Your search found 18 records
1 Thomas, E.; Andres, L. A.; Borja-Vega, C.; Sturzenegger, G. (Eds.) 2018. Innovations in WASH [Water, Sanitation and Hygiene] impact measures: water and sanitation measurement technologies and practices to inform the sustainable development goals. Washington, DC, USA: World Bank. 123p. (Directions in Development - Infrastructure) [doi: https://doi.org/10.1596/978-1-4648-1197-5]
Water quality ; Sanitation ; Technological changes ; Innovation ; Sustainable Development Goals ; Drinking water ; Quality assurance ; Measurement ; Sensors ; Guidelines ; Water supply ; Wastewater treatment ; Water use ; Hygiene ; Monitoring ; Indicators ; Public health ; Health programmes ; Households ; Behaviour ; Hand washing ; Satellite observation ; Remote sensing ; Unmanned aerial vehicles
(Location: IWMI HQ Call no: e-copy only Record No: H048488)
https://openknowledge.worldbank.org/bitstream/handle/10986/29099/9781464811975.pdf?sequence=4&isAllowed=y
https://vlibrary.iwmi.org/pdf/H048488.pdf
https://vlibrary.iwmi.org/pdf/H048488.pdf
(1.58 MB) (1.58 MB)
2 Nhamo, Luxon; van Dijk, R.; Magidi, J.; Wiberg, David; Tshikolomo, K. 2018. Improving the accuracy of remotely sensed irrigated areas using post-classification enhancement through UAV [Unmanned Aerial Vehicle] capability. Remote Sensing, 10(5):1-12. (Special issue: Remote Sensing for Crop Water Management). [doi: https://doi.org/10.3390/rs10050712]
Irrigated sites ; Remote sensing ; Unmanned aerial vehicles ; Land use mapping ; Land cover mapping ; Satellite imagery ; Landsat ; Farmland ; Vegetation index ; Crops / South Africa / Limpopo Province / Venda / Gazankulu
(Location: IWMI HQ Call no: e-copy only Record No: H048752)
(2.23 MB) (2.23 MB)
Although advances in remote sensing have enhanced mapping and monitoring of irrigated areas, producing accurate cropping information through satellite image classification remains elusive due to the complexity of landscapes, changes in reflectance of different land-covers, the remote sensing data selected, and image processing methods used, among others. This study extracted agricultural fields in the former homelands of Venda and Gazankulu in Limpopo Province, South Africa. Landsat 8 imageries for 2015 were used, applying the maximum likelihood supervised classifier to delineate the agricultural fields. The normalized difference vegetation index (NDVI) applied on Landsat imageries on the mapped fields during the dry season (July to August) was used to identify irrigated areas, because years of satellite data analysis suggest that healthy crop conditions during dry seasons are only possible with irrigation. Ground truth points totaling 137 were collected during fieldwork for pre-processing and accuracy assessment. An accuracy of 96% was achieved on the mapped agricultural fields, yet the irrigated area map produced an initial accuracy of only 71%. This study explains and improves the 29% error margin from the irrigated areas. Accuracy was enhanced through post-classification correction (PCC) using 74 post-classification points randomly selected from the 2015 irrigated area map. High resolution aerial photographs of the 74 sample fields were acquired by an unmanned aerial vehicle (UAV) to give a clearer picture of the irrigated fields. The analysis shows that mapped irrigated fields that presented anomalies included abandoned croplands that had green invasive alien species or abandoned fruit plantations that had high NDVI values. The PCC analysis improved irrigated area mapping accuracy from 71% to 95%.
3 Song, P.; Zheng, X.; Li, Y.; Zhang, K.; Huang, J.; Li, H.; Zhang, H.; Liu, L.; Wei, C.; Mansaray, L. R.; Wang, D.; Wang, X. 2020. Estimating reed loss caused by locusta migratoria manilensis using UAV [Unmanned Aerial Vehicle] -based hyperspectral data. Science of the Total Environment, 719:137519. [doi: https://doi.org/10.1016/j.scitotenv.2020.137519]
Crop losses ; Estimation ; Locusta migratoria ; Unmanned aerial vehicles ; Monitoring ; Forecasting ; Models ; Satellite observation ; Remote sensing ; Vegetation index / China / Kenli / Dongying / Shandong
(Location: IWMI HQ Call no: e-copy only Record No: H049853)
(3.89 MB)
Locusta migratoria manilensis has caused major damage to vegetation and crops. Quantitative evaluation studies of vegetation loss estimation from locust damage have seldom been found in traditional satellite-remote-sensing-based research due to insufficient temporal-spatial resolution available from most current satellite-based observations. We used remote sensing data acquired from an unmanned aerial vehicle (UAV) over a simulated Locusta migratoria manilensis damage experiment on a reed (Phragmites australis) canopy in Kenli District, China during July 2017. The experiment was conducted on 72 reed plots, and included three damage duration treatments with each treatment including six locust density levels. To establish the appropriate loss estimation models after locust damage, a hyperspectral imager was mounted on a UAV to collect reed canopy spectra. Loss components of six vegetation indices (RVI, NDVI, SAVI, MSAVI, GNDVI, and IPVI) and two “red edge” parameters (Dr and SDr) were used for constructing the loss estimation models. Results showed that: (1) Among the six selected vegetation indices, loss components of NDVI, MSAVI, and GNDVI were more sensitive to the variation of dry weight loss of reed green leaves and produced smaller estimation errors during the model test process, with RMSEs ranging from 8.8 to 9.1 g/m;. (2) Corresponding model test results based on loss components of the two selected red edge parameters yielded RMSEs of 27.5 g/m2 and 26.1 g/m2 for Dr and SDr respectively, suggesting an inferior performance of red edge parameters compared with vegetation indices for reed loss estimation. These results demonstrate the great potential of UAV-based loss estimation models for evaluating and quantifying degree of locust damage in an efficient and quantitative manner. The methodology has promise for being transferred to satellite remote sensing data in the future for better monitoring of locust damage of larger geographical areas.
