Macadamia Orchard Planting Year and Area Estimation at a National Scale

Accurate estimates of tree crop orchard age and historical crop area are important to develop yield prediction algorithms, and facilitate improving accuracy in ongoing crop forecasts. This is particularly relevant for the increasingly productive macadamia industry in Australia, where knowledge of tree age, as well as total planted area, are important predictors of productivity, and the area devoted to macadamia orchards is rapidly increasing. We developed a technique to aggregate more than 30 years of historical imagery, generate summary tables from the data, and search multiple combinations of parameters to find the most accurate planting year prediction algorithm. This made use of known planting dates of more than 90 macadamia blocks spread across multiple growing regions. The selected algorithm achieved a planting year mean absolute error of 1.7 years. The algorithm was then applied to all macadamia features in east Australia, as defined in an recent Australian tree crops map, to determine the area planted per year and the total cumulative area of macadamia orchards in Australia. The area estimates were refined by improving the resolution of the mapped macadamia features, by removing non-productive areas based on an optimal vegetation index threshold

Block-level macadamia yield forecasting using spatio-temporal datasets

Early crop yield forecasts provide valuable information for growers and industry to base decisions on. This work considers early forecasting of macadamia nut yield at the individual orchard block level with input variables derived from spatio-temporal datasets including remote sensing, weather and elevation. Yield data from 2012–2019, for 101 blocks belonging to 10 orchards, was obtained. We forecast yield on each test year from 2014–2019 using models trained on data from years prior to the test year. Forecasts are generated in January, for the coming harvest in March–September. A linear model using ridge regularized regression produced consistently good predictions compared with other machine learning algorithms including lasso, support vector regression and random forest. Adding meteorological variables offered little improvement over using only remote sensing variables. The 2019 forecast root mean square error at the block level was 0.8 t/ha, and mean absolute percentage error was 20.9%. When block level predictions were aggregated across the multiple orchards per region, production prediction errors were between 0–15% from 2016–2019. The ridge regression model can be easily implemented in GIS platforms to deliver block-level yield forecast maps to end users

Multisensor Capacitance Probes for Simultaneously Monitoring Rice Field Soil-Water-Crop-Ambient Conditions

Multisensor capacitance probes (MCPs) have traditionally been used for soil moisture monitoring and irrigation scheduling. This paper presents a new application of these probes, namely the simultaneous monitoring of ponded water level, soil moisture, and temperature profile, conditions which are particularly important for rice crops in temperate growing regions and for rice grown with prolonged periods of drying. WiFi-based loggers are used to concurrently collect the data from the MCPs and ultrasonic distance sensors (giving an independent reading of water depth). Models are fit to MCP water depth vs volumetric water content (VWC) characteristics from laboratory measurements, variability from probe-to-probe is assessed, and the methodology is verified using measurements from a rice field throughout a growing season. The root-mean-squared error of the water depth calculated from MCP VWC over the rice growing season was 6.6 mm. MCPs are used to simultaneously monitor ponded water depth, soil moisture content when ponded water is drained, and temperatures in root, water, crop and ambient zones. The insulation effect of ponded water against cold-temperature effects is demonstrated with low and high water levels. The developed approach offers advantages in gaining the full soil-plant-atmosphere continuum in a single robust sensor

