title: Furthering deep learning in near-infrared spectroscopy for fruit quality assessment

authors: Wohlers, Mark W.

abstract: Near-infrared (NIR) spectroscopy is widely used to assess fruit quality in the horticulture industry. It enables non-destructive estimation of key fruit quality measures from spectra, including dry matter content (associated with taste) and soluble solids content (associated with ripeness). Traditionally, partial least squares regression (PLSR) has been the dominant modelling method. However, more recently, deep learning (DL) has shown promise due to its ability to learn features automatically and model non-linear patterns. However, there are several challenges DL faces when applied to NIR. Labelled datasets are complicated, expensive, and time-consuming to obtain at the size required to fit these models. Deciding on the appropriate architecture and hyperparameters can also be challenging when validation data is sparse. Additionally, a problem of great practical importance in NIR spectroscopy is the difficulty of generalising across different devices of the same model or under different conditions, such as temperature. 

This thesis addresses these challenges through three complementary methods. The first uses a data augmentation technique that samples from a multivariate normal distribution with a covariance matrix designed to simulate spectral differences observed across devices. The experiments investigate whether the augmentation improves generalisability and training with small sample sizes.

The second method is a metric based on model stability to diffeomorphic transformations relative to uncorrelated perturbations of similar magnitude. The experiment evaluates the appropriateness of this method for model selection tasks and compares its performance with standard validation methods.

The third method adapts the Barlow Twins contrastive learning method to enable semi-supervised learning in the NIR setting. The Barlow Twins loss function allows unlabelled data to compensate when labelled data is scarce. This method also improves generalisability by encouraging multiple measurements on the same fruit to be similar in the encoded latent space. 

Evaluation of these methods is conducted on two datasets: a new dataset containing 5418 kiwifruit sampled across five devices and three seasons, and a previously published dataset of 4675 mangoes measured across four seasons. 

The results show that the methods improve predictive performance, especially for small labelled datasets and calibration transfer problems. This allows for the easier application of deep learning to NIR spectroscopy by reducing the requirements for labelled data, improving model generalisability across devices, and enabling model selection under data constraints.

text: Furthering deep learning in near-infrared spectroscopy for fruit quality assessment Near-infrared (NIR) spectroscopy is widely used to assess fruit quality in the horticulture industry. It enables non-destructive estimation of key fruit quality measures from spectra, including dry matter content (associated with taste) and soluble solids content (associated with ripeness). Traditionally, partial least squares regression (PLSR) has been the dominant modelling method. However, more recently, deep learning (DL) has shown promise due to its ability to learn features automatically and model non-linear patterns. However, there are several challenges DL faces when applied to NIR. Labelled datasets are complicated, expensive, and time-consuming to obtain at the size required to fit these models. Deciding on the appropriate architecture and hyperparameters can also be challenging when validation data is sparse. Additionally, a problem of great practical importance in NIR spectroscopy is the difficulty of generalising across different devices of the same model or under different conditions, such as temperature. 

This thesis addresses these challenges through three complementary methods. The first uses a data augmentation technique that samples from a multivariate normal distribution with a covariance matrix designed to simulate spectral differences observed across devices. The experiments investigate whether the augmentation improves generalisability and training with small sample sizes.

The second method is a metric based on model stability to diffeomorphic transformations relative to uncorrelated perturbations of similar magnitude. The experiment evaluates the appropriateness of this method for model selection tasks and compares its performance with standard validation methods.

The third method adapts the Barlow Twins contrastive learning method to enable semi-supervised learning in the NIR setting. The Barlow Twins loss function allows unlabelled data to compensate when labelled data is scarce. This method also improves generalisability by encouraging multiple measurements on the same fruit to be similar in the encoded latent space. 

Evaluation of these methods is conducted on two datasets: a new dataset containing 5418 kiwifruit sampled across five devices and three seasons, and a previously published dataset of 4675 mangoes measured across four seasons. 

The results show that the methods improve predictive performance, especially for small labelled datasets and calibration transfer problems. This allows for the easier application of deep learning to NIR spectroscopy by reducing the requirements for labelled data, improving model generalisability across devices, and enabling model selection under data constraints.

year: 2026

title: Measuring eye movements while tracking accelerating and decelerating targets

authors: Luckie, Joanne Marie

subjects: Eye-Tracking

abstract: Humans have an anisotropic perception of motion in depth.  An object moving towards the eye is perceived correctly, but when an object is moving away the points closest to the eye appear to be moving faster than the points further away from the eye.  This research examined if there is a difference between eye movement patterns during the two directions (forwards and away), how the eye tracks an accelerating or decelerating target, and if the anisotropic response to motion can be improved with practice.  Participants were asked to watch a movie of a target moving across a computer screen.  The target moved either left to right accelerating from a slow velocity (forwards condition), or right to left decelerating from a fast velocity (backwards condition).  Participant’s eye velocity and saccades were used to look for differences between the two conditions as well as changes over time (learning).  It was found that mean tracking velocity errors differed between the directions, and at different time slices of the videos.  Participants learned to make less tracking errors at the highest velocities during the backwards condition.  Anticipation differed between the directions, as anticipation was only seen during the backwards condition.  Participants learned to make saccades prior to the motion of the target, and the more trials they experienced the greater the learning results.  Overall, a number of differences were identified between accelerating and decelerating movement conditions indicating that eye movements may play a role in the anisotropic perception of motion in depth effect.

