Analysis of the utilization of rice seeds of improved variety (Inpari 32) in Indramayu District, West Java

Agustian, Adang; Aldillah, R; Nurjati, E; Yaumidin, U K; Muslim, Chairul; Ariningsih, E; Rachmawati, R R

doi:10.1088/1755-1315/1114/1/012098

Cited by 2 publications

(1 citation statement)

References 6 publications

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“…Additionally, we examined the effectiveness of ensemble algorithms, such as AdaBoost, gradient boosting, and a combination of high-performing algorithms as stacked learners. The performance of these algorithms was assessed by classifying three rice varieties, INPARI-32, INPARI-33, and INPARI-43 [39][40][41], which are high-yield rice varieties developed by the Indonesian Agency for Agricultural Research and Development (IAARD) in collaboration with the International Rice Research Institute (IRRI), at three different growth stages: six, nine, and twelve weeks after planting (WAP). This study aimed to determine the most effective algorithm for accurately classifying rice varieties, considering features such as reflectance from multispectral bands and vegetation indices.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning for Precise Rice Variety Classification in Tropical Environments Using UAV-Based Multispectral Sensing

Wijayanto,

Junaedi,

Sujaswara

et al. 2023

AgriEngineering

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An efficient assessment of rice varieties in tropical regions is crucial for selecting cultivars suited to unique environmental conditions. This study explores machine learning algorithms that leverage multispectral sensor data from UAVs to evaluate rice varieties. It focuses on three paddy rice types at different ages (six, nine, and twelve weeks after planting), analyzing data from four spectral bands and vegetation indices using various algorithms for classification. The results show that the neural network (NN) algorithm is superior, achieving an area under the curve value of 0.804. The twelfth week post-planting yielded the most accurate results, with green reflectance the dominant predictor, surpassing the traditional vegetation indices. This study demonstrates the rapid and effective classification of rice varieties using UAV-based multispectral sensors and NN algorithms to enhance agricultural practices and global food security.

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Section: Introductionmentioning

confidence: 99%

Machine Learning for Precise Rice Variety Classification in Tropical Environments Using UAV-Based Multispectral Sensing

Wijayanto,

Junaedi,

Sujaswara

et al. 2023

AgriEngineering

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A new concept in assessing adaptability index for superior potential cropping intensity in early-maturing rice

Anshori,

Musa,

Farid

et al. 2024

Front. Sustain. Food Syst.

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Implementing a cropping intensity program with rice cultivation four times a year (CI 400) can be achieved using early maturing varieties of rice. However, this development needs to pay attention to the adaptability of the varieties planted to ensure successful implementation. The adaptability approach is a combination of assessing stability and productivity potential. This concept has been developed and applied in several studies, including research on rice. However, this approach is considered less comprehensive because it is non-parametric and only focuses on one stability analysis. Therefore, a systematic integration of various stability analyses, including index methods, is needed to comprehensively assess adaptability, particularly for early-maturing rice in South Sulawesi. This region is characterized by a dynamic climate zone and is one of the top four highest rice producers in Indonesia. Meanwhile, this study aims to develop a comprehensive adaptability index and select the best early-maturing rice varieties, especially in South Sulawesi. The investigation was conducted in Bone, Soppeng, and Gowa over two seasons using a nested randomized complete block design, with organized replications in each environment (location-season). Additionally, there was a significant focus on the application of five early-maturing and two check rice varieties, with each factor repeated three times at each location, totaling 126 experimental units. The results showed that the adaptability index, by combining stability rank accumulation with yield min max standardization, was effective at assessing the yield potential and stability of early-maturing rice varieties in supporting CI 400. Inpari 13 had the best index value at 0.55, followed by Cakrabuana at 0.31; hence both were recommended as adaptive early-maturing rice varieties, especially in South Sulawesi.

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Analysis of the utilization of rice seeds of improved variety (Inpari 32) in Indramayu District, West Java

Cited by 2 publications

References 6 publications

Machine Learning for Precise Rice Variety Classification in Tropical Environments Using UAV-Based Multispectral Sensing

Machine Learning for Precise Rice Variety Classification in Tropical Environments Using UAV-Based Multispectral Sensing

A new concept in assessing adaptability index for superior potential cropping intensity in early-maturing rice

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