Estimation of nitrogen status and yield of rice crop using unmanned aerial vehicle equipped with multispectral camera

Goswami, Suraj; Choudhary, Sudesh Singh; Chatterjee, Chandranath; Mailapalli, Damodhara Rao; Mishra, Ashok K.; Raghuwanshi, N. S.

doi:10.1117/1.jrs.15.042407

Cited by 9 publications

(1 citation statement)

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“…Nitrogen (N) merupakan salah satu unsur hara esensial yang dibutuhkan untuk pertumbuhan tanaman padi, dan karenanya pemberian N harus diterapkan secara efisien untuk mencapai hasil yang optimal (Goswami et al, 2021). Kandungan N di tanaman padi diyakini mampu memberikan gambaran tentang status pertumbuhan seperti jumlah anakan (Patti S et al, 2013) dan prediksi hasil gabah yang akan dipanen (Yang et al, 2014;Zhang et al, 2019).…”

Section: Pendahuluanunclassified

Drone-Based Vegetation Index Analysis to Estimated Nitrogen Content on The Rice Plantations

Adzima¹,

Neswati

Laban³

et al. 2022

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Nitrogen (N) merupakan salah satu unsur hara esensial yang dibutuhkan untuk pertumbuhan tanaman padi. Oleh karena itu, pemupukan N harus diterapkan secara efisien untuk mencapai hasil yang optimal. Berbagai metode telah digunakan untuk menghitung kandungan N pada tanaman padi seperti analisis jaringan dan penggunaan teknologi soil plant analysis development (SPAD). Teknologi ini masih memiliki kekurangan, salah satunya adalah efisiensi waktu. Dibutuhkan teknologi pendukung lainnya untuk mendukung analisis pertanian secara presisi dengan cepat, seperti penggunaan Unmaned Aerial Vehicles (UAV). Tujuan penelitian ini yaitu menganalisis kandungan N pada tanaman padi menggunakan indeks vegetasi berbasis UAV dan menganalisis perbandingan akurasi pengukuran N antara klorofil SPAD dengan indeks vegetasi tanaman padi. Penelitian ini menggunakan metode survei dan uji laboratorium yang didasari atas beberapa pendekatan, yaitu analisis fisiologi fotosintesis, analisis jaringan daun, dan analisis indeks vegetasi menggunakan UAV. Berdasarkan hasil penelitian, ditemukan nilai indeks vegetasi NDVI yang berkolerasi kuat dengan nilai jaringan N daun dan hasil pengukuran klorofil SPAD. Sedangkan hasil pengujian akurasi, hasil pengukuran klorofil dengan SPAD (CI A) memiliki akurasi yang lebih baik dibandingkan dengan indeks vegetasi NDVI. Nilai r antara CI A – N daun dengan NDVI – N daun tidak menunjukkan perbedaan yang singnifikan. Selain itu, hasil korelasi menunjukkan variabel jaringan N Daun (r=0.83), CI A (r=0.88), CI B (r=81), dan CI TOT (r=0.87) memiliki hubungan korelasi yang sangat tinggi terhadap NDVI. Hal ini menunjukkan hubungan yang searah antara variabel sehingga variabel NDVI dapat dijadikan sebagai pertimbangan untuk mengetahui klorofil pada tanaman yang diteliti.

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Section: Pendahuluanunclassified

Drone-Based Vegetation Index Analysis to Estimated Nitrogen Content on The Rice Plantations

Adzima¹,

Neswati

Laban³

et al. 2022

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Drone‐based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review

Gano,

Bhadra,

Vilbig

et al. 2024

The Plant Phenome Journal

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Over the last decade, the use of unmanned aerial vehicles (UAVs) for plant phenotyping and field crop monitoring has significantly evolved and expanded. These technologies have been particularly valuable for monitoring crop growth and health and for managing abiotic and biotic stresses such as drought, fertilization deficiencies, disease, and bioaggressors. This paper provides a comprehensive review of the progress in UAV‐based plant phenotyping, with a focus on the current use and application of drone technology to gain information on plant growth, development, adaptation, and yield. We reviewed over 200 research articles and discuss the best tools and methodologies for different research purposes, the challenges that need to be overcome, and the major research gaps that remain. First, the review offers a critical focus on elucidating the distinct characteristics of UAV platforms, highlighting the diverse sensor technologies employed and shedding light on the nuances of UAV data acquisition and processing methodologies. Second, it presents a comprehensive analysis of the multiple applications of UAVs in field phenotyping, underscoring the transformative potential of integrating machine learning techniques for plant analysis. Third, it delves into the realm of machine learning applications for plant phenotyping, emphasizing its role in enhancing data analysis and interpretation. Furthermore, the paper extensively examines the open issues and research challenges within the domain, addressing the complexities and limitations faced during data acquisition, processing, and interpretation. Finally, it outlines the future trends and emerging technologies in the field of UAV‐based plant phenotyping, paving the way for innovative advancements and methodologies.

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Assessment of Damage Due to Grain Discoloration Disease in Paddy Crop Using High-Resolution UAV Imagery

Biswal,

Chatterjee,

Mailapalli

2024

J Indian Soc Remote Sens

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Estimation of nitrogen status and yield of rice crop using unmanned aerial vehicle equipped with multispectral camera

Cited by 9 publications

References 0 publications

Drone-Based Vegetation Index Analysis to Estimated Nitrogen Content on The Rice Plantations

Drone-Based Vegetation Index Analysis to Estimated Nitrogen Content on The Rice Plantations

Drone‐based imaging sensors, techniques, and applications in plant phenotyping for crop breeding: A comprehensive review

Assessment of Damage Due to Grain Discoloration Disease in Paddy Crop Using High-Resolution UAV Imagery

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