Predicting spatiotemporal yield variability to aid arable precision agriculture in New Zealand : a case study of maize-grain crop production in the Waikato region : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Agriculture and Horticulture at Massey University, Palmerston North, New Zealand

Precision agriculture attempts to manage within-field spatial variability by applying suitable inputs at the appropriate time, place, and amount. To achieve this, delineation of field-specific management zones (MZs), representing significantly different yield potentials are required. To date, the effectiveness of utilising MZs in New Zealand has potentially been limited due to a lack of emphasis on the interactions between spatiotemporal factors such as soil texture, crop yield, and rainfall. To fill this research gap, this thesis aims to improve the process of delineating MZs by modelling spatiotemporal interactions between spatial crop yield and other complementary factors. Data was collected from five non-irrigated field sites in the Waikato region, based on the availability of several years of maize harvest data. To remove potential yield measurement errors and improve the accuracy of spatial interpolation for yield mapping, a customised filtering algorithm was developed. A supervised machine-learning approach for predicting spatial yield was then developed using several prediction models (stepwise multiple linear regression, feedforward neural network, CART decision tree, random forest, Cubist regression, and XGBoost). To provide insights into managing spatiotemporal yield variability, predictor importance analysis was conducted to identify important yield predictors. The spatial filtering method reduced the root mean squared errors of kriging interpolation for all available years (2014, 2015, 2017 and 2018) in a tested site, suggesting that the method developed in R programme was effective for improving the accuracy of the yield maps. For predicting spatial yield, random forest produced the highest prediction accuracies (R² = 0.08 - 0.50), followed by XGBoost (R² = 0.06 - 0.39). Temporal variables (solar radiation, growing degree days (GDD) and rainfall) were proven to be salient yield predictors. This research demonstrates the viability of these models to predict subfield spatial yield, using input data that is inexpensive and readily available to arable farms in New Zealand. The novel approach employed by this thesis may provide opportunities to improve arable farming input-use efficiency and reduce its environmental impact

Mapping potential surface ponding in agriculture using UAV-SfM

Among the environmental problems that could affect agriculture, one of the most critical is ponding. This may be defined as water storage on the surface in concavities and depressions due to soil saturation. Stagnant water can seriously affect crops and the management of agricultural landscapes. It is mainly caused by prolonged rainfall events, soil type, or wrong mechanization practices, which cause soil compaction. To better understand this problem and thus provide adequate solutions to reduce the related risk, high-resolution topographic information could be strategically important because it offers an accurate representation of the surface morphology. In the last decades, new remote sensing techniques provide interesting opportunities to understand the processes on the Earth's surface based on geomorphic signatures. Among these, Uncrewed Aerial Vehicles (UAVs), combined with the structure-from-motion (SfM) photogrammetry technique, represent a solid, low-cost, rapid, and flexible solution for geomorphological analysis. This study aims to present a new approach to detect the potential areas exposed to water stagnation at the farm scale. The high-resolution digital elevation model (DEM) from UAV-SfM data is used to do this. The potential water depth was calculated in the DEM using the relative elevation attribute algorithm. The detection of more pronounced concavities and convexities allowed an estimation and mapping of the potential ponding conditions. The results were assessed by observations and field measurements and are promising, showing a Cohen's k(X) accuracy of 0.683 for the planimetric extent of the ponding phenomena and a Pearson's rxy coefficient of 0.971 for the estimation of pond water depth. The proposed workflow provides a useful indication to stakeholders for better agricultural management in lowland landscapes

Graph deep learning model for network-based predictive hotspot mapping of sparse spatio-temporal events

The predictive hotspot mapping of sparse spatio-temporal events (e.g., crime and traffic accidents) aims to forecast areas or locations with higher average risk of event occurrence, which is important to offer insight for preventative strategies. Although a network-based structure can better capture the micro-level variation of spatio-temporal events, existing deep learning methods of sparse events forecasting are either based on area or grid units due to the data sparsity in both space and time, and the complex network topology. To overcome these challenges, this paper develops the first deep learning (DL) model for network-based predictive mapping of sparse spatio-temporal events. Leveraging a graph-based representation of the network-structured data, a gated localised diffusion network (GLDNet) is introduced, which integrating a gated network to model the temporal propagation and a novel localised diffusion network to model the spatial propagation confined by the network topology. To deal with the sparsity issue, we reformulate the research problem as an imbalance regression task and employ a weighted loss function to train the DL model. The framework is validated on a crime forecasting case of South Chicago, USA, which outperforms the state-of-the-art benchmark by 12% and 25% in terms of the mean hit rate at 10% and 20% coverage level, respectively

Multi-Objective, Multiphasic and Multi-Step Self-Optimising Continuous Flow Systems

