Open Journal Systems

Embracing smart irrigation management techniques empowers growers to irrigate with greater efficiency, thereby promoting sustainable agricultural production. In this context, growers often rely on crop evapotranspiration (ETc) as a key factor in making informed irrigation decisions, underscoring the significance of accurately determining and spatially mapping crop water status. Technological progress, exemplified by the emergence of unmanned aerial vehicles (UAVs), has brought about a revolutionary shift in agricultural monitoring. UAV platforms can capture high-resolution images with centimeter-level spatial accuracy and offer higher temporal coverage compared to satellite imagery. Considering these advancements, this study introduces a robust method for classifying water stress in cotton using a compact UAV platform and convolutional neural networks (CNN). The experiment was conducted at the USDA-ARS Cropping Systems Research Laboratory (CSRL) in Lubbock, Texas, where the cotton field was divided into 12 drip zones. The study included three replications to evaluate four irrigation treatments: “rainfed”, “full irrigation”, “percent deficit of full irrigation”, and “time delay of full irrigation”. The results demonstrated that the CNN model successfully classified the cotton water stress using the UAV-based RGB image, achieving an overall best prediction accuracy of approximately 91%. By segmenting the original cotton images into separate canopy and soil areas using morphological image processing methods, the authors also isolated and analyzed the individual contributions of these components to cotton water stress. Additionally, a random forest classifier revealed the relative importance of different image features in the classification process through feature importance analysis. These findings highlighted the state-of-the-art performance of the proposed system in cotton water stress classification and provided valuable insights into the key image features contributing to accurate classification. The authors concluded that integrating UAV-based RGB imagery and CNN models had great potential for assessing water stress in cotton.

A new approach to measure spatial variability of soil parameters and field technique to test-value specific fertilizer

Information on the distribution of soil properties is important to know the status of nutrients in the soils based on which fertilizer nutrients are recommended. Given the variability of nutrients in the soils, making a site-specific fertilizer recommendation seems to be a compelling work. To determine the spatial variability of soil nutrients and to make judicious and precise fertilizer recommendations, new measures are designed with this study. These measures are tested against the soil samples (n = 43) for total nitrogen (N), organic matter (OM), phosphorus (P2O5), and potassium (K2O) in the study area. The descriptive statistical analysis indicated an average of low nitrogen and organic matter, while phosphorus was found to be very high and the level of potassium was high. The spread of nutrients across the data sets, however, included low, medium, high, and very high levels of ratings. The Deviation Square Index was developed and applied for the variability measurement and found that the largest variation was with phosphorus distribution, followed by potassium, nitrogen, and organic matter. The coefficient of variation (CV%) analysis also exhibited similar trends in nutrient distributions. Nitrogen was the main determinant explaining the variations in rice yield, while phosphorus and potash were negatively related to the yield. An index of fertilizer nutrient recommendation called Test-Value Specific Dose (TVSD) was developed and used to calculate the nutrient recommendation for each sampled location. This new method gave easy and more accurate doses of fertilizer over the blanket recommendation to fit the variations across the soil samples.

Using satellite image data to identify rice varieties through linear spectral unmixing method (case study: Karangjati Sub District, Ngawi Regency)

Moch Rafli Kusoiry, Lalu Muhamad Jaelani, Hartanto Sanjaya

Article ID: 2538
Vol 5, Issue 2, 2024

DOI: https://doi.org/10.54517/ama.v5i2.2538
VIEWS - 1312 (Abstract)

Download PDF

Abstract

Remote sensing technology has increasingly emerged as a potent tool for precision agriculture, particularly in facilitating the mapping and monitoring of crops on a large scale. An application of this technology is the identification of different types of rice by analyzing the pixels acquired in satellite images. Regrettably, the pixels in the image have been mixed from different recorded items. Therefore, they have the potential to influence the outcome of the identification. An effective approach to addressing this problem is to employ the linear spectral unmixing (LSU) technique. The LSU approach quantifies the ratio of pure objects in every pixel of an image by utilizing the spectral value associated with the endmember of the rice variety. The investigation was carried out in the Karangjati District during the generative stage (70 ± DAP) of the rice planting season. The data indicates that the dominant variety is Inpari 32 HDB. The data validation tests, which involved the use of a confusion matrix and Kappa analysis, resulted in an overall accuracy rate of 85.48% and a Kappa analysis score of 70.6%.

