* Corresponding Author
Normalized Difference Vegetation Index Analysis to Evaluate Corn Cultivation Technology Based on Farmer Participation
https://doi.org/10.22146/ijg.72632 Fadjry Djufry(1), Muhammad Farid(2*), Ahmad Fauzan Adzima(3), Muhammad Fuad Anshori(4), Amir Yassi(5), Yunus Musa(6), Nasaruddin Nasaruddin(7), Muhammad Aqil(8), Hari Iswoyo(9), Muhammad Hatta Jamil(10), Sakka Pati(11)
(1) Indonesian Agency for Agric. Res. and Dev., Ministry of Agriculture of the Republic of Indonesia
(2) Department of Agronomy, Hasanuddin University
(3) Department of Soil Science, Hasanuddin University
(4) Department of Agronomy, Hasanuddin University
(5) Department of Agronomy, Hasanuddin University
(6) Department of Agronomy, Hasanuddin University
(7) Department of Agronomy, Hasanuddin University
(8) Indonesian Cereal Research Institute, Ministry of Agriculture of the Republic Indonesia
(9) Department of Agronomy, Hasanuddin University
(10) Department of Socio-Economics of Agriculture, Hasanuddin University
(11) Department of Civil Law, Hasanuddin University
(*) Corresponding Author
Abstract
An unmanned aerial vehicle (UAV), widely known as a drone, proves very effective in assessing cropping or crop cultivation. Its practical use in evaluating corn cultivation technology systems is feasible when based on farmer participation. UAV can generate the Normalized Difference Vegetation Index (NDVI) algorithm that reflects the greenness of leaves, which is a parameter related to photosynthesis and plant productivity. Therefore, the purpose of this study was to evaluate whether the participation-based UAV-derived NDVI could be effectively used to assess corn cultivation technology and determine the appropriate technology to be used in the cultivation. The research was conducted in Tarowang Village in Galesong Selatan District, Takalar Regency, South Sulawesi, Indonesia, using two plots, namely, mother trial and baby trial. The mother trial applied a randomized block design in which eight packages of corn cultivation technology were randomly assigned, whereas the baby trial consisted of eight corn plots cultivated by farmers. In the latter, each farmer received one package of the cultivation technology. The study results indicated that NDVI and yield could effectively evaluate corn cropping. Three packages, i.e., P1, P4, and P5, are recommended for corn cultivation, especially in the village observed. Nevertheless, they are expected to be also applicable to other districts in South Sulawesi to promote improvement in corn production.
Keywords
Full Text:
PDFReferences
Abduh, A.D.M., Padjung, R., Farid, M., Bahrun, A.H., Anshori, M.F., Nasaruddin, Ridwan, I., Nur, A., Taufik, M. (2021). Interaction of Genetic and Cultivation Technology in Maize Prolific and Productivity Increase. Pak. J. Biol. Sci., 24(6): 716-723.
Al-doski, J., Mansor, S., & Shafri, H. Z. M. (2013). NDVI Differencing and Post-classification to Detect Vegetation Changes in Halabja City, Iraq. IOSR Journal of Applied Geology and Geophysics, 1(2), 01–10. https://doi.org/10.9790/0990-0120110
Anshori, M.F., Purwoko, B.S., Dewi, I.S., Ardie, S.W., Suwarno, W.B. (2019). Selection Index Based on Multivariate Analysis for Selecting Doubled-Haploid Rice Lines in Lowland Saline Prone Area. SABRAO J Breed Genet 51(2), 161-174.
Anshori, M.F., Purwoko, B.S., Dewi, I.S., Ardie, S.W., Suwarno, W.B. (2021a). A New Approach to Select Doubled Haploid Rice Lines Under Salinity Stress Using Indirect Selection Index. Rice Sci 28 (4): 368-378. DOI: 10.1016/j.rsci.2021.05.007
Anshori, M.F., Farid, M., Nasaruddin, Musa, Y., Iswoyo, H., Sakinah, A.I., et al. (2021b). Development of Image-based Phenotyping for Selection Characters of Rice Adaptability on The Seedling Salinity Screening. IOP Conf. Ser. Earth Environ. Sci. 807: 032022.
