Real time estimation of leaf area index and groundnut yield using multispectral UAV

Muhammad Naveed Tahir, Yubin Lan, Yali Zhang, Yingkuan Wang, Faisal Nawaz, Muhammad Arslan Ahmed Shah, Asim Gulzar, Waqar Shahid Qureshi, Syed Manshoor Naqvi, Syed Zaigham Abbas Naqvi

Abstract


Abstract: The use of Unmanned Aerial Vehicles (UAVs) is becoming very common for last few years for monitoring agricultural crops efficiently.  Low altitude remote sensing (UAV) provides people with high temporal and spatial resolution for non-destructive, accurate and timely estimation of biophysical parameters like Leaf Area Index (LAI), crop growth, plant biomass and final crop yield.  Collection of the data by UAV helps to reduce errors and it fills the biasness on an observational scale in precision agriculture.  The main objective of this study was to estimate real time LAI and yield of groundnut crop based on Normalized Difference Vegetation Index (NDVI) using low cost multispectral UAV.  A field experiment was set up  with three different groundnut cultivars (V1= BARD-479, V2 = Chakwal-2011 and V3 = Chakwal-2016) with three replications.  Field data collection regarding LAI was performed in 2019 at two different growth stages (2-3 leaf stage and Peg formation stage) of groundnut on PMAS-Arid Agriculture Research Farm (Knoot), Pakistan.  Final yield was calculated at the time of crop maturity.  In this study, low cost UAV platform was established with DJI Phantom 4 pro and Parrot Sequoia Sensor to develop a multispectral UAV system used as the survey platform.  A Parrot Sequoia camera was mounted on the UAV used as the remote sensor.  The sensor provided the information in five narrow bands including Red, Blue, Green, Near infrared (NIR) and Red Edge.  The processing of UAV images was performed in the Python environment and NDVI images were created.  Then regression model was performed to compare the NDVI data with the LAI and final yield of groundnut crop.  The results indicated that the highest value of R2 = 0.93 was found with NDVI and LAI at peg formation stage while value of R2 = 0.59 was at 2-3 leaf stage.  The strong and positive relationship was found between LAI and yield (R2 = 0.97).  There was also a strong and positive relationship between NDVI and yield of groundnut with value of R2 = 0.92.  The study showed that low cost multispectral UAV can be effectively used for real time estimation of LAI and groundnut yield nondestructively and accurately.  The study results show that this low cost multispectral UAV platform (DJI Phantom 4 Pro with Parrot Squoia) is robust in management decisions of agriculture such as effective fertilizer application, growth monitoring, and yield estimation accurately and timely based on the vegetation indices.  This study also proved the low cost multispectral UAV practicability in estimating plant biophysical parameters at a small field experiment scale reliably.

Keywords: vegetation indices, UAV, LAI, yield, multispectral camera, NDVI, groundnut, growth stages

DOI: 10.33440/j.ijpaa.20200301.70

 

Citation: Tahir M N, Lan Y B, Zhang Y L, Wang Y K, Faisal N, Shah M A A, et al.  Real time estimation of leaf area index and groundnut yield using multispectral UAV.  Int J Precis Agric Aviat, 2020; 3(1): 1–6.


Full Text:

PDF

References


Bendig, J., Bolten, A., Bennertz, S., Broscheit, J., Eichfuss, S., Bareth, G. Estimating biomass of barley using crop surface models (CSMs) derived from UAV-based RGB imaging. Remote Sensing, 2014; 6(11): 10395–10412.

Candiago, S., Remondino, F., De Giglio, M., Dubbini, M., Gattelli, M. Evaluating multispectral images and vegetation indices for precision farming applications from UAV images. Remote Sensing, 2015; 7(4): 4026–4047.

Carvalho, F. P. Agriculture, pesticides, food security and food safety. Environmental science & policy, 2006; 9(7-8): 685–692.

Dalezios, N. R., Domenikiotis, C., Loukas, A., Tzortzios, S. T., Kalaitzidis, C. Cotton yield estimation based on NOAA/AVHRR produced NDVI. Physics and Chemistry of the Earth, Part B: Hydrology, Oceans and Atmosphere, 2001; 26(3): 247–251.

Din M, Ming J, Hussain S, Ata-Ul-Karim S.T., Rashid M, Tahir M. N., Hua, S., Wang. S. Estimation of Dynamic Canopy Variables Using Hyperspectral Derived Vegetation Indices Under Varying N Rates at Diverse Phenological Stages of Rice. Front. Plant Sci. 2019; 9:1883. doi: 0.3389/fpls.2018.01883.

