Estimating of Maize Yield Using UAV Images and NDVI Index

Main Article Content

Phoomchai Traidalanon
Kiattisak Sangpradit

Abstract

The objective of this research is to study the relationship of the maize growth using an aerial geographic mapping with an unmanned aerial-vehicle (UAV) in order to forecast the actual number of yielded maizes. The work was done using Normalized Difference Vegetation Index (NDVI), acquired from considering the reflection of light waves Red (668 nm), Green (560 nm), NIR (842 nm) and Red Edge (717 nm). The study area was in Lam Sai, Wang Noi District, Phra Nakhon Sri Ayutthaya Province, started from June to November 2021. The experimental area was 12,467 square meters, separated into 198 blocks of 6x10 square meters. In the experiment, the area was divided into area A, prepared without soil improvement and B, with soil improvement, each measured 5,600 square meters in size. The result, after collecting the data for 107 days, showed that the relationship between NDVI and weight of maize seed can be described with linear regression, where the acquired yield forecast is Y = 0.67X – 30.927, with 95% level of confidence and 61% r-squared value. Furthermore, the correlation was calculated to be 78% when comparing the yield approximation of the entire area with the weight of harvested seeds. In addition, seeds at 30% humidity are 3,208 kilograms and 2,999 kilograms for area A and B, respectively. When compared to the areas consisting NDVI 50-59%, 60-69, 70-79%, 80-89% and 90-100%, the error was calculated to be 1.75 and 1.67, respectively.

Article Details

How to Cite
1.
Traidalanon P, Sangpradit K. Estimating of Maize Yield Using UAV Images and NDVI Index. J Appl Res Sci Tech [Internet]. 2023 Apr. 5 [cited 2024 Nov. 22];22(1):27-39. Available from: https://ph01.tci-thaijo.org/index.php/rmutt-journal/article/view/250821
Section
Research Articles

References

Department of Agriculture. Maize Production Technology [Internet]. 2019 [ cited 2023 Jan 20]. Availability from: https://www.doa. go.th/oard5/wpcontent/uploads/201909/km62.pdf

OECD-FAO Agricultural Outlook 2021-2030 [Internet]. [ cited 2023 Jan 20]. Availability from: https://www.oecdilibrary.org

Department of Agriculture [Internet]. [ cited 2023 Jan 20]. Availability from: https://www.oae.go.th/assets/portals/1/fileups/forecastdata/files/forecast/situation/3S_MZ.pdf

ARDA. Smart Farming, Agricultural Research Development Agency (Public organization) [Internet]. 2018 [cited 2023 Jan 20]. Availability from: https://www.arda.or.th/knowledge_detail.php?id=6

Mitr Phol. Remote Sensing, Remote Sensing Technology [Internet]. 2018 [ cited 2023 Jan 20]. Availability from: https://www.mitrphol-modernfarm.com

Paolo DF, Tania L, Alice C, Chiara G, Pasquale D, Marco M, et al. Multispectral sentinel-2 and SAR sentinel-1 integration for automatic land cover classification. Land. 2021;10(6):611.

David JM, Yuxin M. Precision Farming. In: Land Resources Monitoring, Modeling, and Mapping with Remote Sensing. 2016. p. 161-78.

Mulla DJ, Miao Y. 2015. Precision Farming. In: Land Resources Monitoring, Modeling, and Mapping with Remote Sensing. 1st ed. Imprint CRC Press; 2015. p. 161–78.

Naphat R, Raksak S, Kriengkri K. Using digital image for estimation leaf area of rice. Thai Science and Technology Journal. 2020;28(9): 1717-24.

Sureeporn K, Napat J, Jitti M. Assessment of greenhouses gas emission and mitigation from maize cultivation in thailand for sustainable production systems. Thai Science and Technology Journal. 2019;26(7):1176-96.

Panath J, Sununtha K, Petchporn C, Yasushi K. NDVI response to nitrogen fertilizer application in waxy corn using uav imagery. Khon Kaen Agriculture Journal. 2020;48(5):1068-81.

Preeyanan S, Piyatida B, Waratchaya P, Arunprapa P. Sugarcane yield estimate from remotely sensed data in nakhon sawan province. Kasetsart University; 2019. p. 1-35.

Nodthaisong C, Saengprachathanarug K, Chea C, Posom J, Wongpichet S, Konyai S, et al. Feasibility study of sugarcane yield prediction using NDVI, cired edge indice associated with volume of digital surface model (DSM). Khon Kaen Agriculture Journal. 2021;47(4):679–94.

Etienne D, Gaëtan D, François J, Philippe B, Alexis C, Benoit de S, et al. Plant detection and counting from high-resolution RGB images acquired from UAVs: comparison between deep-learning and handcrafted methods with application to maize, sugar beet, and sunflower crops. bioRxiv. 2021;1-18. doi: https://doi.org /10.1101/2021.04.27.441631

Kieran O’Keeffe. Maize Growth & Development. Orange, N.S.W.: NSW Dept. of Primary Industries [Internet]. 2009 [ cited 2023 Jan 20]. Availability from: https://cornindustryppnikolakostic.wordpress.com/life-cycle of-corn/