Summary Module 3

UAV remote sensing of spatial variation in banana production

Brian L. MachovinaA,C, Kenneth J. FeeleyA, and Brett J. MachovinaB

ADepartment of Biological Sciences, Florida International University, Miami, FL 33199, USA; and The Fairchild Tropical Botanic Garden, Coral Gables, FL 33156, USA.

BDepartment of Economics and Geosciences, United States Air Force Academy, CO 97331, USA. CCorresponding author. Email: [email protected]

Abstract. Remote sensing through Unmanned Aerial Vehicles (UAV) can potentially be used to identify the factors influencing agricultural yield and thereby increase production efficiency. The use of UAV remains largely underutilised in tropical agricultural systems. In this study we tested a fixed-wing UAV system equipped with a sensor system for mapping spatial patterns of photosynthetic activity in banana plantations in Costa Rica. Spatial patterns derived from the Normalised Difference Vegetation Index (NDVI) were compared with spatial patterns of physical soil quality and banana fruit production data. We found spatial patterns of NDVI were significantly positively correlated with spatial patterns of several metrics of fruit yield and quality: bunch weight, number of hands per bunch, length of largest finger, and yield. NDVI was significantly negatively correlated with banana loss (discarded due to low quality). Spatial patterns of NDVI were not correlated with spatial patterns of physical soil quality. These results indicate that UAV systems can be used in banana plantations to help map patterns of fruit quality and yield, potentially aiding investigations of spatial patterns of underlying factors affecting production and thereby helping to increase agricultural efficiency.

Additional keywords: crop productivity, Musa, NDVI.

Received 12 April 2016, accepted 3 October 2016, published online 23 November 2016

Introduction

Bananas (Musa acuminata) are the developing world’s fourth most valuable food crop (Frison et al. 2004) and globally are the 12thmostimportantplantcropbyvalueandquantity.Worldwide, over 100 megatons (Mt) of bananas are grown annually on an estimated area of ~5 million ha (FAOSTAT 2014). Export production, with a volume exceeding 15 Mt and an estimated value of ~US$5 billion annually, is concentrated primarily in Latin America, where over 80% of banana exports originate (FAO 2009; Robinson and Sauco 2010; Evans 2012). Costa Rica is the world’s second largest exporter of bananas (FAOSTAT 2014).

Banana cultivation involves many financial costs and requires extensive use of expensive agrochemicals as nutrient sources and biocides, often causing downstream environmental effects (Worobetz 2000; Marín et al. 2003; Astorga 2005). Better understanding of the spatial patterns of variables that determine banana production could potentially lead to increases in yields (Cassman 1999; Mueller et al. 2012), decreases in production costs per unit yield, and increase profits. An important strategy for improving agricultural productivity and food security is utilising new technologies to gather information on crop ecology that can help better direct management decisions (Gebbers and Adamchuk 2010; Foley et al. 2011). As a core element of precision agriculture, remote monitoring of crop photosynthesis and yields can reveal patterns of stressors affecting crops, enabling managers to adjust treatments to

specifically target threatened or affected areas while avoiding treating areas not requiring attention.

Remote-sensing platforms with sensors for measuring electromagnetic reflectance patterns from vegetation offer opportunities to identify geographic patterns of crop stressors and can be used to help investigate underlying causes of stress and improve the agricultural management decision-making process (Jackson 1986; Plant 2001). Ground-based sensors, as well as sensors mounted on satellites and manned airplanes, have been used to monitor a variety of parameters in managed and natural systems; parameters measured include water stress (Jones 1999; Takács et al. 2006; Tamás and Lénárt 2006; Jones and Schofield 2008), pest damage (Nutter et al. 2002; Prabhakar et al. 2011; Hillnhütter et al. 2012), and disease (Pozdnyakova et al. 2002; West et al. 2003; Zhang et al. 2003; Apan et al. 2004; Mahlein et al. 2010), as well as underlying physical variables affecting production, such as leaf area index (Hoffmann and Blomberg 2004; Steltzer and Welker 2006), topography (Florinsky 1998; Hirano et al. 2003), soil quality and nutrient availability (Goel et al. 2003; Apan et al. 2004). Stressors are often visible through remote sensors before the effects can be perceived by the human eye, offering advantages to address problems earlier in their cycle of damage (Jones 2004) and at larger spatial scales. The utilisation of spectral reflection patterns of near-infrared (NIR) and red light are used via the commonly applied normalised difference vegetation index (NDVI) (Rouse et al. 1973) to examine spatial