Azzawi, T.N.I.A., Khan, M., Hussain, A., Shahid, M., Imran, Q.M., Mun, B.G., Lee, S.U,. Yun, B.W. (2020). Evaluation of Iraqi Rice Cultivars for Their Tolerance to Drought Stress. Agronomy 10: 1782. doi:10.3390/agronomy10111782
BPS-Statistics Indonesia (2019). Corn Production in Indonesia 2014-2018. Ministry of Agriculture Republic of Indonesia.
Benincasa, P., Antognelli, S., Brunetti, L., Fabbri, C.A., Natale, A., Sartoretti, V., Modeo, G., Guiducci, M., Tei, F., Vizzari, M. (2017). Reliability of NDVI Derived by High Resolution Satellite and UAV Compared to in-Field Methods for The Evaluation of Early Crop Status and Grain Yield in Wheat. Exp. Agric. 54, 604–622.
Brito, C., Dinis, L. T., Moutinho-Pereira, J., & Correia, C. M. (2019). Drought Stress Effects and Olive Tree Acclimation under a Changing Climate. Plants (Basel, Switzerland), 8(7), 232. https://doi.org/10.3390/plants8070232
Cervantes, R.A., Angulo, G.V., Tavizón, E.F., González, J.R. (2014). Impactos potenciales del cambio climático en la producción de maíz Potential impacts of climate change on maize production. Investigación Ciencia, 22, 48–53.
de Castro, A. I., Shi, Y., Maja, J. M., & Peña, J. M. (2021). UAVs for Vegetation Monitoring: Overview and Recent Scientific Contributions. Remote Sensing, 13(11), 2139. http://dx.doi.org/10.3390/rs13112139
Durło, Grzegorz & Jagiełło-Leńczuk, Krystyna & Kormanek, Mariusz & Małek, Stanisław & Banach, Jacek & Pająk, Katarzyna. (2015). Using unmanned aerial vehicle (UAV) to monitor the physiological condition of plants in a nursery. TU Zvilen Mongraph, ISBN 978-80-228-2920-5. 1. 18-28.
Farid, M., Nasaruddin, N., Musa, Y., Anshori, M.F., Ridwan, I., Hendra, J., Subroto, G. (2020). Genetic Parameters and Multivariate Analysis to Determine Secondary Traits in Selecting Wheat Mutant Adaptive on Tropical Lowlands. Plant Breed. Biotechnol. 8(4), 368–377.
Farid, M., Nasaruddin, N., Anshori, M.F., Musa, Y., Iswoyo, H., Sakinah, A.I. (2021). Interaction of rice salinity screening in germination and seedling phase through selection index based on principal components. Chile J. Agric. Res. 81(3), 368–377.
García-Martínez, H., Flores-Magdaleno, H., Ascencio-Hernández, R., Khalil-Gardezi, A., Tijerina-Chávez, L., Mancilla-Villa, O.R., Vázquez-Peña, M.A. (2020). Corn Grain Yield Estimation from Vegetation Indices, Canopy Cover, Plant Density, and a Neural Network Using Multispectral and RGB Images Acquired with Unmanned Aerial Vehicles. Agriculture, 10, 277. https://doi.org/10.3390/agriculture10070277
Guan, S., Fukami, K., Matsunaka, H., Okami, M., Tanaka, R., Nakano, H., Sakai, T., et al. (2019). Assessing Correlation of High-Resolution NDVI with Fertilizer Application Level and Yield of Rice and Wheat Crops using Small UAVs. Remote Sensing, 11(2), 112. MDPI AG. Retrieved from http://dx.doi.org/10.3390/rs11020112
Herrmann, I., Bdolach, E., Montekyo, Y., Rachmilevitch, S., Townsend, P.A., Karnieli, A. (2020). Assessment of maize yield and phenology by drone-mounted super spectral camera. Precision Agric. 21, 51–76. https://doi.org/10.1007/s11119-019-09659-5
Janoušek, J., Jambor, V., Marcoň, P., Dohnal, P., Synková, H., & Fiala, P. (2021). Using UAV-Based Photogrammetry to Obtain Correlation between the Vegetation Indices and Chemical Analysis of Agricultural Crops. Remote Sensing, 13(10), 1878. MDPI AG. Retrieved from http://dx.doi.org/10.3390/rs13101878
Jiang, Z., Huete, A. R., Chen, J., Chen, Y., Li, J., Yan, G., & Zhang, X. (2006). Analysis of NDVI and scaled difference vegetation index retrievals of vegetation fraction. Remote Sensing of Environment, 101(3), 366–378. https://doi.org/10.1016/j.rse.2006.01.003
Kapoor D, Bharwaj S, Landi M, Sharma A, Ramakrishnan M, Sharma A. (2020). The Impact of Drought in Plant Metabolism: How to Exploit Tolerance Mechanisms to Increase Crop Production. Applied Sciencess 10, 5692. doi:10.3390/app10165692
