**4.3 Estimation of protein content**

264 Remote Sensing of Biomass – Principles and Applications

0.85 0.88 0.91 0.94 0.97 1

The rice crop was harvested from three sample areas of 4 m2 from each plot, 102 days after sowing for this experiment. The moisture content (% w.b.) at the time of weighing was estimated using a field moisture meter (Kett PM600, Ohta-Ku, Tokyo, Japan). The yield of each plot (100 m2 area) was estimated as the average of three sampled areas and converted to a ton-per-hectare area using the following equation. Rice yield was estimated at 14%

<sup>1</sup> (100 ) 10000 ( ) 86 1000

Rice yield, ranged from as low as 1.88 ton ha-1 (0 kg ha-1 N) to 3.68 ton ha-1 (132 kg ha-1 N) based on a 14% MC, illustrates the effectiveness of the fertilizer treatment rates on rice yield (Table 4). The crop yield variation was also tested for statistical significance (Johnson and Bhattacharyya, 2001). Yield data between the treatments showed significant differences at the 0.10 and 0.05 levels, whereas differences were not significant among the replicates

0 kg ha-1 1.88 1.97 1.64 1.83 33 kg ha-1 2.13 2.87 3.28 2.76 66 kg ha-1 2.78 2.70 3.44 2.97 99 kg ha-1 2.37 3.85 3.52 3.25 132 kg ha-1 3.52 3.36 3.68 3.52

*A*

Replicate Average 1 2 3

(4)

*MC RW Yield ton ha*

**NDVI LARS**

y = 31.851x - 23.837 <sup>2</sup> R = 0.7598 RMSE = 0.5987

0.00

Fig. 6. Estimation of biomass with NDVILARS values

MC = moisture content (% wet basis)

Table 4. Rice yield (ton ha-1) of the experimental plots

RW = weight of rice (kg) A = harvested area (m2)

2.00

Rep. 1 Rep. 2 Rep. 3

moisture content (MC) for each treatment (Field crop report, 1998).

4.00

6.00

**-1**

**Biomass (tons ha )**

**4.2 Estimation of rice yield** 

Where,

N Rate Treatment 8.00

Protein content is one of the major food nutrients to determine quality of the food-grain. It could be measured as the total available nitrogen content in the food stuff (Kennedy, 1995). The rice was powdered and sieved before testing for total nitrogen with standard method. The linear model of total nitrogen against NDVILARS (with r2 = 0.591, Figure 8) showed positive relationships, and would be useful to the farmers, as they can get idea of quality of rice grain well in advance, at booting stage (from the image taken during booting stage).

Fig. 8. Estimation of protein content with NDVILARS values.

Rice Crop Monitoring with Unmanned Helicopter Remote Sensing Images 267

contamination; lower pesticide application rate and aerial spray can improve efficiency for

A radio-controlled helicopter-based LARS system can be used to acquire multispectral images over a rice canopy to estimate rice yield. The study indicated that the LARS platform could substitute for satellite-based and costly airborne remote sensing system. Images are obtained successfully by the multispectral camera mounted on the radio-controlled helicopter at a height of 20 m over rice plots. Rice yield and total biomass were found to be significantly different at the 0.05 and 0.1 significance levels, respectively, under different N treatment regimes. The relationship between NDVILARS and NDVISPECTRO (r2 = 0.897, RMSE = 0.012) shows the applicability of LARS sensor-based images for estimating NDVI values, which varied over the five levels of applied N. A linear regression model shows a good fit (r2 = 0.728, RMSE = 0.458 ton ha-1) for estimating total biomass for rice using LARS imagebased NDVI values. A linear model (r2 = 0.760, RMSE = 0.598 ton ha-1) indicates that rice yield could also be predicted with NDVI values derived from LARS images. The protein content can be positively estimated well in advance to actual crop harvesting. The regression model procedure outlined herein can be followed for larger rice fields by recording crop

Abdullah, N., Y. W. Ho, and S. Jalaludin. 1992. Microbial colonization and digestion of feed

Aber, J. S., K. Aaviksoo, E. Karofeld, and S. W. Aber. 2002. Patterns in Estonian bogs as depicted in color kite aerial photographs. *Suo (Mires and Peat)* 53(1): 1-15. Alchanatis, V., Z. Schmilovitch, and M. Meron. 2005. In-field assessment of single-leaf nitrogen status by spectral reflectance measurements. *Precision Agric.* 6(1): 25-39. Alvaro, F., L. F. García del Moral, and C. Royo. 2007. Usefulness of remote sensing for the

Amoroso, L., and R. Arrowsmith. 2000. Balloon photography of brush fire scars east of

Auernhammer, H., M. Demmel, F. X. Maidl, U. Shmidnalter, T. Schneider, and P. Wagner.

Buick, R. 2002. GPS guidance: Making an informed decision. In Proc. of 6th International

Bausch, W., and J. A. Delgado. 2005. Impact of residual soil nitrate on in-season nitrogen

Beerwinkle, K. R. 2001. An automatic capture-detection, time-logging instrumentation system for boll weevil pheromone traps. *Applied Eng. in Agric*. 17(6): 893-898.

time application. ASAE Paper No. 991150. St. Joseph, Mich.: ASAE.

materials in cattle and buffaloes: II. Rice straw and palm press fibre. *Asian-*

assessment of growth traits in individual cereal plants grown in the field. *Intl. J.* 

Carefree, Arizona. Tempe, Ariz.: Arizona State University, Department of

1999. An on-farm communication system for precision farming with nitrogen real-

Conference on Precision Agriculture 1979-2004. CD-ROM, P.C.Robert et al.

applications to irrigated corn based on remotely sensed assessments of crop

prevention and cure pests in agriculture and forestry.

input rates and acquiring LARS images.

*Australasian J. Animal Sci.* 5(2): 329-335.

*Remote Sensing* 28(11): 2497-2512.

Madison,Wisc, ASA, CSSA, and SSA.

nitrogen status. *Precision Agric.* 6(6): 509-519.

Geological Sciences.

**6. Conclusions** 

**7. References** 