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Crop & Pasture Science, 2016, 67, 1281–1287 http://dx.doi.org/10.1071/CP16135

patterns of agricultural productivity patterns (Leon et al. 2003; Tamás and Lénárt 2006). NDVI, which indicates the amount of red light absorbed and NIR light reflected, is closely correlated with photosynthetic activity of plants, and spatial patterns of photosynthetic activity can be visualised as varying levels of NDVI. Increased photosynthesis increases crop yields, and spatial patterns of NDVI early in crop development have successfully been used to predict harvest levels many months later (Leon et al. 2003; Dobermann and Ping 2004; Zarco-Tejada et al. 2005).

Small Unmanned Aerial Vehicles (UAV) are rapidly increasing in popularity as a potential tool for monitoring many agricultural practices (Swain et al. 2007; Knoth et al. 2010; Swain et al. 2010; Laliberte et al. 2011; Turner and Watson 2011; Zhang and Kovacs 2012). UAV that include multi-rotor, fixed-wing, and lighter-than-air (i.e. balloon or kite) platforms (Inoue et al. 2000) can, in some situations, offer advantages of acquiring aerial imagery at lower costs than manned airplanes or satellites with user-friendly methodology such as easier flight training, rapid field deployment, and quick turnaround of image processing, especially when target areas are small and numbers of images are low. Small, lightweight sensor systems can capture NIR and red light, enabling monitoring of NDVI of vegetation by small, low-cost UAV (Tamás and Lénárt 2006; Manera et al. 2010). Their use, however, can be limited by aviation laws, safety concerns, short flight times, weather, or small payload capacity (Hardin and Jensen 2011).

The goal of this research was to determine (1) if variability in banana plantation productivity is related to variation in NDVI, (2) if this variation is driven by variation in soil properties, and (3) if an open-market inexpensive UAV is a useful means of acquiring the NDVI data in a banana plantation.

Methods

The UAV system was evaluated for remote vegetation sampling potential in commercial banana plantations located near the city of Rio Frio in Heredia, Costa Rica (108190300N, 838530110W; Fig. 1a). The study area was located at ~100 m a.s.l. on flat topography east of the mountain range that runs north–south through Costa Rica. Between 2008 and 2012, the area received a mean annual rainfall of 4900 mm (Fig. 1b) and had a mean annual temperature of 258C. The region experiences extensive low altitude cloud cover during much of the year, indicated by a paucity of cloud free satellite imagery available from the LANDSAT platforms (http://earthexplorer. usgs.gov/, accessed 11 October 2016). The region was dominated by agricultural activities including banana, pineapple, heart of palm (Bactris gasipaes), and tropical ornamental plant cultivation. The UAV system was evaluated during the first week of April 2014.

The harvesting methods in these banana plantations provided a unique opportunity to compare remotely sensed data to banana production data. Bananas are harvested from specific areas along numbered cable lines which vary in length from ~100 to 300 m that transport bunches to processing facilities, and several standard measurements of banana fruit production and quality are recorded for each cable line. Approximately every

9 months, a banana plant produces a single bunch, which comprises 5–10 hands and which each produce 10–20 bananas (fingers). Typically, the area of harvest encompassed ~50 m on each side of a cable line. In this study, we compared remotely sensed data to six banana fruit production measurements: number of boxes produced per ha (one box = 44 kg), mean weight of a bunch, mean loss (proportion of bananas discarded from packing due to unacceptable quality), mean number of hands per bunch, mean size of largest banana per bunch, and the mean thickness of a banana on the second hand. Production variables were provided as totals or averages from 4-week periods. The mean value per cable line for each variable that was compared with remotely sensed data was calculated as the mean of the combined values recorded during the 13 4-week sampling periods of 2013 and the first 6 recorded 4-week sampling periods of 2014, providing a mean value from 76 weeks of production data.