Keywords

endmember; Karangjati; linear spectral unmixing; precision agriculture; remote sensing; rice varieties

References

1. Azhar HM, Susilastuti D. Analisis Keragaman Hayati Tanaman Padi (Oryza sativa, L). AGRISIA - J. Ilmu-Ilmu Pertan. 2017; 9(2).

2. Ningsih F. Identification of morphological and agronomic characters of some local paddy rice cultivars from the north kampar sub-district of kampar district in the vegetative phase (Indonesian). Universitas Islam Negeri Sultan Syarif Kasim, Riau; 2018.

3. Supriyanti A, Supriyanta, Kristamtini. Characterization of Twenty Local Rice (Oryza Sativa L.) in Yogyakarta Special Region. Vegetalika. 2015; 4(3): 29-41.

4. Pratiwi SH. Growth and Yield of Rice (Oryza sativa L.) on various planting pattern and addition of organic fertilizers. Gontor AGROTECH Science Journal. 2016; 2(2). doi: 10.21111/agrotech.v2i2.410

5. Sari VD, Sukojo BM. Analysis of Rice Production Estimation Based on Growth Phase and Autoregressive Integrated Moving Average (Arima) Forecasting Model Using Landsat 8 Satellite Image (Case Study: Bojonegoro Regency) (Indonesian). Geoid. 2015; 10(2): 194. doi: 10.12962/j24423998.v10i2.828

6. Cipta IM, Jaelani LM, Sanjaya H. Identification of Paddy Varieties from Landsat 8 Satellite Image Data Using Spectral Unmixing Method in Indramayu Regency, Indonesia. ISPRS International Journal of Geo-Information. 2022; 11(10): 510. doi: 10.3390/ijgi11100510

7. Congalton RG. Remote Sensing and Image Interpretation. 7th Edition. Photogrammetric Engineering & Remote Sensing. 2015; 81(8): 615-616. doi: 10.14358/pers.81.8.615

8. Hidayat I, Wijayanto H, Afendi FM. Evaluation of Paddy Production Mesurement in Indonesia. IOP Conference Series: Earth and Environmental Science. 2018; 187: 012035. doi: 10.1088/1755-1315/187/1/012035

9. Liu M, Liu X, Wu L, et al. A Modified Spatiotemporal Fusion Algorithm Using Phenological Information for Predicting Reflectance of Paddy Rice in Southern China. Remote Sensing. 2018; 10(5): 772. doi: 10.3390/rs10050772

10. Sabins FF. Remote Sensing: Principles and Interpretation, 3rd ed. Waveland Press; 2007.

11. Zhao R, Li Y, Chen J, et al. Mapping a Paddy Rice Area in a Cloudy and Rainy Region Using Spatiotemporal Data Fusion and a Phenology-Based Algorithm. Remote Sensing. 2021; 13(21): 4400. doi: 10.3390/rs13214400

12. Parsons AJ. Principles of Remote Sensing. By P. J. Curran. (London: Longman, 1985) [Pp. 260.] Price £1195. International Journal of Remote Sensing. 1985; 6(11): 1765-1765. doi: 10.1080/01431168508948322

13. Wei J, Wang X. An Overview on Linear Unmixing of Hyperspectral Data. Mathematical Problems in Engineering. 2020; 2020: 1-12. doi: 10.1155/2020/3735403

14. Yin Z, Yang B. Unsupervised Nonlinear Hyperspectral Unmixing with Reduced Spectral Variability via Superpixel-Based Fisher Transformation. Remote Sensing. 2023; 15(20): 5028. doi: 10.3390/rs15205028

15. Gandharum Mubekti L, Sanjaya H, Sadmono H, Wibowo BS. Penerapan Metode Spectral Linear Unmixing Pada Citra Landsat TM dan Data Spektrometer Untuk Memetakan Tanaman Padi Terserang Penyakit Hawar Daun Bakteri. In: Pertemuan Ilmiah Tahunan XX dan Kongres MAPIN; 2015.

16. Rivani AP, Jaelani LM, Sumargana L. Identifikasi Varietas Jagung dari Data Citra Satelit Menggunakan Metode Linier Spectral Unmixing (Studi Kasus: Kabupaten Ngawi). Geoid. 2023; 19(1): 106. doi: 10.12962/j24423998.v19i1.18749

17. Sanjaya H, Sukotjo BM, Jaelani LM, et al. Utilizing a Spectroradiometer to Build a Spectral-Library of Rice Leaves. 2022 IEEE Asia-Pacific Conference on Geoscience, Electronics and Remote Sensing Technology (AGERS). Published online December 21, 2022. doi: 10.1109/agers56232.2022.10093263

18. Gorelick N, Hancher M, Dixon M, et al. Google Earth Engine: Planetary-scale geospatial analysis for everyone. Remote Sensing of Environment. 2017; 202: 18-27. doi: 10.1016/j.rse.2017.06.031

19. Rouse JW, Haas RH, Schell JA, Deeering D. Monitoring vegetation systems in the Great Plains with ERTS (Earth Resources Technology Satellite). In: Third Earth Resources Technology Satellite-1 Symposium; 1973.

