of growth and sugar accumulation of sugar beet for potential production conditions. Theory And Model structure. Agricultural Systems 64, 1–19.CrossRefGoogle Scholar
Zaremehrjardi, M, Okhovatian Ardakani, AR and Dehghani, F (2019) Introducing the DOP index and its use to interpret the results of greenhouse cucumber leaf analysis. Leafy Vegetable 2, 9–21, In Persian with English abstract.Google Scholar
Zhou, T, Shi, PJ, Luo, JY and Shao, ZY (2008) Estimation of soil organic carbon based on remote sensing and process model. Frontiers of Forestry in China 3, 139–147.CrossRefGoogle Scholar