4 Nhamo, Luxon; Magidi, J.; Nyamugama, A.; Clulow, A. D.; Sibanda, M.; Chimonyo, V. G. P.; Mabhaudhi, T. 2020. Prospects of improving agricultural and water productivity through unmanned aerial vehicles. Agriculture, 10(7):256. [doi: https://doi.org/10.3390/agriculture10070256]
Agricultural productivity ; Water productivity ; Unmanned aerial vehicles ; Water management ; Plant health ; Crop yield ; Monitoring ; Vegetation index ; Remote sensing ; Evapotranspiration ; Water stress ; Irrigation scheduling ; Mapping ; Smallholders ; Farmers ; Models ; Disaster risk reduction ; Resilience ; Satellite imagery ; Cost benefit analysis
(Location: IWMI HQ Call no: e-copy only Record No: H049892)
(1.05 MB) (1.05 MB)
Unmanned Aerial Vehicles (UAVs) are an alternative to costly and time-consuming traditional methods to improve agricultural water management and crop productivity through the acquisition, processing, and analyses of high-resolution spatial and temporal crop data at field scale. UAVs mounted with multispectral and thermal cameras facilitate the monitoring of crops throughout the crop growing cycle, allowing for timely detection and intervention in case of any anomalies. The use of UAVs in smallholder agriculture is poised to ensure food security at household level and improve agricultural water management in developing countries. This review synthesises the use of UAVs in smallholder agriculture in the smallholder agriculture sector in developing countries. The review highlights the role of UAV derived normalised difference vegetation index (NDVI) in assessing crop health, evapotranspiration, water stress and disaster risk reduction. The focus is to provide more accurate statistics on irrigated areas, crop water requirements and to improve water productivity and crop yield. UAVs facilitate access to agro-meteorological information at field scale and in near real-time, important information for irrigation scheduling and other on-field decision-making. The technology improves smallholder agriculture by facilitating access to information on crop biophysical parameters in near real-time for improved preparedness and operational decision-making. Coupled with accurate meteorological data, the technology allows for precise estimations of crop water requirements and crop evapotranspiration at high spatial resolution. Timely access to crop health information helps inform operational decisions at the farm level, and thus, enhancing rural livelihoods and wellbeing.
5 Mwinuka, P. R.; Mbilinyi, B. P.; Mbungu, W. B.; Mourice, S. K.; Mahoo, H. F.; Schmitter, Petra. 2021. The feasibility of hand-held thermal and UAV-based multispectral imaging for canopy water status assessment and yield prediction of irrigated African eggplant (Solanum aethopicum L). Agricultural Water Management, 245:106584. [doi: https://doi.org/10.1016/j.agwat.2020.106584]
Water stress ; Eggplants ; Canopy ; Water requirements ; Crop yield ; Forecasting ; Infrared imagery ; Multispectral imagery ; Unmanned aerial vehicles ; Remote sensing ; Irrigated farming ; Irrigation water ; Performance evaluation ; Moisture content ; Vegetation index ; Plant developmental stages ; Temperature / Africa / United Republic of Tanzania / Rudewa Watershed
(Location: IWMI HQ Call no: e-copy only Record No: H050054)
https://www.sciencedirect.com/science/article/pii/S0378377420321314/pdfft?md5=25877087dd8e72a2377978976c8abc33&pid=1-s2.0-S0378377420321314-main.pdf
https://vlibrary.iwmi.org/pdf/H050054.pdf
https://vlibrary.iwmi.org/pdf/H050054.pdf
(6.03 MB) (6.03 MB)
This study was conducted to evaluate the feasibility of a mobile phone-based thermal and UAV-based multispectral imaging to assess the irrigation performance of African eggplant. The study used a randomized block design (RBD) with sub-plots being irrigated at 100% (I100), 80% (I80) and 60% (I60) of the calculated crop water requirements using drip. The leaf moisture content was monitored at different soil moisture conditions at early, vegetative and full vegetative stages. The results showed that, the crop water stress index (CWSI) derived from the mobile phone-based thermal images is sensitive to leaf moisture content (LMC) in I80 and I60 at all vegetative stages. The UAV-derived Normalized Difference Vegetation Index (NDVI) and Optimized Soil Adjusted Vegetation Index (OSAVI) correlated with LMC at the vegetative and full vegetative stages for all three irrigation treatments. In cases where eggplant is irrigated under normal conditions, the use of NDVI or OSAVI at full vegetative stages will be able to predict eggplant yields. In cases where, eggplant is grown under deficit irrigation, CWSI can be used at vegetative or full vegetative stages next to NDVI or OSAVI depending on available resources.
