UAV remote sensing of spatial variation in banana production
Brian L. Machovina A C , Kenneth J. Feeley A and Brett J. Machovina B
+ Author Affiliations
- Author Affiliations
A Department of Biological Sciences, Florida International University, Miami, FL 33199, USA; and The Fairchild Tropical Botanic Garden, Coral Gables, FL 33156, USA.
B Department of Economics and Geosciences, United States Air Force Academy, CO 97331, USA.
C Corresponding author. Email: brianmachovina@gmail.com
Crop and Pasture Science 67(12) 1281-1287 https://doi.org/10.1071/CP16135
Submitted: 12 April 2016 Accepted: 3 October 2016 Published: 23 November 2016
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.
References
Apan A, Held A, Phinn S, Markley J (2004) Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery. International Journal of Remote Sensing 25, 489–498.| Detecting sugarcane ‘orange rust’ disease using EO-1 Hyperion hyperspectral imagery.Crossref | GoogleScholarGoogle Scholar |
Astorga Y (2005) The environmental impact of the banana industry. IBC II. Cited in Fairtrade Foundation 2009, 9.
Cassman KG (1999) Ecological intensification of cereal production systems: yield potential, soil quality, and precision agriculture. Proceedings of the National Academy of Sciences of the United States of America 96, 5952–5959.
| Ecological intensification of cereal production systems: yield potential, soil quality, and precision agriculture.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DyaK1MXksFKlur4%3D&md5=dfea60e01a2320b11229b37d5471f0ceCAS |
Dobermann A, Ping J (2004) Geostatistical integration of yield monitor data and remote sensing improves yield maps. Agronomy Journal 96, 285–297.
| Geostatistical integration of yield monitor data and remote sensing improves yield maps.Crossref | GoogleScholarGoogle Scholar |
Evans BE (2012) ‘Banana market.’ (Food and Resource Economics Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida: Gainesville, FL)
FAO (2009) ‘Banana statistics.’ Report to members and observers of the sub-group on bananas of the intergovernmental group on bananas and tropical fruits. Prepared by the Trade and Market Division, Food & Agricultural Organization. (FAO: Rome)
FAOSTAT (2014) www.faostat.fao.org (accessed 8 July 2014).
Florinsky IV (1998) Combined analysis of digital terrain models and remotely sensed data in landscape investigations. Progress in Physical Geography 22, 33–60.
| Combined analysis of digital terrain models and remotely sensed data in landscape investigations.Crossref | GoogleScholarGoogle Scholar |
Foley JA, Ramankutty N, Brauman KA, Cassidy ES, Gerber JS, Johnston M, Mueller ND, O’Connell C, Ray DK, West PC, Balzer C, Bennett EM, Carpenter SR, Hill J, Monfreda C, Polasky S, Rockstrom J, Sheehan J, Siebert S, Tilman D, Zaks DPM (2011) Solutions for a cultivated planet. Nature 478, 337–342.
| Solutions for a cultivated planet.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3MXhtlSktbjF&md5=aa682100cd61546275c2cbb4ecbda7bbCAS |
Frison EE, Escalant JV, Sharrock S, Jain SM, Swennen R (2004) The global Musa genomic consortium: a boost for banana improvement. Banana improvement: cellular, molecular biology, and induced mutations. In ‘Proceedings of a Meeting’. Leuven, Belgium, 24–28 September 2001. pp. 341–349. (Science Publishers, Inc.)
Gebbers R, Adamchuk VI (2010) Precision agriculture and food security. Science 327, 828–831.
| Precision agriculture and food security.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC3cXhslWjur0%3D&md5=0bf3342178d65980f2c23cd168e16bc0CAS |
Goel P, Prasher S, Landry J, Patel R, Bonnell R, Viau A, Miller J (2003) Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn. Computers and Electronics in Agriculture 38, 99–124.
| Potential of airborne hyperspectral remote sensing to detect nitrogen deficiency and weed infestation in corn.Crossref | GoogleScholarGoogle Scholar |
Hardin PJ, Jensen RR (2011) Small-scale unmanned aerial vehicles in environmental remote sensing: challenges and opportunities. GIScience & Remote Sensing 48, 99–111.
| Small-scale unmanned aerial vehicles in environmental remote sensing: challenges and opportunities.Crossref | GoogleScholarGoogle Scholar |
Hillnhütter C, Mahlein A-K, Sikora RA, Oerke E-C (2012) Use of imaging spectroscopy to discriminate symptoms caused by Heterodera schachtii and Rhizoctonia solani on sugar beet. Precision Agriculture 13, 17–32.