The influence of nitrogen and variety on rice grain moisture content dry-down

Rice field management around maturity and harvest are some of the most difficult decisions growers face. Field drainage and harvest timing affect quality, yield, and post-harvest drying costs. These decisions are informed by grain moisture content (MC). Over three years, three sites and three varieties, we studied the field dry-down rate and time to optimal harvest MC. We showed that field-specific parameters significantly affected these characteristics, including rice variety, Nitrogen applied (NA), mid-season N uptake (NU) and dry matter (DM). Increased N and DM is associated with increased MC and thus delays time to harvest. We developed models based on linear regression and nonlinear machine learning (ML) algorithms, including parameters describing these field-specific conditions. Cross validation across the three years provided a realistic expectation of model prediction errors. A linear model with the addition of nonlinear predictors achieved competitive performance compared with more complex and less interpretable ML models. When MC was modeled as a function of days since heading, similar or better accuracy was achieved to using accumulated weather parameters. Moisture content was predicted with mean absolute error of 2.1 %. The predicted time from heading to harvest MC was improved by the inclusion of field-specific parameters (N and variety) from mean absolute error of 6.8 days to 5.7 days. The final linear regression model explained 80 % of the moisture variability in the dataset, and provided estimates of dry-down rates, moisture as a function of time, and time to reach harvest moisture. This study shows the importance of including field-specific parameters when estimating of rice harvest timing, and provides methods to model these effects

Assessment of Aquatic Weed in Irrigation Channels Using UAV and Satellite Imagery

Irrigated agriculture requires high reliability from water delivery networks and high flows to satisfy demand at seasonal peak times. Aquatic vegetation in irrigation channels are a major impediment to this, constraining flow rates. This work investigates the use of remote sensing from unmanned aerial vehicles (UAVs) and satellite platforms to monitor and classify vegetation, with a view to using this data to implement targeted weed control strategies and assessing the effectiveness of these control strategies. The images are processed in Google Earth Engine (GEE), including co-registration, atmospheric correction, band statistic calculation, clustering and classification. A combination of unsupervised and supervised classification methods is used to allow semi-automatic training of a new classifier for each new image, improving robustness and efficiency. The accuracy of classification algorithms with various band combinations and spatial resolutions is investigated. With three classes (water, land and weed), good accuracy (typical validation kappa >0.9) was achieved with classification and regression tree (CART) classifier; red, green, blue and near-infrared (RGBN) bands; and resolutions better than 1 m. A demonstration of using a time-series of UAV images over a number of irrigation channel stretches to monitor weed areas after application of mechanical and chemical control is given. The classification method is also applied to high-resolution satellite images, demonstrating scalability of developed techniques to detect weed areas across very large irrigation networks

Soil moisture forecasting for irrigation recommendation

This study integrates measured soil moisture sensor data, a remotely sensed crop vegetation index, and weather data to train models, in order to predict future soil moisture. The study was carried out on a cotton farm, with wireless soil moisture monitoring equipment deployed across five plots. Lasso, Decision Tree, Random Forest and Support Vector Machine modeling methods were trialled. Random Forest models gave consistently good results (mean 7-day prediction error from 8.0 to 16.9 kPA except in one plot with malfunctioning sensors). Linear regression with two of the most important predictor variables was not as accurate, but allowed extraction of an interpretable model. The system was implemented in Google Cloud Platform and a model was trained continuously through the season. An online irrigation dashboard was created showing previous and forecast soil moisture conditions, along with weather and normalized difference vegetation index (NDVI). This was used to guide operators in advance of irrigation water needs. The methodology developed in this study could be used as part of a closed-loop sensing and irrigation automation system

Rice ponding date detection in Australia using Sentinel-2 and Planet Fusion imagery

Rice is unique, in that yields are maximized when it is grown under ponded (or flooded) conditions. This however has implications for water use (an important consideration in water-scarce environments) and green-house gas emissions. This work aimed to provide precise predictions of the date when irrigated rice fields were ponded, on a per-field basis. Models were developed using Sentinel-2 data (with the advantage of inclusion of water-sensitive shortwave infrared bands) and Planet Fusion data (which provides daily, temporally consistent, cross-calibrated, gap-free data). Models were trained with data from both commercial farms and research sites in New South Wales, Australia, and over four growing seasons (harvest in 2018–2021). Predictions were tested on the 2022 harvest season, which included a variety of sowing and water management strategies. A time-series method was developed to provide models with features including satellite observations from before and after the date being classified (as ponded or non-ponded). Logistic regression models using time-series features produced mean absolute errors for ponding date prediction of 4.9 days using Sentinel-2 data, and 4.3 days using Planet Fusion data. The temporal frequency of the Planet Fusion data compensated for the lack of spectral bands relative to Sentinel-2