Continuous flow chemistry is currently a vibrant area of research, offering many advantages over traditional batch chemistry. These include: enhanced heat and mass transfer, access to a wider range of reaction conditions, safer use of hazardous reagents, telescoping of multi-step reactions and readily accessible photochemistry. As such, there has been an increase in the adoption of continuous flow processes towards the synthesis of active pharmaceutical ingredients (APIs) in recent years. Advances in the automation of laboratory equipment has transformed the way in which routine experimentation is performed, with the digitisation of research and development (R&D) greatly reducing waste in terms of human and material resources. Self-optimising systems combine algorithms, automated control and process analytics for the feedback optimisation of continuous flow reactions. This provides efficient exploration of multi-dimensional experimental space, and accelerates the identification of optimum conditions. Therefore, this technology directly aligns with the drive towards more sustainable process development in the pharmaceutical industry. Yet the uptake of these systems by industrial R&D departments remains relatively low, suggesting that the capabilities of the current technology are still limited. The work in this thesis aims to improve existing self-optimisation technologies, to further bridge the gap between academic and industrial research. This includes introducing multi-objective optimisation algorithms and applying them towards the synthesis of APIs, developing a new multiphasic CSTR cascade reactor with photochemical capabilities and including downstream work-up operations in the optimisation of multi-step processes

Stock predictions and population indicators for Australia's east coast saucer scallop fishery

This project undertook analyses to understand the roles of overfishing and the environment on saucer scallops. Results of this study indicated a decline in numbers of spawning scallops. High levels of fishing effort since the 1980s contributed to stock depletion. In addition, environmental influences such as increased sea surface temperatures (SST) may have amplified scallop mortality rates. These findings can be applied to efforts for stock rehabilitation for the scallop fishery between Yeppoon and K’gari (Fraser Island). Recommended management actions include reduction of the spatial intensity of fishing effort applied, and ensuring sufficient annual spawning to support the scallop population and fishery

New sensing methods for scheduling variable rate irrigation to improve water use efficiency and reduce the environmental footprint : a thesis presented in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Soil Science at Massey University, Palmerston North, New Zealand

Figures are re-used under an Attribution 4.0 International (CC BY 4.0) license, or are not copyrighted.Irrigation is the largest user of allocated freshwater, so conservation of water use should begin with improving the efficiency of crop irrigation. Improved irrigation management is necessary for humid areas such as New Zealand in order to produce greater yields, overcome excessive irrigation and eliminate nitrogen losses due to accelerated leaching and/or denitrification. The impact of two different climatic regimes (Hawkes Bay, Manawatū) and soils (free and imperfect drainage) on irrigated pea (Pisum sativum., cv. ‘Ashton’) and barley (Hordeum vulgare., cv. ‘Carfields CKS1’) production was investigated. These experiments were conducted to determine whether variable-rate irrigation (VRI) was warranted. The results showed that both weather conditions and within-field soil variability had a significant effect on the irrigated pea and barley crops (pea yield - 4.15 and 1.75 t/ha; barley yield - 4.0 and 10.3 t/ha for freely and imperfectly drained soils, respectively). Given these results, soil spatial variability was characterised at precision scales using proximal sensor survey systems: to inform precision irrigation practice. Apparent soil electrical conductivity (ECa) data were collected by a Dualem-421S electromagnetic (EM) survey, and the data were kriged into a map and modelled to predict ECa to depth. The ECa depth models were related to soil moisture (θv), and the intrinsic soil differences. The method was used to guide the placement of soil moisture sensors. After quantifying precision irrigation management zones using EM technology, dynamic irrigation scheduling for a VRI system was used to efficiently irrigate a pea crop (Pisum sativum., cv. ‘Massey’) and a French bean crop (Phaseolus vulgaris., cv. ‘Contender’) over one season at the Manawatū site. The effects of two VRI scheduling methods using (i) a soil water balance model and (ii) sensors, were compared. The sensor-based technique irrigated 23–45% less water because the model-based approach overestimated drainage for the slower draining soil. There were no significant crop growth and yield differences between the two approaches, and water use efficiency (WUE) was higher under the scheduling regime based on sensors. ii To further investigate the use of sensor-based scheduling, a new method was developed to assess crop height and biomass for pea, bean and barley crops at high field resolution (0.01 m) using ground-based LiDAR (Light Detection and Ranging) data. The LiDAR multi-temporal, crop height maps can usefully improve crop coefficient estimates in soil water balance models. The results were validated against manually measured plant parameters. A critical component of soil water balance models, and of major importance for irrigation scheduling, is the estimation of crop evapotranspiration (ETc) which traditionally relies on regional climate data and default crop factors based on the day of planting. Therefore, the potential of a simpler, site-specific method for estimation of ETc using in-field crop sensors was investigated. Crop indices (NDVI, and canopy surface temperature, Tc) together with site-specific climate data were used to estimate daily crop water use at the Manawatū and Hawkes Bay sites (2017-2019). These site-specific estimates of daily crop water use were then used to evaluate a calibrated FAO-56 Penman-Monteith algorithm to estimate ETc from barley, pea and bean crops. The modified ETc–model showed a high linear correlation between measured and modelled daily ETc for barley, pea, and bean crops. This indicates the potential value of in-field crop sensing for estimating site-specific values of ETc. A model-based, decision support software system (VRI–DSS) that automates irrigation scheduling to variable soils and multiple crops was then tested at both the Manawatū and Hawkes Bay farm sites. The results showed that the virtual climate forecast models used for this study provided an adequate prediction of evapotranspiration but over predicted rainfall. However, when local data was used with the VRI–DSS system to simulate results, the soil moisture deficit showed good agreement with weekly neutron probe readings. The use of model system-based irrigation scheduling allowed two-thirds of the irrigation water to be saved for the high available water content (AWC) soil. During the season 2018 – 2019, the VRI–DSS was again used to evaluate the level of available soil water (threshold) at which irrigation should be applied to increase WUE and crop water productivity (WP) for spring wheat (Triticum aestivum L., cv. ‘Sensas’) on the sandy loam and silt loam soil zones at the Manawatū site. Two irrigation thresholds (40% and 60% AWC), were investigated in each soil zone along with a rainfed control. Soil water uptake pattern was affected mainly by the soil type rather than irrigation. The soil iii water uptake decreased with soil depth for the sandy loam whereas water was taken up uniformly from all depths of the silt loam. The 60% AWC treatments had greater irrigation water use efficiency (IWUE) than the 40% AWC treatments, indicating that irrigation scheduling using a 60% AWC trigger could be recommended for this soil-crop scenario. Overall, in this study, we have developed new sensor-based methods that can support improved spatial irrigation water management. The findings from this study led to a more beneficial use of agricultural water