6 Pudasainee, A.; Chaulagain, B. P. 2020. Prospects of ICT based agricultural technology in Nepal. Nepalese Journal of Agricultural Sciences, 19:223-235.
Precision agriculture ; Information and Communication Technologies ; Unmanned aerial vehicles ; Imagery ; Geographical information systems ; Satellites ; Plant health ; Fertilizers ; Decision making ; Agroindustrial sector ; Farmers ; Models / Nepal
(Location: IWMI HQ Call no: e-copy only Record No: H049813)
(0.41 MB)
This cloud agriculture system (CAS) combines drone assisted diagnostics and prescription agriculture (DADAPA), value addition to agriculture produce (VAAP) and cloud market system (CMS). The CAS can be an advantage to the country where lack of trained agriculture service providers, complex geographical patterns and rain fed farming hinders the crop productivity. The low-cost drone imagery and data based analysis as DADAPA in combination with VAAP and CMS will have a significant impact for the upliftment of farming community in Nepal. The CAS can supplement wet bench laboratory and skilled agriculture services. That addresses insufficient market information system and the knowledge in value addition to crops and vegetables. It provides services to farmers by prescribing solutions accurately for problems like irrigation, weed management, plant health and growth, soil nutrition and fertilizers applications, diagnosing different diseases as precision agriculture. Information on agri-products and price in the market develops confidence and income to farmers and wholesalers.
7 Brewer, K.; Clulow, A.; Sibanda, M.; Gokool, S.; Naiken, V.; Mabhaudhi, Tafadzwanashe. 2022. Predicting the chlorophyll content of maize over phenotyping as a proxy for crop health in smallholder farming systems. Remote Sensing, 14(3):518. [doi: https://doi.org/10.3390/rs14030518]
Maize ; Chlorophylls ; Plant health ; Forecasting ; Smallholders ; Farming systems ; Precision agriculture ; Machine learning ; Unmanned aerial vehicles / South Africa / KwaZulu-Natal / Swayimani
(Location: IWMI HQ Call no: e-copy only Record No: H050903)
(6.76 MB) (6.76 MB)
Smallholder farmers depend on healthy and productive crop yields to sustain their socio-economic status and ensure livelihood security. Advances in South African precision agriculture in the form of unmanned aerial vehicles (UAVs) provide spatially explicit near-real-time information that can be used to assess crop dynamics and inform smallholder farmers. The use of UAVs with remote-sensing techniques allows for the acquisition of high spatial resolution data at various spatio-temporal planes, which is particularly useful at the scale of fields and farms. Specifically, crop chlorophyll content is assessed as it is one of the best known and reliable indicators of crop health, due to its biophysical pigment and biochemical processes that indicate plant productivity. In this regard, the study evaluated the utility of multispectral UAV imagery using the random forest machine learning algorithm to estimate the chlorophyll content of maize through the various growth stages. The results showed that the near-infrared and red-edge wavelength bands and vegetation indices derived from these wavelengths were essential for estimating chlorophyll content during the phenotyping of maize. Furthermore, the random forest model optimally estimated the chlorophyll content of maize over the various phenological stages. Particularly, maize chlorophyll was best predicted during the early reproductive, late vegetative, and early vegetative growth stages to RMSE accuracies of 40.4 µmol/m-2 , 39 µmol/m-2 , and 61.6 µmol/m-2 , respectively. The least accurate chlorophyll content results were predicted during the mid-reproductive and late reproductive growth stages to RMSE accuracies of 66.6 µmol/m-2 and 69.6 µmol/m-2 , respectively, as a consequence of a hailstorm. A resultant chlorophyll variation map of the maize growth stages captured the spatial heterogeneity of chlorophyll within the maize field. Therefore, the study’s findings demonstrate that the use of remotely sensed UAV imagery with a robust machine algorithm is a critical tool to support the decision-making and management in smallholder farms.