Karimpour, M. (2019). Effect of Drought Stress on RWC and Chlorophyll Content on Wheat (Triticum durum L.) Genotypes. World. Ess. J. 7(1), 52–56
Kim, E.-J., Nam, S.-H., Koo, J.-W., & Hwang, T.-M. (2021). Hybrid Approach of Unmanned Aerial Vehicle and Unmanned Surface Vehicle for Assessment of Chlorophyll-a Imagery Using Spectral Indices in Stream, South Korea. Water, 13(14), 1930. MDPI AG. Retrieved from http://dx.doi.org/10.3390/w13141930
Lyon, A., Tracy, W., Colley, M,, Culbert, P., Mazourek, M., Myers, J., Zystro, J., Silva, E.M. (2020). Adaptability analysis in a participatory variety trial of organic vegetable crops. Renewable Agriculture and Food Systems, 35(3), 296-312. doi:10.1017/S1742170518000583
Mahlein A. K. (2016). Plant Disease Detection by Imaging Sensors - Parallels and Specific Demands for Precision Agriculture and Plant Phenotyping. Plant disease, 100(2), 241–251. https://doi.org/10.1094/PDIS-03-15-0340-FE
Merga, W. (2017). Review on Participatory Plant Breeding. International Journal of Research Studies in Agricultural Sciences, 3(9), 7-13. DOI: http://dx.doi.org/10.20431/2454-6224.0309002
Messina, G., Modica, G. (2020). Applications of UAV Thermal Imagery in Precision Agriculture: State of the Art and Future Research Outlook. Remote Sensing, 12(9), 1491. https://doi.org/10.3390/rs12091491
Morris, M.L., Bellon, M.R. (2004). Participatory Plant Breeding Research: Opportunities and Challenges for The International Crop Improvement System. Euphytica 136, 21-35. https://doi.org/10.1023/B:EUPH.0000019509.37769.b1
Moore, F.C., Lobell, D.B. (2015). Reply to Gonsamo and Chen: Yield findings independent of cause of climate trends. Proceedings of the National Academy of Sciences of the United States of America, 112(18), E2267. https://doi.org/10.1073/pnas.1504457112
Najeeb, S., Sheikh, F., Parray, G., Shikari, A., Zaffar, G., Kashyap, S.C., Ganie, M., and Shah, A. (2018). Farmers' participatory selection of new rice varieties to boost production under temperate agro-ecosystems. Journal of Integrative Agriculture. Integrative Agriculture, 17(6), 1307-1314. https://doi.org/10.1016/S2095-3119(17)61810-0.
Neupane, K., & Baysal-Gurel, F. (2021). Automatic Identification and Monitoring of Plant Diseases Using Unmanned Aerial Vehicles: A Review. Remote Sensing, 13(19), 3841. https://doi.org/10.3390/rs13193841
Nishant, B.A., Singh, M.N., Srivastava, K., Hemantaranjan, A. (2016). Molecular mapping and breeding of physiological traits. Advances in Plants & Agriculture Research 3(6), 193‒206. doi: 10.15406/apar.2016.03.00120.
Padjung R, Farid M, Musa Y, Anshori MF, Nur A, Masnenong A. (2021a). Drought-adapted maize line based on morphophysiological selection index. Biodiversitas 22(9): 4028–4035. DOI: 10.13057/biodiv/d220951
Panday, U.S., Pratihast, A.K., Aryal, J., Kayastha, R.B. (2020). A Review on Drone-Based Data Solutions for Cereal Crops. Drones, 4(3), 41. https://doi.org/10.3390/drones4030041
Paulus, S. (2019). Measuring crops in 3D: using geometry for plant phenotyping. Plant Methods, 15, 103 https://doi.org/10.1186/s13007-019-0490-0
Perros, N., Kalivas, D., Giovos, R. (2021). Spatial Analysis of Agronomic Data and UAV Imagery for Rice Yield Estimation. Agriculture, 11(9), 809. https://doi.org/10.3390/agriculture11090809
Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., Moscholios, I. (2020). A Compilation of UAV Applications for Precision Agriculture. Computer Networks, 172, 107148. doi:10.1016/j.comnet.2020.107148
Snapp, S., Kanyama-Phiri, G., Kamanga, B., Gilbert, R., Wellard, K. (2002). Farmer and Researcher Partnerships in Malawi: Developing Soil Fertility Technologies for the Near-term and Far-term. Experimental Agriculture, 38(4), 411-431. doi:10.1017/S0014479702000443.
