Home / Regular Issue / JST Vol. 29 (2) Apr. 2021 / JST-2279-2020


Smartphone Application Development for Rice Field Management Through Aerial Imagery and Normalised Difference Vegetation Index (NDVI) Analysis

Nor Athirah Roslin, Nik Norasma Che’Ya, Rhushalshafira Rosle and Mohd Razi Ismail

Pertanika Journal of Science & Technology, Volume 29, Issue 2, April 2021

DOI: https://doi.org/10.47836/pjst.29.2.07

Keywords: Chlorophyll content analysis; multispectral imagery; smart farming; smartphone application

Published on: 30 April 2021

In the current practices, farmers typically rely on the traditional method paper-based for farming data records, which leads to human error. However, the paper-based system can be improved by the mobile app technology to ease the farmers acquiring farm data as all of the farm information will be stored in digital form. This study aimed to develop a smartphone agricultural management app known as Padi2U and implement User Acceptance Test (UAT) for end-users. Padi2U was developed using Master App Builder software and integration with the multispectral imagery. Padi2U provides recommendations based on the Department of Agriculture’s (DOA), such as rice check, pest and disease control, and weed management. Through the Padi2U, farmers can access the field data to understand the crop health status online using the Normalised Difference Vegetation Index (NDVI) map derived from the multispectral images. The NDVI is correlated to the Soil Plant Analysis Development (SPAD) value, corresponding to R² = 0.4012. UAT results showed a 100 percent satisfaction score with suggestions were given to enhance the Padi2U performance. It shows that Padi2U can be improved to help farmers in the field monitoring virtually by integrating multispectral imagery and information from the field.

  • Abdullah, S., Tahar, K. N., Rashid, M. F. A., & Osoman, M. A. (2019). Camera calibration performance on different non-metric cameras. Pertanika Journal of Science & Technology, 27(3), 1397-1406.

  • Alam, M. J., Awal, M. A., & Mustafa, M. N. (2019). Crops diseases detection and solution system. International Journal of Electrical and Computer Engineering, 9(3), 2112-2120. https: //doi.org/10.11591/ijece.v9i3.pp2112-2120

  • Barkunan, S. R., Bhanumathi, V., & Sethuram, J. (2019). Smart sensor for automatic drip irrigation system for paddy cultivation. Computers & Electrical Engineering, 73, 180-193. https: //doi.org/10.1016/j.compeleceng.2018.11.013

  • Bueno-Delgado, M. V., Molina-Martínez, J. M., Correoso-Campillo, R., & Pavón-Mariño, P. (2016). Ecofert: An android application for the optimization of fertilizer cost in fertigation. Computers and Electronics in Agriculture, 121, 32-42. https: //doi.org/10.1016/j.compag.2015.11.006

  • Casanova, D., Epema, G. F., & Goudriaan, J. (1998). Monitoring rice reflectance at field level for estimating biomass and LAI. Field Crops Research, 55(1-2), 83-92. https: //doi.org/10.1016/S0378-4290(97)00064-6

  • Christensen, B. M. (2019). Using mid-season NDVI data from drones to produce variable rate fertilizer maps in wheat [Master Thesis ]. The North Dakota State, United States. Retrieved July 17, 2020, from https: //search.proquest.com/docview/2330627605?accountid=27932

  • Fatah, F. A., Yaakub, N., Ridzuan, R. M., & Ahmad, A. R. (2017). The study on the economic fertilizer requirement for paddy production on a Malaysian soil. Journal of Fundamental and Applied Sciences, 9(2S), 777-798. https: //doi.org/10.4314/jfas.v9i2s.48

  • Fountas, S., Carli, G., Sørensen, C. G., Tsiropoulos, Z., Cavalaris, C., Vatsanidou, A., Liakos, B., Canavari, M., Wiebensohn, J., & Tisserye, B. (2015). Farm management information systems: Current situation and future perspectives. Computers and Electronics in Agriculture, 115, 40-50. https: //doi.org/10.1016/j.compag.2015.05.011

