Digital farming is the practice of modern technologies such as sensors, robotics, and data analysis for shifting from tedious operations to continuously automated processes. This paper reviews some of the latest achievements in agricultural robotics, specifically those that are used for autonomous weed control, field scouting, and harvesting. Object identification, task planning algorithms, digitalization and optimization of sensors are highlighted as some of the facing challenges in the context of digital farming. The concepts of multi-robots, human-robot collaboration, and environment reconstruction from aerial images and ground-based sensors for the creation of virtual farms were highlighted as some of the gateways of digital farming. It was shown that one of the trends and research focuses in agricultural field robotics is towards building a swarm of small scale robots and drones that collaborate together to optimize farming inputs and reveal denied or concealed information. For the case of robotic harvesting, an autonomous framework with several simple axis manipulators can be faster and more efficient than the currently adapted professional expensive manipulators. While robots are becoming the inseparable parts of the modern farms, our conclusion is that it is not realistic to expect an entirely automated farming system in the future.

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Abstract. Unmanned aerial vehicles (UAVs) equipped with lightweight spectral sensors facilitate non-destructive, nearreal-time vegetation analysis. In order to guarantee robust scientific analysis, data acquisition protocols and processing methodologies need to be developed and new sensors must be compared with state-of-the-art instruments. Four different types of optical UAV-based sensors (RGB camera, converted near-infrared camera, six-band multispectral camera and high spectral resolution spectrometer) were deployed and compared in order to evaluate their applicability for vegetation monitoring with a focus on precision agricultural applications. Data were collected in New Zealand over ryegrass pastures of various conditions and compared to ground spectral measurements. The UAV STS spectrometer and the multispectral camera MCA6 (Multiple Camera Array) were found to deliver spectral data that can match the spectral measurements of an ASD at ground level when compared over all waypoints (UAV STS: R 2 = 0.98; MCA6: R 2 = 0.92). Variability was highest in the near-infrared bands for both sensors while the band multispectral camera also overestimated the green peak reflectance. Reflectance factors derived from the RGB (R 2 = 0.63) and converted near-infrared (R 2 = 0.65) cameras resulted in lower accordance with reference measurements. The UAV spectrometer system is capable of providing narrow-band information for crop and pasture management. The six-band multispectral camera has the potential to be deployed to target specific broad wavebands if shortcomings in radiometric limitations can be addressed. Large-scale imaging of pasture variability can be achieved by either using a true colour or a modified near-infrared camera.Data quality from UAV-based sensors can only be assured, if field protocols are followed and environmental conditions allow for stable platform behaviour and illumination.

Irrigation is the major user of allocated global freshwaters, and scarcity of freshwater threatens to limit global food supply and ecosystem function-hence the need for decision tools to optimize use of irrigation water. This research shows that variable alluvial soil ideally requires variable placement of water to make the best use of irrigation water during crop growth. Further savings can be made by withholding irrigation during certain growth stages. The spatial variation of soil water supplied to (1) pasture and (2) a maize crop was modelled and mapped by relating high resolution apparent electrical conductivity maps to soil available water holding capacity (AWC) at two contrasting field sites. One field site, a 156-ha pastoral farm, has soil with wide ranging AWCs (116-230 mm m -1 ); the second field site, a 53-ha maize field, has soil with similar AWCs (161-164 mm m -1 ). The derived AWC maps were adjusted on a daily basis using a soil water balance prediction model. In addition, real-time hourly logging of soil moisture in the maize field showed a zone where poorly drained soil remained wetter than predicted. Variable-rate irrigation (VRI) scenarios are presented and compared with uniform-rate irrigation scenarios for 3 years of climate data at these two sites. The results show that implementation of VRI would enable significant potential mean annual water saving (21.8% at Site 1; 26.3% at Site 2). Daily soil water status mapping could be used to control a variable rate irrigator.

Accurate and efficient monitoring of pasture quality on hill country farm systems is crucial for pasture management and optimizing production. Hyperspectral imaging is a promising tool for mapping a wide range of biophysical and biochemical properties of vegetation from leaf to canopy scale. In this study, the potential of high spatial resolution and airborne hyperspectral imaging for predicting crude protein (CP) and metabolizable energy (ME) in heterogeneous hill country farm was investigated. Regression models were developed between measured pasture quality values and hyperspectral data using random forest regression (RF). The results proved that pasture quality could be predicted with hyperspectral data alone; however, accuracy was improved after combining the hyperspectral data with environmental data (elevation, slope angle, slope aspect, and soil type) where the prediction accuracy for CP was R 2 CV (cross-validated coefficient of determination) = 0.70, RMSE CV (cross-validated root mean square error) = 2.06%, RPD CV (cross-validated ratio to prediction deviation) = 1.82 and ME: R 2 CV = 0.75, RMSE CV = 0.65 MJ/kg DM, RPD CV = 2.11. Interestingly, the accuracy was further out-performed by considering important hyperspectral and environmental variables using RF combined with recursive feature elimination (RFE) (CP: R 2 CV = 0.80, RMSE CV = 1.68%, RPD CV = 2.23; ME: R 2 CV = 0.78, RMSE CV = 0.61 MJ/kg DM, RPD CV = 2.19). Similar performance trends were noticed with validation data. Utilizing the best model, spatial pasture quality maps were created across the farm. Overall, this study showed the potential of airborne hyperspectral data for producing accurate pasture quality maps, which will help farm managers to optimize decisions to improve environmental and economic benefits.