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The production of wild blueberries (Vaccinium angustifolium) contributes 112.2 million dollars yearly to Canada's revenue, which can be further increased by reducing harvest losses. A precise prediction of blueberry harvest losses is necessary to mitigate such losses. The performance of three machine learning (ML) algorithms was assessed to predict the wild blueberry harvest losses on the ground. The data from four commercial fields in Atlantic Canada (including Tracadie, Frank Webb, Small Scott, and Cooper fields) were utilized to achieve the goal. Wild blueberry losses (fruit loss on ground, leaf losses, blower losses) and yield were measured manually from randomly selected plots during mechanical harvesting. The plant height of wild blueberry, field slope, and fruit zone readings were collected from each of the plots. For the purpose of predicting ground loss as a function of fruit zone, plant height, fruit production, slope, leaf loss, and blower damage, three ML models i.e., support vector regression (SVR), linear regression (LR), and random forest (RF)—were used. Statistical parameters i.e., mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2), were used to assess the prediction accuracy of the models. The results of the correlation matrices showed that the blueberry yield and losses (leaf loss, blower loss) had medium to strong correlations accessed based on the correlation coefficient (r) range 0.37–0.79. The LR model showed the foremost predictions of ground loss as compared to all the other models analyzed. Tracadie, Frank Webb, Small Scott, and Cooper had R2 values of 0.87, 0.91, 0.91, and 0.73, respectively. Support vector regression performed comparatively better at all the fields i.e., R2 = 0.93 (Frank Webb field), R2 = 0.88 (Tracadie), and R2 = 0.79 (Cooper) except Small Scott field with R2 = 0.07. When comparing the actual and anticipated ground loss, the SVR performed best (R2 = 0.79–0.93) as compared to the other two algorithms i.e., LR (R2 = 0.73 to 0.92), and RF (R2 = 0.53 to 0.89) for the three fields. The outcomes revealed that these ML algorithms can be useful in predicting ground losses during wild blueberry harvesting in the selected fields. [ABSTRACT FROM AUTHOR]

Freely-available satellite data streams and the ability to process these data on cloud-computing platforms such as Google Earth Engine have made frequent, large-scale landcover mapping at high resolution a real possibility. In this paper we apply these technologies, along with machine learning, to the mapping of peatlands–a landcover class that is critical for preserving biodiversity, helping to address climate change impacts, and providing ecosystem services, e.g., carbon storage–in the Boreal Forest Natural Region of Alberta, Canada. We outline a data-driven, scientific framework that: compiles large amounts of Earth observation data sets (radar, optical, and LiDAR); examines the extracted variables for suitability in peatland modelling; optimizes model parameterization; and finally, predicts peatland occurrence across a large boreal area (397, 958 km2) of Alberta at 10 m spatial resolution (equalling 3.9 billion pixels across Alberta). The resulting peatland occurrence model shows an accuracy of 87% and a kappa statistic of 0.57 when compared to our validation data set. Differentiating peatlands from mineral wetlands achieved an accuracy of 69% and kappa statistic of 0.37. This data-driven approach is applicable at large geopolitical scales (e.g., provincial, national) for wetland and landcover inventories that support long-term, responsible resource management. [ABSTRACT FROM AUTHOR]

• Establish a generalized framework which is capable for both energy demand and CO 2 emissions forecasting in the transportation sector. • Develop a novel multi-objective mathematical model integrating machine learning algorithms improving the prediction accuracy. • Identify and compare multiple machine learning algorithms with the developed multi-objective mathematical Model. • Quantify effects of energy consumption on CO 2 emissions at different levels and compare them with the various types of the energy resources. • Evaluate relationship between energy consumption and CO 2 emissions by sensitivity analysis with a Canada case study. Managing energy demand and reducing greenhouse gas emissions are among the most significant challenges ahead for many countries. Accurate prediction of energy demand and CO 2 emissions is an important method to tackle the challenge. In addition, as a major source of energy consumption and CO 2 emissions, the transportation sector is at the center of the problem. This study employs a hybrid approach integrating a multi-objective mathematical model with data-driven machine learning algorithms to predict energy demand and CO 2 emissions in the transportation sector with improved accuracy. Sensitivity analyses are conducted to evaluate the impact of individual and joint consumptions of different energy resources on CO 2 emissions. Case studies are conducted with countrywide statistics in Canada where the prediction indicates that energy demand and CO 2 emissions will increase by 34.72% and 50.02% from 2019 to 2048 in the Canadian transportation sector. Results also indicated the varied impacts of different energy resources such that a 5% increase in energy demand in oil, gas, electricity, and renewable energy will result in + 4.8%, +0.315%, +0.28%, and −0.51% changes in CO 2 emissions respectively. The proposed framework with integrated prediction model and the sensitivity analyses can help identify critical factors impacting CO 2 emissions in the transportation sector and provide quantitative references for energy demand management and CO 2 emissions reduction. [ABSTRACT FROM AUTHOR]

This research aimed at exploiting the joint use of machine learning and polarimetry for improving the retrieval of surface soil moisture content (SMC) from synthetic aperture radar (SAR) acquisitions at C-band. The study was conducted on two agricultural areas in Canada, for which a series of RADARSAT-2 (RS2) images were available along with direct measurements of SMC from in situ stations. The analysis confirmed the sensitivity of RS2 backscattering (σ°) to SMC. The comparison of SMC with the compact polarimetry (CP) parameters, computed from the RS2 acquisitions by the CP data simulator, pointed out that some CP parameters had a sensitivity to SMC equal or better than σ°, with correlation coefficients up to R ≃ 0.4. Based on these results, the potential of machine learning (ML) for SMC retrieval was exploited by implementing and testing on the available data an artificial neural network (ANN) algorithm. The algorithm was implemented using several combinations of σ° and CP parameters. Validation results of the algorithm with in situ observations confirmed the promising capabilities of the ML techniques for SMC monitoring. Furthermore, results pointed out the potential of CP in improving the SMC retrieval accuracy, especially when used in combination with linearly polarized σ°. Depending on the considered input combination, the ANN algorithm was able to estimate SMC with Root Mean Square Error (RMSE) between 3% and 7% of SMC and R between 0.7 and 0.9. [ABSTRACT FROM AUTHOR]