MEASUREMENT OF LEAF AREA INDEX OF COTTON PLOTS BY INTERPRETATION OF DIGITAL PHOTOGRAPHS ABSTRACT

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1 MEASUREMENT OF LEAF AREA INDEX OF COTTON PLOTS BY INTERPRETATION OF DIGITAL PHOTOGRAPHS Bruno Rapidel 1, (1) CIRAD-CA/IER, Cotton Program. B.P. 1813, Bamako, Mali ABSTRACT To help with the diagnostic of the Malian Cotton small-scale farmers fields, an inexpensive means is needed to measure the Leaf Area Index. A simple method is proposed and tested. It rests on the interpretation of digital photographs, taken with any commercially available camera. The photographs are taken from above the rows. White blankets on the soil are used to facilitate the distinction between the leaves and the ground, as well as to define and standardize the frame of the image. The framed photographs are then contrasted and transformed into bitmap images. The proportion of black points, Index of Black (IB), calculated with a simple program written on a shareware is used as an indicator of LAI. This indicator was tested on 3 trials in 2002, sown at different densities. The LAI was measured with an independent method on the same plots. The results of the comparison between the two methods show that the IB is a good LAI indicator as long as LAI is less than 2. Above this value, the IB saturates. A solution is proposed to increase the validity range of this indicator. INTRODUCTION In southern Mali, cotton is a very important cash-crop for small-scale farmers. Since the beginning of the 90 s, the yields are steadily decreasing (IER/CMDT/OHVN, 1998). It is thought that the LAI (Leaf Area Index) of the cotton fields, in relation or not with planting densities, are lower than optimal. To test this assumption, LAI surveys throughout the Malian cotton growing area are needed. They will permit the identification of the locations where the planting densities could be increased to get higher yields. The methods available to measure LAI are either too expensive and delicate for use in tropical poor countries (LICOR, Hicks and Lascano, 1995), or destructive and time-consuming (extracting and weighting dried leaves of a plot and Specific Leaf Area (SLA) measurements (Heitholt et al., 1992)). We present an attempt to relate LAI measurements to interpretation of photographs taken by commonly available digital camera, an inexpensive but expedite method, well suited to our restrictions. MATERIAL AND METHODS The data are coming from three trials, each in one location in southern Mali at N Tarla (average rainfall about 800 mm per year), Sotuba (900mm) and Farako (1100 mm), in Planting densities in the three trials varied from 35,000 to 160,000 pl.ha -1, while row were 0.6 or 0.8 m apart. At different dates, measurements were performed on the same plants, by two methods, our photographic method and an indirect way presented below, at three locations inside each plot unit. At each date, the values from the three locations in a plot were averaged according to each method, to give a representative LAI value for the plot unit. The values obtained by the two independent methods were then compared. Photography device and image interpretation

2 The photographs were taken with a commercial digital camera (Canon IXUS 300), as shown on Fig. 1. The white blankets on the soil were used to facilitate the distinction between the leaves and the ground, as well as to define and standardize the frame of the image. The photographs taken in the field (Fig. 2) were cut to the white frame, contrasted and transformed into bitmap images (Fig. 3). The bitmap images were then interpreted with a simple program written on a shareware software (BMP Wizard 1.81) to calculate the proportion of black point on the image, called Index of Black (IB). LAI measurements The number of leaves, height and basal diameter of the main stem were measured on three independent plants in each plot unit. These plants were then removed from the field, their leaves were oven-dried and weighted. Their SLA was measured. Relations were then established, per date of measurement and per trial, between the leaf area of each extracted plant and the volume of the main stem (supposed conical) and the number of the leaves. Multiple correlation coefficients between measured and calculated values were always above 0.9. At each date and for each trial, the equations were then used to calculate the leaf area of each plant present on the photograph, on the basis of its measured diameter, number of leaves and height. The measurement of the planting density allowed the calculation of the LAI of each scene photographed. RESULTS AND DISCUSSION The relation between measured LAI and observed IB in each experimental plot is shown on Fig.4. While IB values are lower than 0.85 (or LAI lower than 2), IB can be considered as a good LAI indicator. Above this value, no relation appears between IB and LAI (important LAI variations are noticed within a short range of IB values). A curve was fitted for IB values below 0.85 (Fig. 4). It has two different expressions depending on whether the IB is inferior or superior to 0.5: If IB<0.5, LAI= IB IB If 0.5<IB<0.85, LAI= e2.705 IB The LAI values calculated with these formulae are well correlated to measured values (r 2 =0.94). Discussion and prospects As shown in Fig. 4, the method seems accurate for LAI under 2. Whereas it seems useless for industrial crops assessment in temperate regions, where LAI at full vegetative growth is usually above 3, this rather inexpensive method can be useful for the diagnosis of cotton production in southern Mali, where maximal LAI of farmer fields lay generally below 3 and often below 2 (Traoré, 2002). The restricted LAI validity range of this method comes from the parallax bias due to the photograph taken above the center of the row which occults part of the inter-row. To improve the LAI validity range, two photographs can be taken, one centered on the row and one centered on one of the inter-rows. The averaged IB value of the two photographs could be used as an LAI indicator for LAI higher than 2. This method will be implemented during the campaign: in each experimental field, photographs will be taken in each plot, and LAI will be measured by an independent method in a sub sample of the plots, in the same locations. It will enable to both follow the LAI of each plot with this expedite method and validate this LAI measurement method on a sub sample of the plots.

3 Acknowledgments The author wish to thank Dr. P. Birnbaum, CIRAD-Forêts/IER, for his help with the interpretation of digital images and Dr. K. Mouline and A. Renou, CIRAD-CA, for their careful revision of the manuscript.

4 Photography device Digital camera 1.9 m Cotton plants Raw photograph White blankets Fig. 1: The way of taking the photographs in the fields Fig. 2: Raw photograph Fig. 3: Bitmap image processed

5 LAI «Validity range» Index of black N'Tarla 38 and 68 DAS Farako 63 DAS Sotuba 68 DAS Sotuba 81 DAS N'Tarla 98 and 128 DAS Farako 128 DAS Fig. 4: Relation between Index of Black and LAI (DAS: Days After Sowing) REFENRENCES BIBLIOGRAPHY HEITHOLT, J.J., PETTIGREW, W.T.; MEREDITH, W.R.J. Light interception and lint yield of narrow row cotton. Crop Science, v. 32,p , 1992 HICKS, S.K. and LASCANO, R.J.,. Estimating of leaf area index for cotton canopies using the LI-COR LAI 2000 plant canopy analyzer. Agronomy Journal, v. 87, p , IER/CMDT/OHVN. Réunion de concertation sur la baisse de rendement de la variété NTA 88-6 au cours de la campagne 97/98. Mémoire de réunion, N Tarla, Traoré, B. Diagnostic agronomique de l'impact des pratiques paysannes sur le rendement du coton à Katogo. Mémoire de fin d'étude, Institut Polytechnique Rural de Katibougou, Mali, p.

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