| Use of imaging spectroscopy to discriminate symptoms caused by Heterodera schachtii and Rhizoctonia solani on sugar beet.Crossref | GoogleScholarGoogle Scholar |
Hirano A, Welch R, Lang H (2003) Mapping from ASTER stereo image data: DEM validation and accuracy assessment. ISPRS Journal of Photogrammetry and Remote Sensing 57, 356–370.
| Mapping from ASTER stereo image data: DEM validation and accuracy assessment.Crossref | GoogleScholarGoogle Scholar |
Hoffmann C, Blomberg M (2004) Estimation of leaf area index of Beta vulgaris L. based on optical remote sensing data. Journal of Agronomy & Crop Science 190, 197–204.
| Estimation of leaf area index of Beta vulgaris L. based on optical remote sensing data.Crossref | GoogleScholarGoogle Scholar |
Inoue Y, Morinaga S, Tomita A (2000) A blimp-based remote sensing system for low-altitude monitoring of plant variables: a preliminary experiment for agricultural and ecological applications. International Journal of Remote Sensing 21, 379–385.
| A blimp-based remote sensing system for low-altitude monitoring of plant variables: a preliminary experiment for agricultural and ecological applications.Crossref | GoogleScholarGoogle Scholar |
Jackson RD (1986) Remote sensing of biotic and abiotic plant stress. Annual Review of Phytopathology 24, 265–287.
| Remote sensing of biotic and abiotic plant stress.Crossref | GoogleScholarGoogle Scholar |
Johansen K, Phinn S, Witte C, Philip S, Newton L (2009) Mapping banana plantations from object-oriented classification of SPOT-5 imagery. Photogrammetric Engineering and Remote Sensing 75, 1069–1081.
| Mapping banana plantations from object-oriented classification of SPOT-5 imagery.Crossref | GoogleScholarGoogle Scholar |
Jones HG (1999) Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling. Agricultural and Forest Meteorology 95, 139–149.
| Use of infrared thermometry for estimation of stomatal conductance as a possible aid to irrigation scheduling.Crossref | GoogleScholarGoogle Scholar |
Jones HG (2004) Application of thermal imaging and infrared sensing in plant physiology and ecophysiology. Advances in Botanical Research 41, 107–163.
| Application of thermal imaging and infrared sensing in plant physiology and ecophysiology.Crossref | GoogleScholarGoogle Scholar |
Jones H, Schofield P (2008) Thermal and other remote sensing of plant stress. General and Applied Plant Physiology 34, 19–32.
Knoth CP, Prinz T, Loef P (2010) Microcopter-based Color Infrared (CIR) close range remote sensing as a subsidiary tool for precision farming. In ‘Proceedings of the Workshop on Remote Sensing Methods for Change Detection and Process Modelling’. pp. 49–54.
Laliberte AS, Winters C, Rango A (2011) UAS remote sensing missions for rangeland applications. Geocarto International 26, 141–156.
| UAS remote sensing missions for rangeland applications.Crossref | GoogleScholarGoogle Scholar |
Leon CT, Shaw DR, Cox MS, Abshire MJ, Ward B, Wardlaw MC, Watson C (2003) Utility of remote sensing in predicting crop and soil characteristics. Precision Agriculture 4, 359–384.
| Utility of remote sensing in predicting crop and soil characteristics.Crossref | GoogleScholarGoogle Scholar |
Mahlein A-K, Steiner U, Dehne H-W, Oerke E-C (2010) Spectral signatures of sugar beet leaves for the detection and differentiation of diseases. Precision Agriculture 11, 413–431.
| Spectral signatures of sugar beet leaves for the detection and differentiation of diseases.Crossref | GoogleScholarGoogle Scholar |
Manera JF, Rodríguez L, Delrieux C, Coppo R (2010) Aerial image acquisition and processing for remote sensing. Journal of Computer Science and Technology 10, 97–103.
Marín DH, Romero RA, Guzmán M, Sutton TB (2003) Black Sigatoka: an increasing threat to banana cultivation. Plant Disease 87, 208–222.
| Black Sigatoka: an increasing threat to banana cultivation.Crossref | GoogleScholarGoogle Scholar |
Mueller ND, Gerber JS, Johnston M, Ray DK, Ramankutty N, Foley JA (2012) Closing yield gaps through nutrient and water management. Nature 490, 254–257.
| Closing yield gaps through nutrient and water management.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38Xht1Kku7vI&md5=f2aef4e2f6ce3b0bd4093cdeb7b75301CAS |
Nutter F, Tylka G, Guan J, Moreira A, Marett C, Rosburg T, Basart J, Chong C (2002) Use of remote sensing to detect soybean cyst nematode-induced plant stress. Journal of Nematology 34, 222
Plant RE (2001) Site-specific management: the application of information technology to crop production. Computers and Electronics in Agriculture 30, 9–29.
