title: Predicting apple bruising relationships using machine learning

authors: Holmes, Geoffrey

subjects: computer science

abstract: Many models have been used to describe the influence of internal or external factors on apple bruising. Few of these have addressed the application of derived relationships to the evaluation of commercial operations. From an industry perspective, a model must enable fruit to be rejected on the basis of a commercially significant bruise and must also accurately quantify the effects of various combinations of input features (such as cultivar, maturity, size, and so on) on bruise prediction. Input features must in turn have characteristics which are measurable commercially; for example, the measure of force should be impact energy rather than energy absorbed. Further, as the commercial criteria for acceptable damage levels change, the model should be versatile enough to regenerate new bruise thresholds from existing data. 
Machine learning is a burgeoning technology with a vast range of potential applications particularly in agriculture where large amounts of data can be readily collected [1]. The main advantage of using a machine learning method in an application is that the models built for prediction can be viewed and understood by the owner of the data who is in a position to determine the usefulness of the model, an essential component in a commercial environment.

text: Predicting apple bruising relationships using machine learning Many models have been used to describe the influence of internal or external factors on apple bruising. Few of these have addressed the application of derived relationships to the evaluation of commercial operations. From an industry perspective, a model must enable fruit to be rejected on the basis of a commercially significant bruise and must also accurately quantify the effects of various combinations of input features (such as cultivar, maturity, size, and so on) on bruise prediction. Input features must in turn have characteristics which are measurable commercially; for example, the measure of force should be impact energy rather than energy absorbed. Further, as the commercial criteria for acceptable damage levels change, the model should be versatile enough to regenerate new bruise thresholds from existing data. 
Machine learning is a burgeoning technology with a vast range of potential applications particularly in agriculture where large amounts of data can be readily collected [1]. The main advantage of using a machine learning method in an application is that the models built for prediction can be viewed and understood by the owner of the data who is in a position to determine the usefulness of the model, an essential component in a commercial environment.

year: 1998-04

title: Modelling the relative importance of internal and external nutrient loads on water column nutrient concentrations and phytoplankton biomass in a shallow polymictic lake

authors: Burger, David F.

subjects: lake management

abstract: Lake Rotorua is a large (area 79 km²), shallow (mean depth 10.8 m), polymictic lake in central North Island, New Zealand. The lake is eutrophic, with a mean external aerial load of 18.5 mg m⁻² d⁻¹ for total nitrogen and 1.2 mg m⁻² d⁻¹ for total phosphorus. Blooms of cyanobacteria and occasional anoxia of bottom waters occur during summer (December–March). We used a vertically resolved water quality model, DYRESM–CAEDYM, to examine the relative importance of internal and external nutrient inputs on water column nutrient concentrations and phytoplankton biomass, with particular emphasis on cyanobacteria. External nutrient loads associated with nine major inflows to the lake and three additional inflows representing smaller geothermal and coldwater flows and residual flows, were represented as inputs to the model. Other forcing inputs to the model included local meteorological data, discharge from the only outflow, the Ohau Channel, and measured rates of sediment nutrient release obtained from benthic chamber deployments which were used to prescribe ranges of sediment nutrient release that were simulated dynamically within the model. Profiles of water column nutrient concentrations, surface chlorophyll a concentrations and continuous temperature and dissolved oxygen measurements were used to validate the model. Simulated water column temperature and soluble reactive phosphorus (SRP) and ammonium (NH4) concentrations closely matched field measurements, and captured the timing and duration of stratification events as well as subsequent changes in bottom water nutrient concentrations. Surface water concentrations of chlorophyll a were also similar between simulated and observed data. Model simulations indicate that reductions in sediment nutrient fluxes would be more effective in reducing cyanobacterial biomass than similar proportional reductions in catchment fluxes, due to the coincidence of large sediment nutrient release events with high cyanobacterial biomass. This finding indicates that only a significant and prolonged reduction in external loads, which in turn reduces internal loads, will ultimately reduce cyanobacterial biomass in Lake Rotorua.

