Aerial imagery for yield prediction

UAVs enable fast, high resolution image capture of cotton fields. These images are typically assessed manually to identify areas of stress or reduced productivity. However, these assessments are not currently linked directly with on-farm management decisions. NCEA has developed software that determines yield prediction and irrigation requirements from: (i) UAV images; (ii) automated image analysis that extract cotton growth rates; and (iii) biophysical cotton model. CottonInfo extension officers and agronomists collected imagery in three regions in the 2016/17 and 2017/18 cotton seasons. Yield predictions from the evaluations in the 2016/17 season were within 5% of the final yield

Simulation of site-specific irrigation control strategies with sparse input data

Crop and irrigation water use efficiencies may be improved by managing irrigation application timing and volumes using physical and agronomic principles. However, the crop water requirement may be spatially variable due to different soil properties and genetic variations in the crop across the field. Adaptive control strategies can be used to locally control water applications in response to in-field temporal and spatial variability with the aim of maximising both crop development and water use efficiency. A simulation framework ‘VARIwise’ has been created to aid the development, evaluation and management of spatially and temporally varied adaptive irrigation control strategies (McCarthy et al., 2010). VARIwise enables alternative control strategies to be simulated with different crop and environmental conditions and at a range of spatial resolutions. An iterative learning controller and model predictive controller have been implemented in VARIwise to improve the irrigation of cotton. The iterative learning control strategy involves using the soil moisture response to the previous irrigation volume to adjust the applied irrigation volume applied at the next irrigation event. For field implementation this controller has low data requirements as only soil moisture data is required after each irrigation event. In contrast, a model predictive controller has high data requirements as measured soil and plant data are required at a high spatial resolution in a field implementation. Model predictive control involves using a calibrated model to determine the irrigation application and/or timing which results in the highest predicted yield or water use efficiency. The implementation of these strategies is described and a case study is presented to demonstrate the operation of the strategies with various levels of data availability. It is concluded that in situations of sparse data, the iterative learning controller performs significantly better than a model predictive controller

Automated camera-based crop and irrigation monitoring

Plant growth and irrigation advance rate monitoring is required for crop and irrigation management in broadacre surface irrigated fields. However, this monitoring is typically manual, labour-intensive and conducted in a limited number of locations in the field which may not represent the whole field. A thermal camera on a tower has been used to determine location of water in the field during surface irrigation events; and low-cost cameras on field vehicles and irrigation machines have been used to map cotton growth and fruiting. These systems potentially enable automation of surface irrigation cut-off to improve water use efficiency and improve agronomic and irrigation management

Effect of spatial variability on data requirements for site-specific irrigation

Irrigation control strategies that consider infield spatial variability can lead to improved crop productivity and water use efficiencies. The irrigation control approach that optimises crop and water productivity is expected to depend on the variability of irrigation infiltration and soil and plant properties within specific fields. Field data collected in 2013/14 was combined with a simulation study to determine the spatial and temporal data requirements for site-specific irrigation, relative to the amount of infield variability. This paper presents an exploration of the number of data points/furrows to monitor irrigation application, soil moisture, plant growth and fruit load to control irrigation application and optimise productivity

A Walk in a Dream

A Walk in a Dream is a body of work consisting of 25 16 x 16 color prints. The photos are of nature looking at contrasting textures and focus. The photographer used a Holga camera at multiple state parks. The negatives were then scanned and manipulated in Photoshop. The artist has explored Henri Cartier-Bresson’s photography and Gaston Bachelard’s writings to compare to her work. Her artist statement is as follows: In today’s world it is hard to disconnect from media. Even when we are surrounded by natural beauty we still feel a need to connect with the digital world. Photographs can make us feel as if we are walking into a dream. Connecting not only with the world around us but also with the visual culture that we seem to desire. We feel for an instant we can stay in the moment and not have our attention diverted. As we then drift off into our own reality. The photographs in this body of work bring the images our personal history; the things that make us, us. Allowing the viewer the opportunity to explore the idea, that even though everyone sees the same things in this world, each individual has their own unique perspective and reality

Machine vision App for automated cotton insect counting: initial development and first results