Optimisation Methods For Training Deep Neural Networks in Speech Recognition

Automatic Speech Recognition (ASR) is an example of a sequence to sequence level classification task where, given an acoustic waveform, the goal is to produce the correct word level hypotheses. In machine learning, a classification problem such as ASR is solved in two stages: an inference stage that models the uncertainty associated with the choice of hypothesis given the acoustic waveform using a mathematical model, and a decision stage which employs the inference model in conjunction with decision theory to make optimal class assignments. With the advent of careful network initialisation and GPU computing, hybrid Hidden Markov Models (HMMs) augmented with Deep Neural Networks (DNNs) have shown to outperform traditional HMMs using Gaussian Mixture Models (GMMs) in solving the inference problem for ASR. In comparison to GMMs, DNNs possess a better capability to model the underlying non-linear data manifold due to their deep and complex structure. While the structure of such models gives rich modelling capability, it also creates complex dependencies between the parameters which can make learning difficult via first order stochastic gradient descent (SGD). The task of finding the best procedure to train DNNs continues to be an active area of research and has been made even more challenging by the availability of ever more training data. This thesis focuses on designing better optimisation approaches to train hybrid HMM-DNN models using sequence level discriminative criterion which is a natural loss function that preserves the sequential ordering of frames within a spoken utterance. The thesis presents an implementation of the second order Hessian Free (HF) optimisation method, and shows how the method can made efficient through appropriate modifications to the Conjugate Gradient algorithm. To achieve better convergence than SGD, this work explores the Natural Gradient method to train DNNs with discriminative sequence training. In the DNN literature, the method has been applied to train models for the Maximum Likelihood objective criterion. A novel contribution of this thesis is to extend this approach to the domain of Minimum Bayes Risk objective functions for discriminative sequence training. With sigmoid models trained on a 50hr and 200hr training set from the Multi-Genre Broadcast 1 (MGB1) transcription task, the NG method applied in a HF styled optimisation framework is shown to achieve better Word Error Rate (WER) reductions on the MGB1 development set than SGD from sequence training. This thesis also addresses the particular issue of overfitting between the training criterion and WER, that primarily arises during sequence training of DNN models that use Rectified Linear Units (ReLUs) as activation functions. It is shown how by scaling with the Gauss Newton matrix, the HF method unlike other approaches can overcome this issue. Seeing that different optimisers work best with different models, it is attractive to have a consistent optimisation framework that is agnostic to the choice of activation function. To address the issue, this thesis develops the geometry of the underlying function space captured by different realisations of DNN model parameters, and presents the design considerations for an optimisation algorithm to be well defined on this space. Building on this analysis, a novel optimisation technique called NGHF is presented that uses both the direction of steepest descent on a probabilistic manifold and local curvature information to effectively probe the error surface. The basis of the method relies on an alternative derivation of Taylor’s theorem using the concepts of manifolds, tangent vectors and directional derivatives from the perspective of Information Geometry. Apart from being well defined on the function space, when framed within a HF style optimisation framework, the method of NGHF is shown to achieve the greatest WER reductions from sequence training on the MGB1 development set with both sigmoid and ReLU based models trained on the 200hr MGB1 training set. The evaluation of the above optimisation methods in training different DNN model architectures is also presented.IDB Cambridge International Scholarshi