8 Mwinuka, P. R.; Mourice, S. K.; Mbungu, W. B.; Mbilinyi, B. P.; Tumbo, S. D.; Schmitter, Petra. 2022. UAV-based multispectral vegetation indices for assessing the interactive effects of water and nitrogen in irrigated horticultural crops production under tropical sub-humid conditions: a case of African eggplant. Agricultural Water Management, 266:107516. [doi: https://doi.org/10.1016/j.agwat.2022.107516]
Crop production ; Water use efficiency ; Nitrogen ; Unmanned aerial vehicles ; Irrigated farming ; Vegetation index ; Water stress ; Subhumid climate ; Horticulture ; Eggplants ; Crop yield ; Irrigation water ; Water requirements / Africa / United Republic of Tanzania / Kilosa
(Location: IWMI HQ Call no: e-copy only Record No: H051019)
https://www.sciencedirect.com/science/article/pii/S0378377422000634/pdfft?md5=204296c2ca8c64d46a7e0fd0fa774e05&pid=1-s2.0-S0378377422000634-main.pdf
https://vlibrary.iwmi.org/pdf/H051019.pdf
https://vlibrary.iwmi.org/pdf/H051019.pdf
(5.25 MB) (5.25 MB)
UAV-based multispectral vegetation indices are often used to assess crop performance and water consumptive use. However, their ability to assess the interaction between water, especially deficit irrigation, and nitrogen application rates in irrigated agriculture has been less explored. Understanding the effect of water-nitrogen interactions on vegetation indices could further support optimal water and N management. Therefore, this study used a split plot design with water being the main factor and N being the sub-factor. African eggplants were drip irrigated at 100% (I100), 80% (I80) or 60% (I60) of the crop water requirements and received 100% (F100), 75% (F75), 50% (F50) or 0% (F0) of the crop N requirements. Results showed that the transformed difference vegetation index (TDVI) was best in distinguishing differences in leaf moisture content (LMC) during the vegetative stage irrespective of the N treatment. The green normalized difference vegetation index (GNDVI) worked well to distinguish leaf N during vegetative and full vegetative stages. However, the detection of the interactive effect of water and N on crop performance required a combination of GNDVI, NDVI and OSAVI across both stages as each of these 3 VI showed an ability to detect some but not all treatments. The fact that a certain amount of irrigation water can optimize the efficiency of N uptake by the plant is an important criterion to consider in developing crop specific VI based decision trees for crop performance assessments and yield prediction.
9 Brewer, K.; Clulow, A.; Sibanda, M.; Gokool, S.; Odindi, J.; Mutanga, O.; Naiken, V.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2022. Estimation of maize foliar temperature and stomatal conductance as indicators of water stress based on optical and thermal imagery acquired using an Unmanned Aerial Vehicle (UAV) platform. Drones, 6(7):169. [doi: https://doi.org/10.3390/drones6070169]
Crop growth stage ; Maize ; Temperature measurement ; Stomatal conductance ; Estimation ; Water stress ; Thermal infrared imagery ; Unmanned aerial vehicles ; Machine learning ; Forecasting ; Models ; Precision agriculture ; Smallholders ; Small-scale farming ; Crop water use ; Indicators / South Africa / KwaZulu-Natal / Swayimani
(Location: IWMI HQ Call no: e-copy only Record No: H051298)
https://www.mdpi.com/2504-446X/6/7/169/pdf?version=1657704795
https://vlibrary.iwmi.org/pdf/H051298.pdf
https://vlibrary.iwmi.org/pdf/H051298.pdf
(7.44 MB) (7.44 MB)
Climatic variability and extreme weather events impact agricultural production, especially in sub-Saharan smallholder cropping systems, which are commonly rainfed. Hence, the development of early warning systems regarding moisture availability can facilitate planning, mitigate losses and optimise yields through moisture augmentation. Precision agricultural practices, facilitated by unmanned aerial vehicles (UAVs) with very high-resolution cameras, are useful for monitoring farm-scale dynamics at near-real-time and have become an important agricultural management tool. Considering these developments, we evaluated the utility of optical and thermal infrared UAV imagery, in combination with a random forest machine-learning algorithm, to estimate the maize foliar temperature and stomatal conductance as indicators of potential crop water stress and moisture content over the entire phenological cycle. The results illustrated that the thermal infrared waveband was the most influential variable during vegetative growth stages, whereas the red-edge and near-infrared derived vegetation indices were fundamental during the reproductive growth stages for both temperature and stomatal conductance. The results also suggested mild water stress during vegetative growth stages and after a hailstorm during the mid-reproductive stage. Furthermore, the random forest model optimally estimated the maize crop temperature and stomatal conductance over the various phenological stages. Specifically, maize foliar temperature was best predicted during the mid-vegetative growth stage and stomatal conductance was best predicted during the early reproductive growth stage. Resultant maps of the modelled maize growth stages captured the spatial heterogeneity of maize foliar temperature and stomatal conductance within the maize field. Overall, the findings of the study demonstrated that the use of UAV optical and thermal imagery, in concert with prediction-based machine learning, is a useful tool, available to smallholder farmers to help them make informed management decisions that include the optimal implementation of irrigation schedules.
