A pilot project combining multispectral proximal sensors and digital cameras for monitoring tropical pastures

Abstract

Abstract. Timely and accurate monitoring of pasture biomass and ground cover is necessary in livestock production systems to ensure productive and sustainable management. Interest in the use of proximal sensors for monitoring pasture status in grazing systems has increased, since data can be returned in near real time. Proximal sensors have the potential for deployment on large properties where remote sensing may not be suitable due to issues such as spatial scale or cloud cover. There are unresolved challenges in gathering reliable sensor data and in calibrating raw sensor data to values such as pasture biomass or vegetation ground cover, which allow meaningful interpretation of sensor data by livestock producers. Our goal was to assess whether a combination of proximal sensors could be reliably deployed to monitor tropical pasture status in an operational beef production system, as a precursor to designing a full sensor deployment. We use this pilot project to (1) illustrate practical issues around sensor deployment, (2) develop the methods necessary for the quality control of the sensor data, and (3) assess the strength of the relationships between vegetation indices derived from the proximal sensors and field observations across the wet and dry seasons. Proximal sensors were deployed at two sites in a tropical pasture on a beef production property near Townsville, Australia. Each site was monitored by a Skye SKR-four-band multispectral sensor (every 1 min), a digital camera (every 30 min), and a soil moisture sensor (every 1 min), each of which were operated over 18 months. Raw data from each sensor was processed to calculate multispectral vegetation indices. The data capture from the digital cameras was more reliable than the multispectral sensors, which had up to 67 % of data discarded after data cleaning and quality control for technical issues related to the sensor design, as well as environmental issues such as water incursion and insect infestations. We recommend having a system with both sensor types to aid in data interpretation and troubleshooting technical issues. Non-destructive observations of pasture characteristics, including above-ground standing biomass and fractional ground cover, were made every 2 weeks. This simplified data collection was designed for multiple years of sampling at the remote site, but had the disadvantage of high measurement uncertainty. A bootstrapping method was used to explore the strength of the relationships between sensor and pasture observations. Due to the uncertainty in the field observations, the relationships between sensor and field data are not confirmational and should be used only to inform the design of future work. We found the strongest relationships occurred during the wet season period of maximum pasture growth (January to April), with generally poor relationships outside of this period. Strong relationships were found with multispectral indices that were sensitive to the green and dry components of the vegetation, such as those containing the band in the lower shortwave infrared (SWIR) region of the electromagnetic spectrum. During the wet season the bias-adjusted bootstrap point estimate of the R2 between above-ground biomass and the normalized ratio between the SWIR and red bands (NVI-SR) was 0.72 (95 % CI of 0.28 to 0.98), while that for the percentage of green vegetation observed in three dimensions and a simple ratio between the near infrared and SWIR bands (RatioNS34) was 0.81 (95 % CI of 0.53 to 1.00). Relationships between field data and the vegetation index derived from the digital camera images were generally weaker than from the multispectral sensor data, except for green vegetation observations in two and three dimensions. Our successful pilot of multiple proximal sensors supports the design of future deployments in tropical pastures and their potential for operational use. The stringent rules we developed for data cleaning can be more broadly applied to other sensor projects to ensure quality data. Although proximal sensors observe only a small area of the pasture, they deliver continual and timely pasture measurements to inform timely on-farm decision-making.

Monitoring Seasonal Pasture Quality Degradation in the Mediterranean Montado Ecosystem: Proximal versus Remote Sensing

“…Remote sensing images used to study pastures in the montado ecosystem have the added limitation of not accessing the pasture under the tree canopies. This limitation of RS images reinforces the usefulness of PS in montado ecosystems and in any ecosystem where there is an interest in monitoring the understory vegetation [14,23].…”

“…Remote sensing techniques based on satellite images using time series are especially important for the identification of stable recurring crop patterns [22], even though the practical application is constrained by poor quality images on cloudy days [23], which can occur frequently during the spring. Figure 5 shows the reconstruction of historical NDVI trends, time series records of NDVI SAT , retrieved between February and June of 2016, 2017, and 2018.…”

A Holistic Approach to the Evaluation of the Montado Ecosystem Using Proximal Sensors

“…Sentinel-1 and Sentinel-2 satellites are also suitable for monitoring pasture development due to their temporal resolution (5 days). Applications of satellite observations are, however, often limited due to the coarse spatial resolution [ 18 ] associated with poor quality image on cloudy days [ 19 ]. In alternative to traditional satellite-based remote sensing, UAVs provide high-resolution images in real time for PA applications with the advantage of being more flexible and more independent of climatic variables for the extraction of agriculturally-useful information to predict several physiological variables [ 12 , 20 , 21 ].…”

“…In the montado ecosystems, satellite, airplane or UAV images have the disadvantage of not being able to access the pasture under the trees, which highlights the importance of proximal sensors as a solution for monitoring pasture under these conditions [ 19 ]. Advances in non-invasive techniques, based on proximal sensing technology and data analysis techniques provide information on soil, crops and associated environmental properties [ 12 ].…”

Review: Precision nutrition of ruminants: approaches, challenges and potential gains

“…A range of sensors are being investigated for this application including light detection and ranging (LiDAR) and light reflectance or absorption from the ground using spectrophotometers, redgreen-blue (RGB) imagery, 3D image reconstruction, multior hyper-spectral sensors which can be handheld or mounted on vehicles, static poles, unmanned vehicles (e.g. drones) or satellites (Handcock et al, 2016;Schaefer and Lamb, 2016). Under intensive housing conditions, feed availability has been measured succesfully using volume as a proxy of feed weight or amount using both LiDAR (McCarthy et al, 2018) and video imagery (Shelley et al, 2016), which could open oportunities for new ways of measuring feed intake in herbivores.…”