20. Rouse JW Jr, Haas R, Schell J, Deering D. Monitoring vegetation systems in the Great Plains with ERTS. NASA Spec. Publ; 1974.

21. Xu H. Modification of normalised difference water index (NDWI) to enhance open water features in remotely sensed imagery. International Journal of Remote Sensing. 2006; 27(14): 3025-3033. doi: 10.1080/01431160600589179

22. Keshava N. A Survey of Spectral Unmixing Algorithms. Lincoln Lab. J. 2003; 14(1): 55-78.

23. Keshava N, Mustard JF. Spectral unmixing. IEEE Signal Processing Magazine. 2002; 19(1): 44-57. doi: 10.1109/79.974727

24. Plaza A, Plaza J. Parallel implementation of linear and nonlinear spectral unmixing of remotely sensed hyperspectral images. High-Performance Computing in Remote Sensing. Published online October 6, 2011. doi: 10.1117/12.897326

25. Congalton RG. A review of assessing the accuracy of classifications of remotely sensed data. Remote Sens. Environ. 1991; 37(1): 35-46. doi: 10.1016/0034-4257(91)90048-B

26. Anderson JR, Hardy EE, Roach JT, et al. A land use and land cover classification system for use with remote sensor data. Professional Paper. Published online 1976. doi: 10.3133/pp964

27. Landis JR, Koch GG. The Measurement of Observer Agreement for Categorical Data. Biometrics. 1977; 33(1): 159. doi: 10.2307/2529310

28. Kosasih D, Buce Saleh M, Budi Prasetyo L. Visual and Digital Interpretations for Land Cover Classification in Kuningan District, West Java. Jurnal Ilmu Pertanian Indonesia. 2019; 24(2): 101-108. doi: 10.18343/jipi.24.2.101

Refbacks

There are currently no refbacks.

Copyright (c) 2024 Moch Rafli Kusoiry, Lalu Muhamad Jaelani, Hartanto Sanjaya

License URL: https://creativecommons.org/licenses/by/4.0/

This site is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).

Editor-in-Chief

Prof. Zhengjun Qiu

Zhejiang University, China

Honorary Editor-in-Chief

Cheng Sun

Academician of World Academy of Productivity Science; Executive Chairman, World Confederation of Productivity Science China Chapter, China

Processing Speed

- - - ＜5 days from submission to initial review decision;
  - 64% acceptance rate

News & Announcements

2024-09-11

Smart agriculture is becoming a reality!

Modern agricultural technology is evolving rapidly, with scientists collaborating with leading agricultural enterprises to develop intelligent management practices. These practices utilize advanced systems that provide tailored fertilization and treatment options for large-scale land management.

2024-05-01

Notice on the prohibition of using AI techniques to generate papers!

This journal values human initiative and intelligence, and the employment of AI technologies to write papers that replace the human mind is expressly prohibited. When there is a suspicious submission that uses AI tools to quickly piece together and generate research results, the editorial board of the journal will reject the article, and all journals under the publisher's umbrella will prohibit all authors from submitting their articles.

Readers and authors are asked to exercise caution and strictly adhere to the journal's policy regarding the usage of Artificial Intelligence Generated Content (AIGC) tools.

2024-01-14

We are recruiting Editorial Team Members

Advances in Modern Agriculture (AMA) is looking for members of the Editorial Board.

2023-12-28

Notice on the change of publication frequency

The publication frequency of Advances in Modern Agriculture is Quarterly since 2024, that means the journal will publish 4 issues per year from 2024 onwards.

More Announcements...

Journal Center

Asia Pacific Academy of Science Pte. Ltd. (APACSCI) specializes in international journal publishing. APACSCI adopts the open access publishing model and provides an important communication bridge for academic groups whose interest fields include engineering, technology, medicine, computer, mathematics, agriculture and forestry, and environment.

more

Article Tools

Indexing metadata

How to cite item

Review policy