10 Wu, S.; Deng, L.; Guo, L.; Wu, Y. 2022. Wheat leaf area index prediction using data fusion based on high-resolution unmanned aerial vehicle imagery. Plant Methods, 18:68. [doi: https://doi.org/10.1186/s13007-022-00899-7]
Leaf area index ; Forecasting ; Unmanned aerial vehicles ; Thermal infrared imagery ; Data fusion ; Machine learning ; Estimation ; Wheat ; Vegetation index ; Remote sensing ; Satellites ; Biomass ; Models / China / Henan
(Location: IWMI HQ Call no: e-copy only Record No: H051401)
https://plantmethods.biomedcentral.com/counter/pdf/10.1186/s13007-022-00899-7.pdf
https://vlibrary.iwmi.org/pdf/H051401.pdf
https://vlibrary.iwmi.org/pdf/H051401.pdf
(7.53 MB) (7.53 MB)
Background: Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion.
Methods : To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.
Results: The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat.
Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.
Methods : To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression.
Results: The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R2 raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R2 = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R2 = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat.
Conclusions: The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.
11 Mabhaudhi, Tafadzwanashe; Bangira, T.; Sibanda, M.; Cofie, Olufunke. 2022. Use of drones to monitor water availability and quality in irrigation canals and reservoirs for improving water productivity and enhancing precision agriculture in smallholder farms. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on West and Central African Food Systems Transformation. 36p.
Water availability ; Water quality ; Monitoring ; Irrigation canals ; Reservoirs ; Water productivity ; Precision agriculture ; Smallholders ; Unmanned aerial vehicles ; Imagery ; Remote sensing ; Floods ; Mapping ; Water levels ; Parameters
(Location: IWMI HQ Call no: e-copy only Record No: H051656)
(735 KB)
The report provides a methodology protocol for measuring temporal and spatial changes in water quantity and quality using drone imagery. The procedure is informed by the need for effective and sustainable water resource use to enhance water productivity under climate change. It is based on a literature review that allows the identification of appropriate processes, materials, and procedures for water monitoring, including mapping spatial and temporal dynamics of reservoirs, measurement of water quality parameters, and flood mapping of irrigation canals.
12 Gokool, S.; Mahomed, M.; Kunz, R.; Clulow, A.; Sibanda, M.; Naiken, V.; Chetty, K.; Mabhaudhi, Tafadzwanashe. 2023. Crop monitoring in smallholder farms using unmanned aerial vehicles to facilitate precision agriculture practices: a scoping review and bibliometric analysis. Sustainability, 15(4):3557. (Special issue: Advanced Technologies, Techniques and Process for the Sustainable Precision Agriculture) [doi: https://doi.org/10.3390/su15043557]
Precision agriculture ; Crop monitoring ; Smallholders ; Farming systems ; Unmanned aerial vehicles ; Bibliometric analysis ; Food security ; Machine learning ; Remote sensing ; Technology
(Location: IWMI HQ Call no: e-copy only Record No: H051762)
https://www.mdpi.com/2071-1050/15/4/3557/pdf?version=1676858967
https://vlibrary.iwmi.org/pdf/H051762.pdf
https://vlibrary.iwmi.org/pdf/H051762.pdf
(1.95 MB) (1.95 MB)
In this study, we conducted a scoping review and bibliometric analysis to evaluate the state-of-the-art regarding actual applications of unmanned aerial vehicle (UAV) technologies to guide precision agriculture (PA) practices within smallholder farms. UAVs have emerged as one of the most promising tools to monitor crops and guide PA practices to improve agricultural productivity and promote the sustainable and optimal use of critical resources. However, there is a need to understand how and for what purposes these technologies are being applied within smallholder farms. Using Biblioshiny and VOSviewer, 23 peer-reviewed articles from Scopus and Web of Science were analyzed to acquire a greater perspective on this emerging topical research focus area. The results of these investigations revealed that UAVs have largely been used for monitoring crop growth and development, guiding fertilizer management, and crop mapping but also have the potential to facilitate other PA practices. Several factors may moderate the potential of these technologies. However, due to continuous technological advancements and reductions in ownership and operational costs, there remains much cause for optimism regarding future applications of UAVs and associated technologies to inform policy, planning, and operational decision-making.