Supplied by MarcusUAV, Inc. (www.marcusuav.com, accessed 11 October 2016), the fixed wing UAV system (Fig. 2) was a 2.5-kg delta-wing design with a 175-cm wingspan, powered by two 2700 mAh, 14.7 v, 4-cell LiPO batteries. Manual flight control during takeoff and landing was

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Fig. 1. (a) An elevationmap indicating studyarea in relation to San Jose, the capital of Costa Rica, and (b) weekly mean rainfall beginning 1 January (2008–2012) at the study area.

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performed with a Spektrum DX8 RC controller. Mission planning and automated flight control was performed using Mission Planner 1.22.99 on a laptop computer, relayed via a ground-based radio-modem antenna. A small video camera mounted in the nose of the UAV relayed live video footage of the flight path to a ground-based tracking antenna. All automated flight operations and video processing were managed via a single laptop computer connected to the antenna system. Flight plans were made creating survey grids using the Auto Waypoint and Polygon tools in Mission Planner on imagery downloaded from Ovi Satellite Maps, which provided better high-resolution coverage of the region than Google Maps. Takeoff and landings were performed via manual control, but image-capture flight patterns were under automated control by the flight control software.

The fixed-wing UAV was outfitted with a 90-g Tetracam ADC Micro (www.tetracam.com, accessed 11 October 2016), which was mounted on a motorised roll stabiliser. The Tetracam Micro captures Near-Infrared, Red, and Green wavelengths similar to Landsat Thematic Mapper bands TM2, TM3 and TM4. Wavelengths recorded are Infrared: 760–900 nm (recorded on red channel), Red: 630–690 nm (recorded on green channel), and Green: 520–600 um (recorded on blue channel). The system has a 3.2-megapixel resolution (2048 � 1536) sensor and a fixed 8.43-mm lens. Images were stored on 16-GB removable storage cards. Geographic locations of camera trigger points were recorded by the Tetracam from the UAV’s flight controller GPS.

Prior to the flights, images of a white Teflon calibration plate were recorded with the Tetracam under ambient light

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Fig. 2. Fixed Wing UAV showing (a) approximate size, (b) antenna system for location tracking and live video capture, and (c) UAV mounted on launcher.

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conditions. The UAV was launched from a dual slide-rail launcher constructed from PVC piping and powered by a 15- m, triple-cord bungee line staked into the ground ~30 m in front of the UAV. A foot operated trigger released the UAV. Launches were performed from an athletic field located within 0.5–2 km of the origins of the onset of imagery capture. Landings occurred at the same location as launches, and were achieved via manual triggering of a parachute deployment or by manually slide landing the UAV on the grassy field. Three flights were performed, reaching 260-m altitude image capture elevation, traveling at 16 m/s, lasting from 20 to 22 min, flying linear distances of 11.7 km, 16.4 km, 16.5 km and recording imagery covering 165 ha, 186 ha, 164 ha respectively. Images were recorded with ~60% forelap and 40% sidelap and a pixel resolution of 10 cm.

Post-flight images were transferred to a laptop and visually sorted to remove takeoff/landing images lower than 260-m altitude and blurry images. Images were processed into false- colour infrared images and NDVI classified images using the Teflon standard images and Pixel Wrench, the image processing software supplied by Tetracam. Using Agisoft Photoscan Professional, we attempted to mosaic and orthorectify images from each of the flights, but only the second flight provided sufficient image quality and overlap to enable the creation of a quality mosaicked single image using automated methods of the software. All banana production data comparisons were performed on data extracted from the mosaic from this flight.