  • Gamon, J. A., Field, C. B., Goulden, M. L., Griffin, K. L., Hartley, A. E., Joel, G., Penuelas, J., & Valentini, R. (1995). Relationships between NDVI, canopy structure, and photosynthesis in three Californian vegetation types. Ecological Applications, 5(1), 28-41. https: //doi.org/10.2307/1942049

  • Guan, S., Fukami, K., Matsunaka, H., Okami, M., Tanaka, R., Nakano, H., Sakai, T., Nakano, K., Ohdan, H., & Takahashi, K. (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), Article 112. https: //doi.org/10.3390/rs11020112

  • Guo, J., Li, X., Li, Z., Hu, L., Yang, G., Zhao, C., Fairbairn, D., Watson, D., & Ge, M. (2018). Multi-GNSS precise point positioning for precision agriculture. Precision Agriculture, 19(5), 895-911. https: //doi.org/10.1007/s11119-018-9563-8

  • Hassan, M. S., Khair, A., Haque, M. M., Azad, A. K., & Hamid, A. (2009). Genotypic variation in traditional rice varieties for chlorophyll content, SPAD value and nitrogen use efficiency. Bangladesh Journal of Agricultural Research, 34(3), 505-515. https: //doi.org/10.3329/bjar.v34i3.3977

  • Hassan, S., Mohamed, Z. A. B., Abdullah, S. N. S., & Zaini, N. N. (2017). Personality traits and its relationship with work performance for majority group of paddy farmers in Malaysia. Australian Academy of Business and Economics Review, 2(3), 234- 243.

  • Hassan, S., Yussof, N., & Galadima, M. (2019). Farmers current agriculture practices on paddy cultivation and relationship with work performance in Iada Batang Lupar, Sarawak, Malaysia. Asian Journal of Agricultural Extension, Economics & Sociology, 31(3), 1-14. https: //doi.org/10.9734/ajaees/2019/v31i330134

  • Herrick, J. E., Beh, A., Barrios, E., Bouvier, I., Coetzee, M., Dent, D., & Matuszak, J. (2016). The land‐potential knowledge system (LandPKS): Mobile apps and collaboration for optimizing climate change investments. Ecosystem Health and Sustainability, 2(3), Article e01209. https: //doi.org/10.1002/ehs2.1209

  • Hudzari, R. M., Ishak, W. W. W., & Noorman, M. M. (2010). Parameter acceptance of software development for oil palm fruit maturity prediction. Journal of Software Engineering, 4(3), 244-256.

  • Ibrahim, A. Z., & Alam, M. M. (2016). Climatic changes, government interventions, and paddy production: an empirical study of the Muda irrigation area in Malaysia. International Journal of Agricultural Resources, Governance and Ecology, 12(3), 292-304. https: //doi.org/10.1504/IJARGE.2016.078319

  • Ishak, W. I. W., & Hudzari, R. M. (2010). Image based modeling for oil palm fruit maturity prediction. Journal of Food, Agriculture & Environment, 8(2), 469-476.

  • Jaiganesh, S., Gunaseelan, K., & Ellappan, V. (2017). IOT agriculture to improve food and farming technology. In 2017 Conference on Emerging Devices and Smart Systems (ICEDSS). IEEE Conference Publication.

  • Johannes, A., Picon, A., Alvarez-Gila, A., Echazarra, J., Rodriguez- Vaamonde, S., Navajas, A. D., & Ortiz-Barredo, A. (2017). Automatic plant disease diagnosis using mobile capture devices, applied on a wheat use case. Computers and Electronics in Agriculture, 138, 200- 209. https: //doi.org/10.1016/j.compag.2017.04.013

  • Khaliq, A., Comba, L., Biglia, A., Ricauda Aimonino, D., Chiaberge, M., & Gay, P. (2019). Comparison of satellite and UAV-based multispectral imagery for vineyard variability assessment. Remote Sensing, 11(4), Article 436. https: //doi.org/10.3390/rs11040436

  • Khanna, A., & Kaur, S. (2019). Evolution of Internet of Things (IoT) and its significant impact in the field of precision agriculture. Computers and Electronics in Agriculture, 157, 218-231. https: //doi.org/10.1016/j.compag.2018.12.039

  • Kodali, R. K., & Sarjerao, B. S. (2017). A low cost smart irrigation system using MQTT protocol. In 2017 IEEE Region 10 Symposium (TENSYMP). IEEE Conference Publication.