text: Modelling the relative importance of internal and external nutrient loads on water column nutrient concentrations and phytoplankton biomass in a shallow polymictic lake Lake Rotorua is a large (area 79 km²), shallow (mean depth 10.8 m), polymictic lake in central North Island, New Zealand. The lake is eutrophic, with a mean external aerial load of 18.5 mg m⁻² d⁻¹ for total nitrogen and 1.2 mg m⁻² d⁻¹ for total phosphorus. Blooms of cyanobacteria and occasional anoxia of bottom waters occur during summer (December–March). We used a vertically resolved water quality model, DYRESM–CAEDYM, to examine the relative importance of internal and external nutrient inputs on water column nutrient concentrations and phytoplankton biomass, with particular emphasis on cyanobacteria. External nutrient loads associated with nine major inflows to the lake and three additional inflows representing smaller geothermal and coldwater flows and residual flows, were represented as inputs to the model. Other forcing inputs to the model included local meteorological data, discharge from the only outflow, the Ohau Channel, and measured rates of sediment nutrient release obtained from benthic chamber deployments which were used to prescribe ranges of sediment nutrient release that were simulated dynamically within the model. Profiles of water column nutrient concentrations, surface chlorophyll a concentrations and continuous temperature and dissolved oxygen measurements were used to validate the model. Simulated water column temperature and soluble reactive phosphorus (SRP) and ammonium (NH4) concentrations closely matched field measurements, and captured the timing and duration of stratification events as well as subsequent changes in bottom water nutrient concentrations. Surface water concentrations of chlorophyll a were also similar between simulated and observed data. Model simulations indicate that reductions in sediment nutrient fluxes would be more effective in reducing cyanobacterial biomass than similar proportional reductions in catchment fluxes, due to the coincidence of large sediment nutrient release events with high cyanobacterial biomass. This finding indicates that only a significant and prolonged reduction in external loads, which in turn reduces internal loads, will ultimately reduce cyanobacterial biomass in Lake Rotorua.

year: 2008

title: Developing an integrated ocean observing system for New Zealand

authors: O'Callaghan, J

subjects: Science & Technology

abstract: New Zealand (NZ) is an island nation with stewardship of an ocean twenty times larger than its land area. While the challenges facing NZ's ocean are similar to other maritime countries, no coherent national plan exists that meets the needs of scientists, stakeholders or kaitiakitanga (guardianship) of NZ's ocean in a changing climate. The NZ marine science community used the OceanObs'19 white paper to establish a framework and implementation plan for a collaborative NZ ocean observing system (NZ-OOS). Co-production of ocean knowledge with Māori will be embedded in this national strategy for growing a sustainable, blue economy for NZ. The strengths of an observing system for a relatively small nation come from direct connections between the science impetus through to users and stakeholders of an NZ-OOS. The community will leverage off existing ocean observations to optimize effort and resources in a system that has historically made limited investment in ocean observing. The goal of the community paper will be achieved by bringing together oceanographers, data scientists and marine stakeholders to develop an NZ-OOS that provides best knowledge and tools to the sectors of society that use or are influenced by the ocean.

text: Developing an integrated ocean observing system for New Zealand New Zealand (NZ) is an island nation with stewardship of an ocean twenty times larger than its land area. While the challenges facing NZ's ocean are similar to other maritime countries, no coherent national plan exists that meets the needs of scientists, stakeholders or kaitiakitanga (guardianship) of NZ's ocean in a changing climate. The NZ marine science community used the OceanObs'19 white paper to establish a framework and implementation plan for a collaborative NZ ocean observing system (NZ-OOS). Co-production of ocean knowledge with Māori will be embedded in this national strategy for growing a sustainable, blue economy for NZ. The strengths of an observing system for a relatively small nation come from direct connections between the science impetus through to users and stakeholders of an NZ-OOS. The community will leverage off existing ocean observations to optimize effort and resources in a system that has historically made limited investment in ocean observing. The goal of the community paper will be achieved by bringing together oceanographers, data scientists and marine stakeholders to develop an NZ-OOS that provides best knowledge and tools to the sectors of society that use or are influenced by the ocean.

year: 2019-03-26