Land Cover Classification of Nine Perennial Crops Using Sentinel-1 and -2 Data

Land cover mapping of intensive cropping areas facilitates an enhanced regional response to biosecurity threats and to natural disasters such as drought and flooding. Such maps also provide information for natural resource planning and analysis of the temporal and spatial trends in crop distribution and gross production. In this work, 10 meter resolution land cover maps were generated over a 6200 km² area of the Riverina region in New South Wales (NSW), Australia, with a focus on locating the most important perennial crops in the region. The maps discriminated between 12 classes, including nine perennial crop classes. A satellite image time series (SITS) of freely available Sentinel-1 synthetic aperture radar (SAR) and Sentinel-2 multispectral imagery was used. A segmentation technique grouped spectrally similar adjacent pixels together, to enable object-based image analysis (OBIA). K-means unsupervised clustering was used to filter training points and classify some map areas, which improved supervised classification of the remaining areas. The support vector machine (SVM) supervised classifier with radial basis function (RBF) kernel gave the best results among several algorithms trialled. The accuracies of maps generated using several combinations of the multispectral and radar bands were compared to assess the relative value of each combination. An object-based post classification refinement step was developed, enabling optimization of the tradeoff between producers’ accuracy and users’ accuracy. Accuracy was assessed against randomly sampled segments, and the final map achieved an overall count-based accuracy of 84.8% and area-weighted accuracy of 90.9%. Producers’ accuracies for the perennial crop classes ranged from 78 to 100%, and users’ accuracies ranged from 63 to 100%. This work develops methods to generate detailed and large-scale maps that accurately discriminate between many perennial crops and can be updated frequently

Assessment of in-season cotton nitrogen status and lint yield prediction from unmanned aerial system imagery

The present work assessed the usefulness of a set of spectral indices obtained from an unmanned aerial system (UAS) for tracking spatial and temporal variability of nitrogen (N) status as well as for predicting lint yield in a commercial cotton (Gossypium hirsutum L.) farm. Organic, inorganic and a combination of both types of fertilizers were used to provide a range of eight N rates from 0 to 340 kg N ha−1. Multi-spectral images (reflectance in the blue, green, red, red edge and near infrared bands) were acquired on seven days throughout the season, from 62 to 169 days after sowing (DAS), and data were used to compute structure- and chlorophyll-sensitive vegetation indices (VIs). Above-ground plant biomass was sampled at first flower, first cracked boll and maturity and total plant N concentration (N%) and N uptake determined. Lint yield was determined at harvest and the relationships with the VIs explored. Results showed that differences in plant N% and N uptake between treatments increased as the season progressed. Early in the season, when fertilizer applications can still have an effect on lint yield, the simplified canopy chlorophyll content index (SCCCI) was the index that best explained the variation in N uptake and plant N% between treatments. Around first cracked boll and maturity, the linear regression obtained for the relationships between the VIs and both plant N% and N uptake was statistically significant, with the highest r2 values obtained at maturity. The normalized difference red edge (NDRE) index, and SCCCI were generally the indices that best distinguished the treatments according to the N uptake and total plant N%. Treatments with the highest N rates (from 307 to 340 kg N ha−1) had lower normalized difference vegetation index (NDVI) than treatments with 0 and 130 kg N ha−1 at the first measurement day (62 DAS), suggesting that factors other than fertilization N rate affected plant growth at this early stage of the crop. This fact affected the earliest date at which the structure-sensitive indices NDVI and the visible atmospherically resistant index (VARI) enabled yield prediction (97 DAS). A statistically significant linear regression was obtained for the relationships between SCCCI and NDRE with lint yield at 83 DAS. Overall, this study shows the practicality of using an UAS to monitor the spatial and temporal variability of cotton N status in commercial farms. It also illustrates the challenges of using multi-spectral information for fertilization recommendation in cotton at early stages of the crop