  • Kularbphettong, K., Phoso, W., & Roonrakwit, P. (2019). The Automation of Mobile Application to Manage the Rice Fields. TEM Journal, 8(3), 866-871.

  • Liu, C., Liu, Y., Lu, Y., Liao, Y., Nie, J., Yuan, X., & Chen, F. (2019). Use of a leaf chlorophyll content index to improve the prediction of above- ground biomass and productivity. Peer J, 6, Article e6240. https: //doi.org/10.7717/peerj.6240

  • Liu, S., Li, L., Gao, W., Zhang, Y., Liu, Y., Wang, S., & Lu, J. (2018). Diagnosis of nitrogen status in winter oilseed rape (Brassica napus L.) using in-situ hyperspectral data and unmanned aerial vehicle (UAV) multispectral images. Computers and Electronics in Agriculture, 151, 185-195. https: //doi.org/10.1016/j.compag.2018.05.026

  • Majid, K., Herdiyeni, Y., & Rauf, A. (2013). I-PEDIA: Mobile application for paddy disease identification using fuzzy entropy and probabilistic neural network. In 2013 International Conference on Advanced Computer Science and Information Systems (ICACSIS). IEEE Conference Publication.

  • Manso, G. L., Knidel, H., Krohling, R. A., & Ventura, J. A. (2019). A smartphone application to detection and classification of coffee leaf miner and coffee leaf rust. Computer Vision and Pattern Recognition, 1-36.

  • McHugh, M. L. (2012). Interrater reliability: The kappa statistic. Biochemia Medica, 22(3), 276-282.

  • Miah, M. N. H., Yoshida, T., & Yamamoto, Y. (1997). Effect of nitrogen application during ripening period on photosynthesis and dry matter production and its impact on yield and yield components of semidwarf iniica rice varieties under water culture conditions. Soil Science and Plant Nutrition, 43(1), 205-217. https: //doi.org/10.1080/00380768.1997.10414728

  • Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., Wanichsombat, A., & Nillaor, P. (2019). IoT and agriculture data analysis for smart farm. Computers and Electronics in Agriculture, 156, 467-474. https: //doi.org/10.1016/j.compag.2018.12.011

  • Mwebaze, E., & Owomugisha, G. (2016). Machine learning for plant disease incidence and severity measurements from leaf images. In 2016 15th IEEE international conference on machine learning and applications (ICMLA). IEEE Conference Publication.

  • Nam, W. H., Kim, T., Hong, E. M., Choi, J. Y., & Kim, J. T. (2017). A wireless sensor network (WSN) application for irrigation facilities management based on Information and Communication Technologies (ICTs). Computers and Electronics in Agriculture, 143, 185-192. https: //doi.org/10.1016/j.compag.2017.10.007

  • Nasir, H., Aris, A. N., Lajis, A., Kadir, K., & Safie, S. I. (2018). Development of android application for pest infestation early warning system. In 2018 IEEE 5th International Conference on Smart Instrumentation, Measurement and Application (ICSIMA) (pp. 1-5). IEEE Conference Publication.

  • Norasma, C. Y. N., Fadzilah, M. A., Roslin, N. A., Zanariah, Z. W. N., Tarmidi, Z., & Candra, F. S. (2019). Unmanned aerial vehicle applications in agriculture. In IOP Conference Series: Materials Science and Engineering. Aceh, Indonesia. IOP Publishing.

  • Raza, S. M. H., Mahmood, S. A., Gillani, S. A., Hassan, S. S., Aamir, M., Saifullah, M., Basheer, M., Ahmad, A., Rehman, S. U., & Ali, T. (2019). Estimation of net rice production by remote sensing and multi source datasets. Sarhad Journal of Agriculture, 35(3), 955-965. https: //doi.org/10.17582/journal.sja/2019/35.3.955.965

  • Razali, M. H., Ismail, W. I. W., Ramli, A. R., Sulaiman, M. N., & Harun, M. H. (2009). Development of image based modeling for determination of oil content and days estimation for harvesting of fresh fruit bunches. International Journal of Food Engineering, 5(2), 1633-1637.

