Precision agriculture in cotton: Definition of the optimal imaging resolution required for purple nutsedge detection Tal Miller, Liraz Cohen, Eldar Peleg, Matan Gilad and Anat Stein Western Galilee Regional Highschool Hanan Eizenberg Department of Weed Research, Newe Ya’ar Research Center, Agricultural Research Organization (ARO) The 2 nd International Conference of Novel and Sustainable Weed Management in Arid and Semi-Arid Agro Ecosystems September 6 th -10 th, Santorini, Greece

Introduction Purple nutsedge is a troublesome weed, causing severe damage in cotton Weeds may compete on resources such as water, light, space, nutrients etc.

The effect of purple nutsedge infestation on cotton biomass Purple nutsedge biomass (g m -2 ) Cotton biomass (g m -2 )

Precision agriculture approach Precision agriculture, specifically, site specific weed management is a modern approach for reducing herbicide rates This could be achieved by spraying herbicides only on weed patches based on the detection of the spatial distribution of weeds (and not on the entire field)

50% savings

66% savings

Weeds are easily detected visually because they are green plants on brown soil Several indexes were developed for this purpose How can we detect weeds grown in the field?

Normalized Difference Vegetation index (NDVI) NIR = Reflection in Near Infra Red (770  m) Red = Reflection in Red (660  m) It was reported in the literature that NDVI is highly correlated to vegetative growth, nitrogen and chlorophyll levels

NDVI image by NASA

NDVI of wheat field pre planting savings 50%

Wheat field pre planting

When NIR channel is not available, NGRDI index may be used:

The main goal of this study was to determine the optimal resolution required for the detection of purple nutsedge in cotton Research objectives

Specific objectives were: Detecting purple nutsedge on bare soil (inter rows) using NGRDI index Defining the threshold resolution for purple nutsedge detection Research objectives (cont.) High resolution RGB image (0.05 x 0.05 m per pixel)

Hypothesis We hypothesize that NGRDI values, greater than bare soil NGRDI (~0.01) represent vegetative growth, in our case purple nutsedge infestation

Experiments were performed in a commercial cotton field in the Jesreel Valley, Northern Israel Aerial images were captured at the same day of data collection Image resolution was 0.05 x 0.05 m per pixel Twenty-five plots were randomly selected for data collection Materials & Methods

Materials & Methods (cont.) Weed coverage (%) was visually estimated Purple nutsedge shoots were counted Plot locations were marked using a differential GPS (dGPS – sub-meter) Data were imported into a Geographical Information Software (GIS) software for advance analysis

Results 10m

Data processing Originally pixel size was 0.05 x 0.05 m Computing values of RGB channels Reducing the resolution by increasing the size of the pixels Re-computing values for the merged pixels by using the average value 0.45 m 1.70 m

Imaging GIS Observations Fixing imageMarking location by dGPS Increasing pixel size Validation Determining threshold value NoYes Creating multi-layer map Choosing plots Color channels analysis Computing NGRDI index Is index value higher than ground value?

Relations between pixel size and NGRDI index NGRDI Pixel size (cm 2 )

Conclusions The threshold resolution for purple nutsedge detection from an aerial RGB image is 0.5 X 0.5 m per pixel (using NGRDI index) Although NIR imaging is separating better weed from soil, using RGB channels (NGRDI index) is much cheaper and available for weed detection Weed coverage that causes damage to cotton could be detected with a resolution of 0.5 x 0.5 m per pixel

Acknowledgment EWRS for supporting my trip Anat Stein for her assistance and motivation Research team in the Newe Ya’ar Research Center, Department of Weed Research Dr. Yafit Cohen, Sensing, Information and Mechanization Engineering, ARO Jimmie Ipen, field crops action, Alonim Shay Mey-tal, Agam LTD The school, for support and resources

Thank you for your attention!