13 Buthelezi, S.; Mutanga, O.; Sibanda, M.; Odindi, J.; Clulow, A. D.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2023. Assessing the prospects of remote sensing maize leaf area index using UAV-derived multi-spectral data in smallholder farms across the growing season. Remote Sensing, 15(6):1597. (Special issue: Retrieving Leaf Area Index Using Remote Sensing) [doi: https://doi.org/10.3390/rs15061597]
Maize ; Leaf area index ; Vegetation index ; Remote sensing ; Unmanned aerial vehicles ; Multispectral imagery ; Small-scale farming ; Smallholders ; Growth stages ; Monitoring ; Forecasting ; Models ; Machine learning ; Agricultural productivity ; Crop yield / South Africa / KwaZulu-Natal / Swayimane
(Location: IWMI HQ Call no: e-copy only Record No: H051818)
https://www.mdpi.com/2072-4292/15/6/1597/pdf?version=1678869485
https://vlibrary.iwmi.org/pdf/H051818.pdf
https://vlibrary.iwmi.org/pdf/H051818.pdf
(3.96 MB) (3.96 MB)
Maize (Zea Mays) is one of the most valuable food crops in sub-Saharan Africa and is a critical component of local, national and regional economies. Whereas over 50% of maize production in the region is produced by smallholder farmers, spatially explicit information on smallholder farm maize production, which is necessary for optimizing productivity, remains scarce due to a lack of appropriate technologies. Maize leaf area index (LAI) is closely related to and influences its canopy physiological processes, which closely relate to its productivity. Hence, understanding maize LAI is critical in assessing maize crop productivity. Unmanned Aerial Vehicle (UAV) imagery in concert with vegetation indices (VIs) obtained at high spatial resolution provides appropriate technologies for determining maize LAI at a farm scale. Five DJI Matrice 300 UAV images were acquired during the maize growing season, and 57 vegetation indices (VIs) were generated from the derived images. Maize LAI samples were collected across the growing season, a Random Forest (RF) regression ensemble based on UAV spectral data and the collected maize LAI samples was used to estimate maize LAI. The results showed that the optimal stage for estimating maize LAI using UAV-derived VIs in concert with the RF ensemble was during the vegetative stage (V8–V10) with an RMSE of 0.15 and an R2 of 0.91 (RRMSE = 8%). The findings also showed that UAV-derived traditional, red edge-based and new VIs could reliably predict maize LAI across the growing season with an R2 of 0.89–0.93, an RMSE of 0.15–0.65 m2/m2 and an RRMSE of 8.13–19.61%. The blue, red edge and NIR sections of the electromagnetic spectrum were critical in predicting maize LAI. Furthermore, combining traditional, red edge-based and new VIs was useful in attaining high LAI estimation accuracies. These results are a step towards achieving robust, efficient and spatially explicit monitoring frameworks for sub-Saharan African smallholder farm productivity.
14 Abrahams, M.; Sibanda, M.; Dube, T.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2023. A systematic review of UAV applications for mapping neglected and underutilised crop species’ spatial distribution and health. Remote Sensing, 15(19):4672. (Special issue: Crops and Vegetation Monitoring with Remote/Proximal Sensing II) [doi: https://doi.org/10.3390/rs15194672]
Underutilized species ; Mapping ; Unmanned aerial vehicles ; Plant health ; Crop production ; Remote sensing ; Machine learning ; Food security ; Precision agriculture ; Spatial distribution ; Stomatal conductance ; Smallholders ; Farmland ; Vegetation index ; Systematic reviews
(Location: IWMI HQ Call no: e-copy only Record No: H052234)
https://www.mdpi.com/2072-4292/15/19/4672/pdf?version=1695462413
https://vlibrary.iwmi.org/pdf/H052234.pdf
https://vlibrary.iwmi.org/pdf/H052234.pdf
(11.70 MB) (11.7 MB)
Timely, accurate spatial information on the health of neglected and underutilised crop species (NUS) is critical for optimising their production and food and nutrition in developing countries. Unmanned aerial vehicles (UAVs) equipped with multispectral sensors have significantly advanced remote sensing, enabling the provision of near-real-time data for crop analysis at the plot level in small, fragmented croplands where NUS are often grown. The objective of this study was to systematically review the literature on the remote sensing (RS) of the spatial distribution and health of NUS, evaluating the progress, opportunities, challenges, and associated research gaps. This study systematically reviewed 171 peer-reviewed articles from Google Scholar, Scopus, and Web of Science using the PRISMA approach. The findings of this study showed that the United States (n = 18) and China (n = 17) were the primary study locations, with some contributions from the Global South, including southern Africa. The observed NUS crop attributes included crop yield, growth, leaf area index (LAI), above-ground biomass (AGB), and chlorophyll content. Only 29% of studies explored stomatal conductance and the spatial distribution of NUS. Twenty-one studies employed satellite-borne sensors, while only eighteen utilised UAV-borne sensors in conjunction with machine learning (ML), multivariate, and generic GIS classification techniques for mapping the spatial extent and health of NUS. The use of UAVs in mapping NUS is progressing slowly, particularly in the Global South, due to exorbitant purchasing and operational costs, as well as restrictive regulations. Subsequently, research efforts must be directed toward combining ML techniques and UAV-acquired data to monitor NUS’ spatial distribution and health to provide necessary information for optimising food production in smallholder croplands in the Global South.
