Android-based rice leaf color analyzer for estimating the needed amount of nitrogen fertilizer

2015 ◽  
Vol 116 ◽  
pp. 228-233 ◽  
Author(s):  
Yuttana Intaravanne ◽  
Sarun Sumriddetchkajorn
2014 ◽  
Vol 53 ◽  
pp. 179-184 ◽  
Author(s):  
Sarun Sumriddetchkajorn ◽  
Yuttana Intaravanne

2018 ◽  
Vol 6 (2) ◽  
pp. 81-86
Author(s):  
Purushottam Subedi ◽  
Salina Panta

Proper application of nitrogen (N) fertilizer is vital to improve the growth and grain yield of rice crop. As there prevails more aerobic period in direct seeded rice, nitrogen loss is generally more in such environment. Therefore, nitrogen recommendation for direct seeded rice is slightly higher (22.5-30 Kg ha-1) than that under the transplanted rice. Insufficient and/or inappropriate nitrogen fertilizer application is highly critical to the crops. Optimal nitrogen management strategies aim at matching the nitrogen fertilizer supply to the actual crop demand. Leaf color is generally used as a visual and subjective indicator of the rice crop need for nitrogen fertilizer. The Leaf Color Chart is a simple and inexpensive tool for real time nitrogen management in rice. It helps farmers to improve their decision-making process in nitrogen management. It provides the idea of when and how much nitrogen fertilizer to apply based on relative greenness of the rice leaf. In overall, LCC based nitrogen management improves productivity and profitability of the rice crop by nitrogen saving and ensuring its higher use efficiency.Int. J. Appl. Sci. Biotechnol. Vol 6(2): 81-86


2005 ◽  
Vol 97 (3) ◽  
pp. 949-959 ◽  
Author(s):  
M. Murshedul Alam ◽  
J. K. Ladha ◽  
S. Rahman Khan ◽  
Foyjunnessa ◽  
Harun-ur-Rashid ◽  
...  

CCIT Journal ◽  
2020 ◽  
Vol 13 (2) ◽  
pp. 168-174
Author(s):  
Khoirul Umam ◽  
Eko Heri Susanto

Leaf Color Chart (LCC) is a measurement tool that can be used to measure the color intensity of rice leaves. The function of these measurements is to find out how many doses of fertilizer are needed by rice plants. However, readings made by human vision have a high level of subjectivity and risk of error. Therefore we need a method that can minimize errors and the level of subjectivity. One method that can be done is to classify the green color of rice leaves using LAB color space. Rice leaf image taken using a smartphone device is then extracted in RGB format. The color is then converted to LAB color space and then compared to the standard green color in the LCC. The comparison results are then used to classify the colors. The testing results show that the method has the value of accuracy, average precision, and average recall of 54.74%, 54.44%, and 51.16% respectively. Therefore the method can only classify correctly half of the data testing.


Author(s):  
Torikul Islam ◽  
Rafsan Uddin ◽  
Yeasir Arefin ◽  
Md Shafiuzzaman ◽  
Md. Alam ◽  
...  

Agronomie ◽  
2002 ◽  
Vol 22 (7-8) ◽  
pp. 789-800 ◽  
Author(s):  
Monika Langmeier ◽  
Emmanuel Frossard ◽  
Michael Kreuzer ◽  
Paul Mäder ◽  
David Dubois ◽  
...  

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