Silverleaf whitefly, cotton aphids and spider mites cause cotton yield loss through plant feeding and lint contamination from waste secretions. Agronomists determine if control action is required from weekly monitoring of changes in pest counts. This manual sampling is labour-intensive as hundreds of leaves are sampled at 20-30 leaves per 25 hectares of cotton and examined by eye for the presence and density of each pest. Machine vision has potential to automate the pest counting on each leaf using infield cameras and image analysis software. There is potential to transfer the machine vision algorithms to a mobile device App for agronomist to enable real-time photo capture and analysis for pest counting. This App would standardise pest counting between different observers, improve chemical control decisions, provide a convenient method for logging and viewing data for each field, and inform Area Wide Management from silverleaf whitefly nymph counts. Data collection and software development have been conducted to develop the image analysis algorithms for detecting silverleaf whitefly nymphs. A dataset of training images was captured from glasshouses cultures of whitefly and commercial cotton farms in southern Queensland with three smartphone models. Image analysis algorithms were developed to extract numbers of silverleaf whitefly nymphs (3rd/4th instar) on each leaf. Two image analysis methods were implemented: a segmentation-based approach, and a machine learning approach. The segmentation-based approach and machine learning approach detected silverleaf whitely nymphs with up to 67% and 79% accuracy, respectively. The image analysis algorithms will be refined through parameter optimisation and incorporated into an App that will be evaluated by agronomists in the 2019/20 season. The image analysis algorithms will be extended to cotton aphids and mites as all three insects can occur simultaneously

In-season yield prediction using VARIwise

In-season yield prediction supports improved agronomic management and planning for crop sales and insurance contracts. Yield is currently often estimated using rules of thumb and manual boll counts. Data analytics approaches have been developed using site- and season specific multispectral satellite imagery-based correlations that require significant datasets for wider scale transferability. An alternative approach is to forecast yield using known soil-plant-atmosphere interactions in crop production models and calibrated using available field data. USQ has developed software ‘VARIwise’ to provide yield prediction throughout the season combining these models with: (i) plant parameters extracted from UAV imagery using image analysis; (ii) online soil and weather data; and (iii) on-farm management information. In the 2017/18 and 2018/19 seasons, VARIwise was evaluated at one cotton site in Goondiwindi and 16 sites in Griffith. Management zones in the field monitored using the UAV were identified from vegetation index surveys, yield maps or satellite images. Phantom 4 UAV imagery was collected monthly at each site between January and picking for calibrating the crop model. The sites had varying levels of fruit removal, hail damage and heat stress. In the 2017/18 Griffith trial, the percentage yield prediction errors were 10.2% in January, 6.0% in February, 2.5% in March, and 0.5% at picking, and in the 2018/19 Griffith trial the errors were 18.8% in January, 4.9% in February, 9.5% in March, and 10.1% at picking. In the 2018/19 Goondiwindi trial, the yield prediction percentage errors were 8.7% in February, 5.9% in March, 7.1% in April and 2.6% in May. The prediction errors at Griffith were higher in the 2018/19 season than the 2017/18 season because the sites experienced hail and heat stress that are not currently represented within the VARIwise crop model. The yield predictor will be evaluated in 2019/20 to improve performance under insect and hail damage

Real-time irrigation decision-making and control for site-specific irrigation of cotton using a centre pivot, 2012/13

Automated, site-specific irrigation control systems provide opportunities to deliver irrigation requirements when and where they are needed in spatially variable fields. Site-specific irrigation hardware developed for centre pivots and lateral moves currently involves loading the site-specific irrigation volumes before the irrigation event. However, the required irrigation application often changes during the irrigation event depending on the time taken for the machine to pass over the field. The irrigation volume may be further refined and updated in real-time during the irrigation event using infield measurements (e.g. weather, soil-water) or measurements of the crop from sensors mounted on the irrigation machine (e.g. cameras). The real-time irrigation control framework 'VARIwise' automatically determines site-specific irrigation requirements using weather, soil-water and plant growth measurements. These use control strategies and crop production models to predict irrigation application that achieve the desired performance objective (e.g. maximise crop yield, water productivity). An adaptive control strategy trial was conducted on a span of a centre pivot on a cotton crop at Jondaryan, QLD in 2012/13 to demonstrate the integration of infield sensors with a real-time irrigation control system. This utilised real-time, Internet-enabled irrigation control hardware, weather data, soil-water sensors, irrigation machine mounted plant sensing systems and a processor running VARIwise. The plant sensing systems estimated plant density, flower count and boll count from images, and plant height from a distance sensor. The adaptive control trials produced an average yield improvement of 7%, and water use reductions of 4% compared with industry-standard irrigation treatment using FAO-56

Automated camera-based crop monitoring and site-specific irrigation control systems

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Automated camera-based crop monitoring and site-specific irrigation control systems for cotton, horticulture and dairy pasture

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