The orthorectified mosaic of the flight was imported into ArcGIS. A vector map indicating locations of cable lines, supplied by growers, was also imported. A total of 23 cable lines with active banana production areas were identified. Along each of these cable lines, 20 locations were identified visually for sampling NDVI values from the NDVI mosaic. NDVI was calculated as (NIR – R)/(NIR + R). Twenty sample locations along each cable line were sampled via a stratified random sampling method by dividing each side of a cable into 10 approximately-equal-sized zones and randomly selecting the approximate centre of one of the four quadrants in each zone. At each sampling location, the closest 10-m-diameter (78.5 m2) circular area that covered only bananas (no roads, paths, canals, or other vegetation types) was selected and the mean NDVI value for the circular area was calculated. The mean NDVI value for a cable line was calculated as the combined mean of pixels in all 20 sample location areas along each cable line. A vector map indicating locations of samples for determining soil classifications, supplied by growers, was also imported. These classifications were made based on soil core samples previously made by growers at the specific locations. Soils at sample sites were classified on a four-tier scale (I-IV) of most to least favourable classes, respectively, for banana cultivation based on physical soil characteristics including texture, structure, portion of coarse fragments, consistence, and drainage. At each soil core sample location, a 10-m-diameter (78.5 m2) circular area was selected from the NDVI mosaic. Only soil sample

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Fig. 3. (a) Locations of soil samples on mosaic of false-colour imagery acquired with Tetracam Micro. Green = soil class I (n = 14, blue = soil class II (n = 12), yellow = soil class III (n = 12), red = soil class IV (n = 12). (b) Locations of samples of NDVI values taken along cable lines (n = 460). Green = high NDVI levels, yellow = moderate NDVI levels, red = low NDVI values.

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locations where the 10-m-diameter NDVI sample included bananas alone (no other vegetation types) were included in analysis. The mean NDVI value for each soil classification was determined by calculating the mean of all pixels from all soil sample locations for each soil classification level.

Results

After sorting, flights one, two, and three, produced 269, 259, and 294 images. Attempts to mosaic all images from the first and third flight were not successful. Flight two produced better results for mosaicking, but required several rounds of utilising manual tie points to correctly match and align adjacent images and groups of images. The mosaic from the second flight contained some areas with slight misalignment among adjacent images, but provided sufficient accuracy to locate sample points along cable lines and soil sample locations (Fig. 3a).

Mean NDVI values from the 23 cable lines ranged from 0.20 to 0.35 with a mean value across all cable lines of 0.26. In general, the region north of the road bisecting the mosaic image exhibited higher NDVI values (Fig. 3b). Mean NDVI was significantly positively correlated with four banana fruit production variables: mean bunch weight (Fig. 4a); mean number of hands per bunch (Fig. 4b); mean length of largest finger (Fig. 4d); mean boxes per hectare (Fig. 4e). Mean NDVI was significantly negatively correlated with mean loss (Fig. 4f). No significant relationship existed between mean NDVI and mean banana thickness on the second hand (Fig. 4c).

A total of 49 soil sample locations (12 from Class I, 12 from Class II, 13 from Class III, 12 from class IV) were located in areas containing only banana plants in the mosaic. NDVI values from the soil classes ranged from 0.23 to 0.27. No significant difference in average NDVI value existed among the soil classes.

Discussion

The UAV system involved several operational challenges. It required approximately 2 h to set up, perform safety checks and successfully launch. It also required locating large, flat grassy fields for safe launches and landings. Manual flight control of the fixed wing system requires skill and could only be performed by someone with extensive experience and training. During manual landings that involved skidding the fixed-wing UAV on the grassy field, the rapid speed of the UAV was challenging and rough landings sometimes occurred, especially under windy conditions. Over time this could lead to gradual damage and increased risk of an accident. Deployment of the parachute for landing was preferred but required precise timing and altitude inordertoachievelandingswithintheconfinesofanathleticfield, especially if surrounded by large trees. Further improvements in automation (take-off and landing) that limit manual flight as well as failsafe parachute deployment during instances of loss of flight control would greatly expand the system’s utility and safety. However, the challenges of system operation were balanced by a high level of image capture per unit time and long flight distances and areas of coverage. A single flight

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Fig. 4. Relationships among mean NDVI remotely sensed with Tetracam Micro and banana production measures: (a) Mean NDVI and mean bunch weight; (b) Mean NDVI and mean number of hands per bunch; (c) Mean NDVI and mean banana thickness on second hand; (d) Mean NDVI and mean length of largest finger; (e) Mean NDVI and mean boxes per hectare; (f) Mean NDVI and mean loss.

