Your search found 6 records
1 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.
2 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.
3 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.
4 Shurtz, K. M.; Dicataldo, E.; Sowby, R. B.; Williams, G. P. 2022. Insights into efficient irrigation of urban landscapes: analysis using remote sensing, parcel data, water use, and tiered rates. Sustainability, 14(3):1427. [doi: https://doi.org/10.3390/su14031427]
Irrigation efficiency ; Urban areas ; Landscape ; Water use ; Remote sensing ; Irrigation systems ; Irrigated land ; Water conservation ; Plant health ; Evapotranspiration ; Precipitation ; Geographical information systems ; Normalized difference vegetation index / United States of America / Utah
(Location: IWMI HQ Call no: e-copy only Record No: H051192)
https://www.mdpi.com/2071-1050/14/3/1427/pdf?version=1643267602
https://vlibrary.iwmi.org/pdf/H051192.pdf
https://vlibrary.iwmi.org/pdf/H051192.pdf
(3.43 MB) (3.43 MB)
To understand how landscape irrigation can be better managed, we selected two urban irrigation systems in northern Utah, USA, and performed a statistical analysis of relationships among water use, irrigated area, plant health (based on the Normalized Difference Vegetation Index), and water rate structures across thousands of parcels. Our approach combined remote sensing with 4-band imagery and on-site measurements from water meters. We present five key findings that can lead to more efficient irrigation practices. First, tiered water rates result in less water use when compared to flat water rates for comparable plant health. Second, plant health does not strictly increase with water application but has an optimum point beyond which further watering is not beneficial. Third, many water users irrigate beyond this optimum point, suggesting that there is water conservation potential without loss of aesthetics. Fourth, irrigation is not the only contributor to plant health, and other factors need more attention in research and in water conservation programs. Fifth, smaller irrigated areas correlate with higher water application rates, an observation that may inform future land use decisions. These findings are especially pertinent in responding to the current drought in the western United States.
5 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.
6 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.
Powered by DB/Text WebPublisher, from