Dodds, F., Bartram, J. (Eds.). The water, food, energy and climate Nexus: Challenges and an Agenda for action, 2016.

Dutta, D., Kundu, A., Patel, N. R., Saha, S. K., Siddiqui, A. R. Assessment of agricultural drought in Rajasthan (India) using remote sensing derived Vegetation Condition Index (VCI) and Standardized Precipitation Index (SPI). The Egyptian Journal of Remote Sensing and Space Science, 2015; 18(1): 53–63.

Hammons, R. O. Genetics of Arachishypogaea. Peanuts: culture and uses. American Peanut Research and Education Association, 1973; 135–173.

http://www.fao.org/publications/sofa/2015/en/

http://www.parc.gov.pk/index.php/en/

Li, X., & Shao, G. Object-based land-cover mapping with high resolution aerial photography at a county scale in midwestern USA. Remote Sensing, 2014; 6(11): 11372–11390.

Machado, B. B., Orue, J. P., Arruda, M. S., Santos, C. V., Sarath, D. S., Goncalves, W. N., Rodrigues-Jr, J. F. BioLeaf: A professional mobile application to measure foliar damage caused by insect herbivory. Computers and Electronics in Agriculture, 2016; 129: 44–55.

Naqvi, S. M. Z. A., Tahir, M. N., Shah, G. A., Sattar, R. S., Awais, M. Remote estimation of wheat yield based on vegetation indices derived from time series data of landsat 8 imagery. Appl. Ecol.Environ.Res, 2018; 17(2): 3909–3925.

Rodriguez, J. C., Duchemin, B., Hadria, R., Watts, C., Garatuza, J., Chehbouni, A., Khabba, S., Boulet, G., Palacios, E. Lahrouni, A. Wheat yield estimation using remote sensing and the STICS model in the semiarid Yaqui valley, Mexico. Agronomie, 2004; 24: 295–304.

Rouse, J. W., Haas, R. H., Schell, J. A., Deering, D. W. Monitoring vegetation systems in the Great Plains with ERTS. NASA Special Publication, 1974; 351, 309.

Savage, G. P., Keenan, J. I. The composition and nutritive value of groundnut kernels. In The groundnut crop, 1994; 173–213.

Singh, R., Goyal, R. C., Saha, S. K., Chhikara, R. S. Use of satellite spectral data in crop yield estimation surveys. International Journal of Remote Sensing, 1992; 13(14): 2583–2592.

Tahir, M. N., Jun, L., Liu, B., Zhao, G., Fuqi, Y., Chengfeng, C. Hyperspectral estimation model for nitrogen contents of summer corn leaves under rainfed conditions. Pak. J. Bot. 2013; 45: 1623–30.

Tahir, M. N., Jun, L., Liu, B.F., Cui, C.F. Hyperspectral remote estimation of leaf nitrogen content of summer corn based on red edge inflection point. Sci.int. (Lahore), 2015; 27(2), 1689–1691.

Tahir, M.N., Naqvi, S. Z. A., Lan, Y.B., Zhang, Y.L., Wang, Y. K., Afzal, M. Real time monitoring of chlorophyll content based on vegetation indices derived from multispectral UAVs in the kinnow orchard. Int. J. Precis. Agric. Aviat., 2018; 1(1): 24–31.

United Nations. Transforming our world: The 2030 agenda for sustainable development. General Assembley 70 session, 2015.

Van Wart, J., Kersebaum, K. C., Peng, S., Milner, M., Cassman, K. G. Estimating crop yield potential at regional to national scales. Field Crops Research, 2013; 143: 34–43.

Vermeulen, S. J., Aggarwal, P. K., Ainslie, A., Angelone, C., Campbell, B. M., Challinor, A. J., .. Lau, C. Options for support to agriculture and food security under climate change. Environmental Science & Policy, 2012; 15(1): 136–144.

Wang, L., Lan, Y., Zhang, Y., Zhang, H., Tahir, M. N., Ou, S., Liu, X., Chen, P. Applications and Prospects of Agricultural Unmanned Aerial Vehicle Obstacle Avoidance Technology in China. Sensors 2019, 19, 642; doi: 10.3390/s19030642.

Wang, X., Sun, H., Long, Y., Zheng, L., Liu, H., Li, M. Development of visualization system for agricultural UAV crop growth information collection. IFAC-PapersOnLine, 2018; 51(17): 631–636.

Xue, J., Fan, Y., Su, B., Fuentes, S. Assessment of canopy vigor information from kiwifruit plants based on a digital surface model from unmanned aerial vehicle imagery. International Journal of Agricultural and Biological Engineering, 2019; 12(1): 165–171.


Refbacks

  • There are currently no refbacks.