15 Sibanda, M.; Ndlovu, H. S.; Brewer, K.; Buthelezi, S.; Matongera, T. N.; Mutanga, O.; Odidndi, J.; Clulow, A. D.; Chimonyo, V. G. P.; Mabhaudhi, Tafadzwanashe. 2023. Remote sensing hail damage on maize crops in smallholder farms using data acquired by remotely piloted aircraft system. Smart Agricultural Technology, 6:100325. [doi: https://doi.org/10.1016/j.atech.2023.100325]
Crop damage ; Hail damage ; Maize ; Remote sensing ; Smallholders ; Farmers ; Farmland ; Small-scale farming ; Unmanned aerial vehicles ; Plant health ; Leaf area index ; Vegetation index ; Agricultural productivity ; Climate change / South Africa / KwaZulu Natal / Swayimane
(Location: IWMI HQ Call no: e-copy only Record No: H052320)
https://www.sciencedirect.com/science/article/pii/S2772375523001545/pdfft?md5=79fa6611a7221a58090e695d915834b8&pid=1-s2.0-S2772375523001545-main.pdf
https://vlibrary.iwmi.org/pdf/H052320.pdf
https://vlibrary.iwmi.org/pdf/H052320.pdf
(17.40 MB) (17.4 MB)
Smallholder farmers reside in marginal environments typified by dryland maize-based farming systems. Despite the significant contribution of smallholder farmers to food production, they are vulnerable to extreme weather events such as hailstorms, floods and drought. Extreme weather events are expected to increase in frequency and intensity under climate change, threatening the sustainability of smallholder farming systems. Access to climate services and information, as well as digital advisories such as Robust spatially explicit monitoring techniques from remotely piloted aircraft systems (RPAS), could be instrumental in understanding the impact and extent of crop damage. It could assist in providing adequate response mechanisms suitable for bolstering crop productivity in a spatially explicit manner. This study, therefore, sought to evaluate the utility of drone-derived multispectral data in estimating crop productivity elements (Equivalent water thickness (EWT), Chlorophyll content, and leaf area index (LAI)) in maize smallholder croplands based on the random forest regression algorithm. A hailstorm occurred in the study area during the reproductive stages 2 to 3 and 3 to 4. EWT, Chlorophyll content, and LAI were measured before and after the storm. Results of this study showed that EWT, Chlorophyll content, and LAI could be optimally estimated based on the red edge and its spectral derivatives. Specifically, EWT was estimated to a rRMEs 2.7% and 59%, RMSEs of 5.31 gm- 2 and 27.35 gm-2, R2 of 0.88 and 0.77, while chlorophyll exhibited rRMSE of 28% and 25%, RMSEs of 87.4 µmol m- 2 and 76.2 µmol m- 2 and R2 of 0.89 and 0.80 and LAI yielded a rRMSE of 10.9% and 15.2%, RMSEs of 0.6 m2 /m2 and 0.19 m2 /m2 before and after the hail damage, respectively. Overall, the study underscores the potential of RPAS-based remote sensing as a valuable resource for assessing crop damage and responding to the impact of hailstorms on crop productivity in smallholder croplands. This offers a means to enhance agricultural resilience and adaptability under climate change.
16 Sibanda, M.; Buthelezi, S.; Mutanga, O.; Odindi, J.; Clulow, A. D.; Chimonyo, V. G. P.; Gokool, S.; Naiken, V.; Magidi, J.; Mabhaudhi, Tafadzwanashe. 2023. Exploring the prospects of UAV-remotely sensed data in estimating productivity of maize crops in typical smallholder farms of Southern Africa. ISPRS Annals of the Photogrammetry, Remote Sensing and Spatial Information Sciences, X-1/W1-2023:1143-1150. (ISPRS Geospatial Week 2023, Cairo, Egypt, 2-7 September 2023) [doi: https://doi.org/10.5194/isprs-annals-X-1-W1-2023-1143-2023]
Agricultural productivity ; Small farms ; Smallholders ; Maize ; Yield forecasting ; Models ; Remote sensing ; Unmanned aerial vehicles ; Vegetation index / Southern Africa / South Africa / KwaZulu-Natal
(Location: IWMI HQ Call no: e-copy only Record No: H052490)
https://isprs-annals.copernicus.org/articles/X-1-W1-2023/1143/2023/isprs-annals-X-1-W1-2023-1143-2023.pdf
https://vlibrary.iwmi.org/pdf/H052490.pdf
https://vlibrary.iwmi.org/pdf/H052490.pdf
(1.59 MB) (1.59 MB)
This study estimated maize grain biomass, and grain biomass as a proportion of the absolute maize plant biomass using UAV-derived multispectral data. Results showed that UAV-derived data could accurately predict yield with R2 ranging from 0.80 - 0.95, RMSE ranging from 0.03 - 0.94 kg/m2 and RRMSE ranging from 2.21% - 39.91% based on the spectral datasets combined. Results of this study further revealed that the VT-R1 (56-63 days after emergence) vegetative growth stage was the most optimal stage for the early prediction of maize grain yield (R2 = 0.85, RMSE = 0.1, RRMSE = 5.08%) and proportional yield (R2 = 0.92, RMSE = 0.06, RRMSE = 17.56%), with the Normalized Difference Vegetation Index (NDVI), Enhanced Normalized Difference Vegetation Index (ENDVI), Soil Adjusted Vegetation Index (SAVI) and the red edge band being the most optimal prediction variables. The grain yield models produced more accurate results in estimating maize yield when compared to the biomass and proportional yield models. The results demonstrate the value of UAV-derived data in predicting maize yield on smallholder farms – a previously challenging task with coarse spatial resolution satellite sensors.
