Food demand is expected to rise significantly by 2050 due to the increase in population; additionally, receding water levels, climate change, and a decrease in the amount of available arable land will threaten food production. To address these challenges and increase food security, input cost reductions and yield optimization can be accomplished using yield precision maps created by machine learning models; however, without considering the spatial structure of the data, the precision map’s accuracy evaluation assessment risks being over-optimistic, which may encourage poor decision making that can lead to negative economic impacts (e.g., lowered crop yields). In fact, most machine learning research involving spatial data, including the unmanned aerial vehicle (UAV) imagery-based yield prediction literature, ignore spatial structure and likely obtain over-optimistic results. The present work is a UAV imagery-based corn yield prediction study that analyzed the effects of image spatial and spectral resolution, image acquisition date, and model evaluation scheme on model performance. We used various spatial generalization evaluation methods, including spatial cross-validation (CV), to (a) identify over-optimistic models that overfit to the spatial structure found inside datasets and (b) estimate true model generalization performance. We compared and ranked the prediction power of 55 vegetation indices (VIs) and five spectral bands over a growing season. We gathered yield data and UAV-based multispectral (MS) and red-green-blue (RGB) imagery from a Canadian smart farm and trained random forest (RF) and linear regression (LR) models using 10-fold CV and spatial CV approaches. We found that imagery from the middle of the growing season produced the best results. RF and LR generally performed best with high and low spatial resolution data, respectively. MS imagery led to generally better performance than RGB imagery. Some of the best-performing VIs were simple ratio index(near-infrared and red-edge), normalized difference red-edge index, and normalized green index. We found that 10-fold CV coupled with spatial CV could be used to identify over-optimistic yield prediction models. When using high spatial resolution MS imagery, RF and LR obtained 0.81 and 0.56 correlation coefficient (CC), respectively, when using 10-fold CV, and obtained 0.39 and 0.41, respectively, when using a k-means-based spatial CV approach. Furthermore, when using only location features, RF and LR obtained an average CC of 1.00 and 0.49, respectively. This suggested that LR had better spatial generalizability than RF, and that RF was likely being over-optimistic and was overfitting to the spatial structure of the data.

Precision agriculture and smart farming can enable real-time decision-making to optimize resources and lower costs via data-driven model predictions. Adoption rates of smart farming systems are unfortunately low due to farmers’ privacy concerns and the high initial monetary costs of deploying such systems. High monetary costs can be lowered by replacing expensive sensing equipment with machine learning models. Cloud computing can be used to train models, but this suffers from poor privacy. Instead, fog and edge computing can train local models, but important geographical trends may be lost due to data segmentation. Federated learning can be used to address these challenges. A privacy-aware Internet of Things (IoT)-based smart farming architecture that uses federated learning was proposed. A prototype was deployed to gather sensor data from a local Canadian smart farm in Ottawa, Ontario. For various data-driven models, we perform nitrous oxide prediction experiments using centralized, local, federated, and distributed ensemble learning. We found that federated and ensemble learning can compete similarly well with centralized learning. Our results demonstrate that our methodology can potentially replace expensive nitrous oxide emission sensing equipment using inexpensive sensors combined with predictive analytics models.

In recent years, big data produced by the Internet of Things has enabled new kinds of useful applications. One such application is monitoring a fleet of vehicles in real time to predict their remaining useful life. The consensus self-organized models (COSMO) approach is an example of a predictive maintenance system. The present work proposes a novel Internet of Things based architecture for predictive maintenance that consists of three primary nodes: the vehicle node, the server leader node, and the root node, which enable on-board vehicle data processing, heavy-duty data processing, and fleet administration, respectively. A minimally viable prototype of the proposed architecture was implemented and deployed to a local bus garage in Gatineau, Canada.The present work proposes improved consensus self-organized models (ICOSMO), a fleet-wide unsupervised dynamic sensor selection algorithm. To analyze the performance of ICOSMO, a fleet simulation was implemented. The J1939 data gathered from a hybrid bus was used to generate synthetic data in the simulations. Simulation results that compared the performance of the COSMO and ICOSMO approaches revealed that in general ICOSMO improves the average area under the curve of COSMO by approximately 1.5% when using the Cosine distance and 0.6% when using Hellinger distance.

Agriculture and Agri-Food Canada, in its unwavering commitment to sustainable agriculture, has launched a program to reduce nitrous oxide (N ₂ O) emissions from fertilizer utilization in farming practices. This initiative is a response to the pressing environmental and climate challenges we face. To achieve our goal, we must delve into the mechanism of N ₂ O emission by measuring and predicting the flux of N ₂ O. This study proposes a novel architecture for neural network models, namely the agriculture-informed neural network (AINN) model, consisting of recurrent neural networks and a process-based ecosystem model, the Dynamic Land Ecosystem Model (DLEM), to predict N ₂ O emissions from farming. During the 2021 and 2022 growing seasons, field data on the flux of N ₂ O, soil temperature, and soil moisture were collected. However, the amount of nitrate in the soil was missing since collecting accurate data on nitrate quantities from the soil was challenging. Therefore, assumptions about the nitrate quantity in the soil were made when training and testing AINN with the data collected from the 2021 and 2022 growing seasons. In 2024, from January to April, an indoor experiment under controlled conditions was successfully executed to collect data on nitrate quantity in the soil. This experiment demonstrated that nitrate quantity is an essential factor for predicting the emission of N ₂ O. To demonstrate the versatility of the AINN across various neural networks, we conduct a comprehensive comparison with four state-of-the-art models: multilayer perceptron, convolutional neural network, long short-term memory, and Transformer. Our experiment and simulation results unequivocally demonstrate that the performance of AINN is superior to single neural network models. The DLEM component of the AINN acts as a regularizer, facilitating the training process of the AINN. This mathematical formulation transforms the problem of N ₂ O emission into a constrained optimization issue, minimizing the explicit objective function and satisfying the constraints of the parameters fed into the DLEM in the AINN. The empirical results show that by incorporating information from the agricultural field, the AINN significantly reduces the generalization error compared to the corresponding neural network, underscoring its potential to revolutionize the field of neural network modeling.

Nitrous oxide (N ₂ O) is a powerful greenhouse gas (GHG) that has nearly 273 times more global warming potential than carbon dioxide over a 100-year period. By 2030, the Canadian government is requiring Canadian farmers to reduce their synthetic fertilizer-based GHG emissions by one third. Measuring N ₂ O emissions is therefore important, but high frequency sampling requires expensive sensing equipment. Therefore, we propose replacing the expensive equipment with an affordable in-field Internet of Things (IoT) sensing device equipped with intelligence to make reasonably accurate N ₂ O emission predictions by using only proximal sensor data. We gathered N ₂ O emission, weather, and soil sensor data from a smart farm located in Ottawa, Ontario, Canada, during the 2021, 2022, and 2023 growing seasons. We built a soil sensing microprocessor-based prototype. We performed N ₂ O emission prediction single-year interpolation (or gap-filling) and multi-year extrapolation experiments using data-driven models. Random forest and long short-term memory (LSTM) were the best performing models at interpolating, achieving 0.70–0.90 and 0.71–0.89 R ² , respectively. When training models using 2021 data to predict 2022 emissions, reasonable accuracy (up to 0.62 R ² ) was achieved by the multilayer perceptron model, which was one of the best performing models, alongside LSTM, in these experiments.