  • Rosle, R., Che’Ya, N. N., Roslin, N. A., Halip, R. M., & Ismail, M. R. (2019). Monitoring early stage of rice crops growth using normalized difference vegetation index generated from UAV. In IOP Conference Series: Earth and Environmental Science. Sulawesi, Indonesia. IOP Publishing.

  • Roy, P. S. (1989). Spectral reflectance characteristics of vegetation and their use in estimating productive potential. Plant Sciences, 99(1), 59-81. https: //doi.org/10.1007/BF03053419

  • Siahaan, A. P. U., & Wijaya, R. F. (2018). Smart farmer application in monitoring and learning of android-based rice cultivation. International Journal of Scientific Research in Science and Technology 4(11), 16-20. https: //doi.org/10.32628/IJSRST1840115

  • Simorangkir, G. D., Sarwoko, E. A., Sasongko, P. S., & Endah, S. N. (2018). Usability testing of corn diseases and pests detection on a mobile application. In 2018 2nd International Conference on Informatics and Computational Sciences (ICICoS). IEEE Conference Publication.

  • Stiglitz, R., Mikhailova, E., Post, C., Schlautman, M., Sharp, J., Pargas, R., Glover, B., & Mooney, J. (2017). Soil color sensor data collection using a GPS- enabled smartphone application. Geoderma, 296, 108-114. https: //doi.org/10.1016/j.geoderma.2017.02.018

  • Sushanth, G., & Sujatha, S. (2018). IOT based smart agriculture system. 2018 International Conference on Wireless Communications, Signal Processing and Networking (WiSPNET). IEEE Conference Publication.

  • Valdez-Morones, T., Pérez-Espinosa, H., Avila-George, H., Oblitas, J., & Castro, W. (2018). An android app for detecting damage on tobacco (Nicotiana tabacum L.) leaves caused by blue mold (Penospora tabacina Adam). In 2018 7th International Conference On Software Process Improvement (CIMPS). IEEE Conference Publication.

  • Vesali, F., Omid, M., Kaleita, A., & Mobli, H. (2015). Development of an android app to estimate chlorophyll content of corn leaves based on contact imaging. Computers and Electronics in Agriculture, 116, 211- 220. https: //doi.org/10.1016/j.compag.2015.06.012

  • Wang, F. M., Huang, J. F., & Lou, Z. H. (2011). A comparison of three methods for estimating leaf area index of paddy rice from optimal hyperspectral bands. Precision Agriculture, 12(3), 439-447. https: //doi.org/10.1007/s11119-010-9185-2

  • Watcharabutsarakham, S., Methasate, I., Watcharapinchai, N., Sinthupinyo, W., & Sriratanasak, W. (2016). An approach for density monitoring of brown planthopper population in simulated paddy fields. In 2016 13th International Joint Conference on Computer Science and Software Engineering (JCSSE) (pp. 1-4). IEEE Conference Publication.

  • Yusof, Z. M., Misiran, M., Baharin, N. F., Yaacob, M. F., Aziz, N. A. B. A., & Sanan, N. H. B. (2019). Projection of Paddy Production in Kedah Malaysia: A Case Study. Asian Journal of Advances in Agricultural Research, 1-6. https: //doi.org/10.9734/ajaar/2019/v10i330030

  • Yuzugullu, O., Marelli, S., Erten, E., Sudret, B., & Hajnsek, I. (2017). Determining rice growth stage with X-band SAR: A metamodel based inversion. Remote Sensing, 9(5), Article 460. https: //doi.org/10.3390/rs9050460

  • Zhang, F., & Cao, N. (2019). Application and research progress of geographic information system (GIS) in agriculture. In 2019 8th International Conference on Agro-Geoinformatics (Agro- Geoinformatics) (pp. 1-5). IEEE Conference Publication.

ISSN 0128-7680

e-ISSN 2231-8526

Article ID


Download Full Article PDF

Share this article

Recent Articles