17 Singh, K. 2023. Current trends in River Bathymetry using UAV-borne technology to inform E-flow assessments in Southern Africa. Colombo, Sri Lanka: International Water Management Institute (IWMI). CGIAR Initiative on Digital Innovation. 17p.
(Location: IWMI HQ Call no: e-copy only Record No: H052680)
(1.48 MB)
Freshwater, constituting a mere 2.5% of Earth's total water, is a critical resource facing escalating competition due to an anticipated global population surge to 9.7 billion by 2050. Southern Africa is characterized by uneven water distribution and quality challenges which exacerbates these issues. Environmental flow (E-flow) management is a crucial approach that quantifies water requirements for maintaining ecological integrity, aiming to balance human and environmental water needs. Including Eflows in management helps to ensure sustainability of water resources River bathymetry is a core part of E-flow assessments. This document reports on core research within a project that delves into management of E-flows in the Limpopo and neighbouring basins in Southern Africa. It covers a scientific investigation to determine optimal water quantities and qualities for river systems and to assist with their management. The report focuses particularly on the use of bathymetric surveys, specifically the need for high-resolution Digital Elevation Models (DEMs) to inform hydraulic modelling. The spatial and temporal variability of bathymetry is crucial for applications ranging from flood risk mitigation to ecosystem studies and for long-term management of E-flow implementation. While traditional Total Station Theodolite (TST) surveys provide accurate ground control points and in the past were the basis for river hydraulic studies, they are limited in scale and efficiency. In situ measurements, despite their accuracy, may lack spatial representativeness and are resource intensive. Remote sensing techniques, particularly Unmanned Aerial Vehicles (UAVs), offer an alternative for bathymetric data collection driven by their ability to access challenging areas of a river and provide high-resolution data at relatively low cost. To this end, this report focuses on direct methods for bathymetric data collection, exploring optical and acoustic approaches. The primary objective was to explore and investigate UAV-based waterpenetrating surveying techniques to create high-resolution DEMs for hydraulic modelling linked to Eflow studies. A review of recent, relevant literature indicated that airborne laser bathymetry appeared preferential in the context of E-flows, compared to spectrally derived bathymetry, multimedia photogrammetry, Ground-Penetrating Radar (GPR), and Sound Navigation and Ranging (SONAR) techniques. Currently, the RIEGL VQ-840-GL green lidar sensor appears to be the forefront technology for use in E-flows UAV-borne bathymetric surveys. This research aims to contribute valuable insights into efficient and cost-effective methods for E-flow studies, addressing the growing challenges in water resource management.
18 Gokool, S.; Mahomed, M.; Brewer, K.; Naiken, V.; Clulow, A.; Sibanda, M.; Mabhaudhi, Tafadzwanashe. 2024. Crop mapping in smallholder farms using unmanned aerial vehicle imagery and geospatial cloud computing infrastructure. Heliyon, 10(5):E26913. [doi: https://doi.org/10.1016/j.heliyon.2024.e26913]
Crops ; Mapping ; Unmanned aerial vehicles ; Imagery ; Machine learning ; Smallholders ; Farmers ; Land use ; Land cover / South Africa / KwaZulu-Natal / Swayimane
(Location: IWMI HQ Call no: e-copy only Record No: H052587)
https://www.cell.com/action/showPdf?pii=S2405-8440%2824%2902944-X
https://vlibrary.iwmi.org/pdf/H052587.pdf
https://vlibrary.iwmi.org/pdf/H052587.pdf
(6.41 MB) (6.41 MB)
Smallholder farms are major contributors to agricultural production, food security, and socioeconomic growth in many developing countries. However, they generally lack the resources to fully maximize their potential. Subsequently they require innovative, evidence-based and lowercost solutions to optimize their productivity. Recently, precision agricultural practices facilitated by unmanned aerial vehicles (UAVs) have gained traction in the agricultural sector and have great potential for smallholder farm applications. Furthermore, advances in geospatial cloud computing have opened new and exciting possibilities in the remote sensing arena. In light of these recent developments, the focus of this study was to explore and demonstrate the utility of using the advanced image processing capabilities of the Google Earth Engine (GEE) geospatial cloud computing platform to process and analyse a very high spatial resolution multispectral UAV image for mapping land use land cover (LULC) within smallholder farms. The results showed that LULC could be mapped at a 0.50 m spatial resolution with an overall accuracy of 91%. Overall, we found GEE to be an extremely useful platform for conducting advanced image analysis on UAV imagery and rapid communication of results. Notwithstanding the limitations of the study, the findings presented herein are quite promising and clearly demonstrate how modern agricultural practices can be implemented to facilitate improved agricultural management in smallholder farmers.
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