| Site-specific management: the application of information technology to crop production.Crossref | GoogleScholarGoogle Scholar |
Pozdnyakova L, Oudemans PV, Hughes MG, Giménez D (2002) Estimation of spatial and spectral properties of phytophthora root rot and its effects on cranberry yield. Computers and Electronics in Agriculture 37, 57–70.
| Estimation of spatial and spectral properties of phytophthora root rot and its effects on cranberry yield.Crossref | GoogleScholarGoogle Scholar |
Prabhakar M, Prasad Y, Thirupathi M, Sreedevi G, Dharajothi B, Venkateswarlu B (2011) Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae). Computers and Electronics in Agriculture 79, 189–198.
| Use of ground based hyperspectral remote sensing for detection of stress in cotton caused by leafhopper (Hemiptera: Cicadellidae).Crossref | GoogleScholarGoogle Scholar |
Robinson J, Sauco V (2010) ‘Bananas and plantains.’ (CABI International: Oxfordshire, UK)
Rouse JW Jr, Schell RH, Schell JA, Deering DW (1973) Monitoring vegetation systems in the Great Plains with ERTS. NASA special publication, 351, 309.
Steltzer H, Welker JM (2006) Modeling the effect of photosynthetic vegetation properties on the NDVI-LAI relationship. Ecology 87, 2765–2772.
| Modeling the effect of photosynthetic vegetation properties on the NDVI-LAI relationship.Crossref | GoogleScholarGoogle Scholar |
Swain KC, Jayasuriya HP, Salokhe VM (2007) Suitability of low-altitude remote sensing images for estimating nitrogen treatment variations in rice cropping for precision agriculture adoption. Journal of Applied Remote Sensing 1, 013547
| Suitability of low-altitude remote sensing images for estimating nitrogen treatment variations in rice cropping for precision agriculture adoption.Crossref | GoogleScholarGoogle Scholar |
Swain KC, Thomson SJ, Jayasuriya HP (2010) Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop. Transactions of the ASABE 53, 21–27.
| Adoption of an unmanned helicopter for low-altitude remote sensing to estimate yield and total biomass of a rice crop.Crossref | GoogleScholarGoogle Scholar |
Takács I, Burai P, Tamás J (2006) Field size precision water management based on time series analysis of satellite images. In ‘9th AGILE Conference on Geographic Information Science’. Visegrád, Hungary.
Tamás J, Lénárt C (2006) Analysis of a small agricultural watershed using remote sensing techniques. International Journal of Remote Sensing 27, 3727–3738.
| Analysis of a small agricultural watershed using remote sensing techniques.Crossref | GoogleScholarGoogle Scholar |
Turner DLA, Watson C (2011) Development of an Unmanned Aerial Vehicle (UAV) for hyper resolution vineyard mapping based on visible, multispectral, and thermal imagery. In ‘Proceedings of 34th International Symposium on Remote Sensing of Environment’. p. 4.
West JS, Bravo C, Oberti R, Lemaire D, Moshou D, McCartney HA (2003) The potential of optical canopy measurement for targeted control of field crop diseases. Annual Review of Phytopathology 41, 593–614.
| The potential of optical canopy measurement for targeted control of field crop diseases.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BD3sXptFWltr8%3D&md5=a9cc7e522f570db2a21c3b41b9ad94feCAS |
Worobetz K (2000) ‘The growth of the banana industry in Costa Rica and its effect on biodiversity.’ (Department of Biological Sciences, University of Alberta: Edmonton, Alberta, Canada)
Zarco-Tejada PJ, Ustin S, Whiting M (2005) Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery. Agronomy Journal 97, 641–653.
| Temporal and spatial relationships between within-field yield variability in cotton and high-spatial hyperspectral remote sensing imagery.Crossref | GoogleScholarGoogle Scholar |
Zhang C, Kovacs JM (2012) The application of small unmanned aerial systems for precision agriculture: a review. Precision Agriculture 13, 693–712.
| The application of small unmanned aerial systems for precision agriculture: a review.Crossref | GoogleScholarGoogle Scholar | 1:CAS:528:DC%2BC38XhtValsr%2FK&md5=79098db39b6ff38cfe83c777732a96d7CAS |
Zhang M, Qin Z, Liu X, Ustin SL (2003) Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing. International Journal of Applied Earth Observation and Geoinformation 4, 295–310.
| Detection of stress in tomatoes induced by late blight disease in California, USA, using hyperspectral remote sensing.Crossref | GoogleScholarGoogle Scholar |