Shin, Y.K., Bhandari, S.R., Jo, S.J., Song, J.W., Lee, J.G. (2021). Effect of Drought Stress on Chlorophyll Fluorescence Parameters, Phytochemical Contents, and Antioxidant Activities in Lettuce Seedlings. Horticulturae 7(8), 238. https://doi.org/10.3390/horticulturae7080238
Széles, A., Megyes, A., Nagy, J. (2012). Irrigation and nitrogen effects on the leaf chlorophyll content and grain yield of maize in different crop years. Agricultural Water Management. 107, 133–144. DOI:10.1016/j.agwat.2012.02.001.
Tarancón, M., Díaz-Ambrona, C.H., Trueba, I. (2011). Cómo alimentar a 9.000 millones de personas en el 2050?. Proceedings of the XV Congreso Internacional de Ingeniería de Proyectos 6–8 July, Huesca, Spain,. pp. 1563-1578.
Tyack, N. (2020). Genetic resources and agricultural productivity in the developing world. 2020 Annual Meeting, July 26-28, Kansas City, Missouri 304277, Agricultural and Applied Economics Association. https://ideas.repec.org/p/ags/aaea20/304277.html
Wahab, I., Hall, O., & Jirström, M. (2018). Remote Sensing of Yields: Application of UAV Imagery-Derived NDVI for Estimating Maize Vigor and Yields in Complex Farming Systems in Sub-Saharan Africa. Drones, 2(3), 28. MDPI AG. Retrieved from http://dx.doi.org/10.3390/drones2030028
Weier, J. and Herring, D. (2000). Measuring Vegetation (NDVI & EVI). NASA Earth Observatory, Washington DC.
Xiang, D., Peng, L., Zhao, J., Zou, L., Zhao, G., & Song, C. (2013). Effect of drought stress on yield, chlorophyll contents and photosynthesis in tartary buckwheat (Fagopyrum tataricum). J. Food Agric. Environ, 11, 1358–1363.
Yan, Y., Hou, P., Duan, F., Niu, L., Dai, T., Wang, K., Zhao, M., Li, S., & Zhou, W. (2021). Improving photosynthesis to increase grain yield potential: an analysis of maize hybrids released in different years in China. Photosynthesis Research, 150, 295 - 311. https://doi.org/10.1007/s11120-021-00847-x
Zaman-Allah, M., Vergara, O., Araus, J. L., Tarekegne, A., Magorokosho, C., Zarco-Tejada, P. J., Hornero, A., Albà, A. H., Das, B., Craufurd, P., Olsen, M., Prasanna, B. M., & Cairns, J. (2015). Unmanned aerial platform-based multi-spectral imaging for field phenotyping of maize. Plant methods, 11, 35. https://doi.org/10.1186/s13007-015-0078-2
Zhang, L., Zhang, H., Niu, Y., & Han, W. (2019). Mapping Maize Water Stress Based on UAV Multispectral Remote Sensing. Remote Sensing, 11(6), 605. https://doi.org/10.3390/rs11060605
DOI: https://doi.org/10.22146/ijg.72632
Article Metrics
Abstract views : 705
| 
views : 407
Refbacks
- There are currently no refbacks.
Copyright (c) 2022 Ahmad Fauzan Adzima, Fadjry Djufry, Muhammad Farid, Yunus Musa, Muhammad Fuad Anshori
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
Accredited Journal, Based on Decree of the Minister of Research, Technology and Higher Education, Republic of Indonesia Number 225/E/KPT/2022, Vol 54 No 1 the Year 2022 - Vol 58 No 2 the Year 2026 (accreditation certificate download)
ISSN 2354-9114 (online), ISSN 0024-9521 (print)