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could cover >16 km linear distance and capture hundreds of images.

Given the difficulties of using a UAV system, an alternative and potentially superior option under many circumstances may be to use manned airplanes as a platform, especially for monitoring plantations that are thousands of hectares in size. Manned airplane platforms could be potentially more reliable due to low likelihood of crashes, extended flight times and area coverage, and larger sensor and image area capacity. However, manned airplanes can be prohibitively expensive for smaller operations, but may be preferable for large plantation owners, many which already utilise airplanes for biocide spraying operations. The costs and benefits of a UAV versus manned- plane remote sensing need to be evaluated by a plantation owner. This research indicated that a UAV can be used to capture valuable NDVI imagery, and could especially be useful for targeting specific areas where plantation owners want to investigate spatial or temporal variation in productivity.

Improvements in the ability to mosaic imagery from banana plantations could be made by increasing the forelap and sidelap. Levels of ~60% forelap and 40% sidelap were not sufficient, perhaps due to the largely featureless and monotonous nature of large commercial banana plantations. A minimum of 80% forelap and 60% sidelap would be recommended for future flights. Recording and including flight attitude data in the input parameters used in mosaicking software would also improve alignment. Adding pitch stabilisation to the UAV might improve image quality.

Results indicated that NDVI calculated from the Tetracam imagery revealed spatial patterns in plant productivity and are significantly correlated with banana fruit production values. NDVI was significantly correlated with mean bunch weight, mean hands per bunch, mean length of largest finger, mean boxes per hectare (yield), and mean loss of bananas discarded due to quality standards. The correlation of NDVI not only with yield, but with measures of fruit quality indicate that NDVI can correlate with multiple fruit morphological variables affected by photosynthesis and underlying environmental conditions. Previous NDVI values used to identify banana plantations have been reported to range from mean values of 0.1–0.573 (Johansen et al. 2009), and the results of this study (0.2–0.35) are within this range. Variation among cable lines exhibited large differences in NDVI, which varied by 43% across the 23 cable lines.

The comparison of NDVI values to soil quality did not reveal any relationships. This may be due to the sampling of 10-m2 areas around the soil analysis points and the potential for finer spatial variation in soil quality than captured in this sampling area for mean NDVI values. Finer-scale variation of soil quality may be more difficult to detect. Also, it is possible that the physical variation in soil quality may not have strong effects on banana plant productivity and therefore NDVI or the boundaries defining the different soil classes are not accurate.

This study indicates that NDVI indices are valuable for estimating spatial patterns of banana fruit productivity and quality. It is therefore possible to map much larger areas of banana cultivation and classify areas of varying fruit yield and fruit quality. This can enable managers to identify well defined geographic regions of their plantations where yields can

potentially be increased through management of stressors. Geographic patterns revealed from NDVI from the UAV are of a much finer-scale than the averages of fruit production attained through measures taken directly on fruit combined from a single cable line, and potentially enable addressing multiple target locations within a single cable line. Further investigation of the underlying variables affecting spatial patterns of NDVI in banana plantations by UAV is warranted, including comparisons of geographic patterns of the indices against topography, drainage, nutrient availability, disease, pests, and more thorough investigation of soil types. Utilising UAV to detect patterns of productivity and underlying causes of variation may enable management scenarios that can address problems and improve yields. Improving yields will in turn allow for greater efficiency, decreasing environmental impacts of banana cultivation as global demand for food increases.

Acknowledgement

This research was funded by Dole Food Company, Inc.

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UAV remote sensing of bananas Crop & Pasture Science 1287

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