Using UAV's for improved cashew nut production in Benin Thierry Olatounde Yabbi Hermann, Monitoring & Evaluation, Accountability and Learning – CRS Benin Ognen Plavevski, Architect for ICT4D Solutions – CRS Baltimore

Background CRS Benin approached GKIM to help them in their project with the following requirements: Assess if UAV methods are means of demarcating Cashew farms Get a more accurate area mapping process Get a more accurate area under good agricultural practices and techniques (weeding, pruning, thinning, crop association, etc.) Assess if the aerial photo’s could be used to analyze density Use these variables as a means to estimate yield / unit area and yield / farm Appreciate the vegetative state (level of flowering, fruiting level, …) of trees

Background (continued) GKIM approached Nethope and their newly formed UAV unit for assistance in the project Nethope offered to have DanOffice come in country, and make use of their prototype Cumulus UAV in order to record the imagery Dan Office IT would also bring in a DJI Phantom UAV Action plan was to record the imagery, analyze the data, and provide the outputs to the Benin team

Background (continued) Expectations were set in terms of the capability of the UAV to deliver the requirements or a subset of requirements Proper government approvals from the government of Denmark (for the UAV’s to leave the country) and the government of Benin (to allow the UAV’s to come in to the country and fly) were obtained.

On site… Nethope + DanOfficeIT arrived on site Farms that needed to be scanned were identified Project team went on site to take imagery Problems started…

Issues… Farms were not clearly identified before we began When asked to provide coordinates of the location we would fly in, the coordinates for the capital of Benin was provided This created additional issues that we did not know where we were flying Flight plans had to be created on site This caused additional problems…

Issues… Internet not available on the ground, causing problems with mission planning software Cumulus drone did not take sufficient overlap imagery, so images could not be stitched in order to provide a seamless image

Issues… Imagery could not be uploaded from the internet connection in country Images had to be uploaded from the US once the team got back in their offices Farmers could not clearly identify their plots from the aerial imagery

Analysis Currently ongoing process Identified a couple of vendors that provide AG imagery processing for the following items: Crop / tree count NDI levels Crop stress Water levels Fertilizer levels

Analysis… Simple observation – UAV imagery vs satellite:

Analysis – tree / crop count

Analysis – NDVI

Analysis…

Analysis…

Lessons learned Identify plots before going on ground Plan flights before going in the field Find good internet to upload imagery OR Have Pix4D to analyze imagery on the ground Work with farmers to identify plot sizes Print farms on paper and have them draw on top of the maps

Processing the imagery Currently ongoing process Identifying vendors that are able to provide processing algorithms Not all things can be automated and provide automatic output (weeding, prunning, etc) Some items can be observed from the imagery it self as resolution is very good

Return of Investment (RoI) 20-25% savings in nitrogen 146% ROI http://agribotix.com/roi-service-provider/ Vendor charge for one UAV flight (50 Hectares using DJI Phantom) $50,000 Cost of DJI Phantom 4: $1,500 (includes bells and whistles) Cost of multispectral camera: $3,500

Cost associated Upfront investment in UAV Cost can be between $800 - $30,000 depending on model Advanced sensors (multispectral, infrared) Multispectral ($3500) Image analysis software Can cost between $1 per hectare or $140 monthly OR (pix4D montly license) $2900 (pix4D perpetual license)

Next steps Finding the right software to perform the analysis needed Pre-deployment instructions Obtaining coordinates of locations Internet capability on site Legislative issues Setting expectations

Questions?