Prospects of Improving Agricultural and Water Productivity through Unmanned Aerial Vehicles

Abstract

<jats:p>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.</jats:p>

Application of the Random Forest Classifier to Map Irrigated Areas Using Google Earth Engine

“…In 2016, IWMI further developed an improved irrigated area map for Asia and Africa using canonical correlation analysis and time-lagged regression at 250 m spatial resolution for 2000 and 2010 [13]. Although these datasets are important for indicating irrigated areas, they generally over-estimate the areas due to the low spatial resolution data used [18]. A recent study in South Africa has shown how low spatial resolution data can result in over-estimates of irrigated areas, particularly in smallholder fields that are about 2 ha in size, very small to be detected by low spatial resolution satellites [18].…”

“…Advances in remote sensing technologies in conjunction with the emergence of big data and cloud-based processing platforms such as Google Earth Engine (GEE) are facilitating the classification of irrigated areas within improved accuracies in a time and costeffective manner, enhancing the monitoring of these at both local and global scales [19,20]. This is aided by freely available high-resolution remotely sensed products and novel non-parametric machine learning algorithms for land use classification [18,21,22]. Supervised image classification machine learning algorithms include Support Vector Machine (SVM), Random Forest (RF), decision tree algorithms, and Extreme Gradient Boosting (XGboost) [23].…”

Influence of Spatial Resolution for Vegetation Indices’ Extraction Using Visible Bands from Unmanned Aerial Vehicles’ Orthomosaics Datasets

“…Today, varieties of UAVs, characterised by different flight mechanics and take-off weights, and equipable sensors are continuously put on the market, providing a wide choice for operators in the sector even at minimal costs [5]. On the other hand, their versatility and transversality have triggered new application areas [6][7][8].…”

Climate Change Impacts on Water and Agriculture Sectors in Southern Africa: Threats and Opportunities for Sustainable Development

“…This development holds promise for the hot and dry climates and water scarce regions such as Southern Africa. Remote sensing, and particularly the of use unmanned aerial vehicles (UAVs), also called drones, has become an important components of agricultural water management, particularly in irrigation scheduling for both commercial and smallholder sub-sectors [56]. Developments in precision farming have been aided by freely available remotely sensed products and high and user-defined spatial and temporal resolution drone images.…”

“…UAVs can precisely estimate crop loss by comparing the pre-and post-disaster images [59]. This pre-and post-crop damage data is critical for insurance companies as it provides information that allows assessment of the damage incurred by farmers [56]. This is particularly relevant as insurance companies move towards insuring smallholder farmers against extreme weather events [60,61].…”