text: Measuring eye movements while tracking accelerating and decelerating targets Humans have an anisotropic perception of motion in depth.  An object moving towards the eye is perceived correctly, but when an object is moving away the points closest to the eye appear to be moving faster than the points further away from the eye.  This research examined if there is a difference between eye movement patterns during the two directions (forwards and away), how the eye tracks an accelerating or decelerating target, and if the anisotropic response to motion can be improved with practice.  Participants were asked to watch a movie of a target moving across a computer screen.  The target moved either left to right accelerating from a slow velocity (forwards condition), or right to left decelerating from a fast velocity (backwards condition).  Participant’s eye velocity and saccades were used to look for differences between the two conditions as well as changes over time (learning).  It was found that mean tracking velocity errors differed between the directions, and at different time slices of the videos.  Participants learned to make less tracking errors at the highest velocities during the backwards condition.  Anticipation differed between the directions, as anticipation was only seen during the backwards condition.  Participants learned to make saccades prior to the motion of the target, and the more trials they experienced the greater the learning results.  Overall, a number of differences were identified between accelerating and decelerating movement conditions indicating that eye movements may play a role in the anisotropic perception of motion in depth effect.

year: 2018

title: Te kū o te kupu

authors: Raerino, Heddell Robert

subjects: Māori language

abstract: Ko tēnei tuhingaroa, he rangahau hai wherawhera ake i te āhuatanga o te reo Māori, e kiia ana ko tōna oro, me te kū o ngā kupu kōrero o taua reo. Ko te aronga whakaaro o tēnei rangahau, he rapu i tētahi āhuatanga kāore anō kia tirotirohia, kia tuhia rānei mō te reo Māori. Ehara tēnei rangahau i te kaupapa, i te mahi hou. Otirā, kai te whai te kaituhi i ngā rangahau kua oti kē i wētahi tohunga te wānanga mō wētahi atu reo. Ko tā te kaituhi he whai i ngā tauira a te hunga wetewete reo nei, ka uwhi ai ki runga i te reo Māori hai āwhina i ngā tātaritanga ā te kaituhi mō tōna ake reo, arā, te reo Māori.

Kai te upoko tuatahi ka whaia ngā rerenga whakaaro i pūpū ake ai te taitara 'te kū o te kupu' mō tēnei mahi rangahau. Inā ka tirohia i ahu ngā oro o te reo mai i whea e ai ki tā te Māori wānanga. Mai i ngā oro ka puta ko ngā ku o ngā kupu me ngā kupu o ngā rārangi kōrero.

Hai te upoko tuarua ka tirohia ngā kōrero kua tuhia e ngā tohunga wetewete reo e pā ana ki te kaupapa o tēnei tuhingaroa, arā, te kū o te kupu. Ko ngā tirohanga mō te reo ake, arā, i ahu mai i whea, he aha ōna momo whakaata me tōna pakaritanga ka tirohia i roto i te upoko tuatoru.

Mai i ngā momo whakaata o te reo i roto i ngā momo waiata, ka āta tahuri te kaituhi ki te whakawhānui haere i tana wetewete me tāna tūhononga i ngā rerenga me ngā pekanga o te ku o te kupu.

Kai te upoko tuarima ka tirohia ngā whāinga me ngā aronga ā wētahi kaitito
waiata Māori me wētahi kaikōrero i te reo Māori, mō ō rātau māramatanga ki ngā kū me ngā oro o te reo Māori.

Kai te upoko tuaono e whāriki ana wētahi tauira kōrero Māori, hai whakaata i ngā momo orōmāhara, oro ohorere, ororite, kupu whakahuarua me wērā atu tauira whakaahua i ngā kū o te kupu me ngā oro o te reo Māori.

Ko ngā hua o tēnei rangahau, o tēnei tuhingaroa ka kitea i roto i te upoko
tuawhitu. Kōinei te whakakopinga o tēnei mahi.

text: Te kū o te kupu Ko tēnei tuhingaroa, he rangahau hai wherawhera ake i te āhuatanga o te reo Māori, e kiia ana ko tōna oro, me te kū o ngā kupu kōrero o taua reo. Ko te aronga whakaaro o tēnei rangahau, he rapu i tētahi āhuatanga kāore anō kia tirotirohia, kia tuhia rānei mō te reo Māori. Ehara tēnei rangahau i te kaupapa, i te mahi hou. Otirā, kai te whai te kaituhi i ngā rangahau kua oti kē i wētahi tohunga te wānanga mō wētahi atu reo. Ko tā te kaituhi he whai i ngā tauira a te hunga wetewete reo nei, ka uwhi ai ki runga i te reo Māori hai āwhina i ngā tātaritanga ā te kaituhi mō tōna ake reo, arā, te reo Māori.

Kai te upoko tuatahi ka whaia ngā rerenga whakaaro i pūpū ake ai te taitara 'te kū o te kupu' mō tēnei mahi rangahau. Inā ka tirohia i ahu ngā oro o te reo mai i whea e ai ki tā te Māori wānanga. Mai i ngā oro ka puta ko ngā ku o ngā kupu me ngā kupu o ngā rārangi kōrero.

Hai te upoko tuarua ka tirohia ngā kōrero kua tuhia e ngā tohunga wetewete reo e pā ana ki te kaupapa o tēnei tuhingaroa, arā, te kū o te kupu. Ko ngā tirohanga mō te reo ake, arā, i ahu mai i whea, he aha ōna momo whakaata me tōna pakaritanga ka tirohia i roto i te upoko tuatoru.

Mai i ngā momo whakaata o te reo i roto i ngā momo waiata, ka āta tahuri te kaituhi ki te whakawhānui haere i tana wetewete me tāna tūhononga i ngā rerenga me ngā pekanga o te ku o te kupu.

Kai te upoko tuarima ka tirohia ngā whāinga me ngā aronga ā wētahi kaitito
waiata Māori me wētahi kaikōrero i te reo Māori, mō ō rātau māramatanga ki ngā kū me ngā oro o te reo Māori.

Kai te upoko tuaono e whāriki ana wētahi tauira kōrero Māori, hai whakaata i ngā momo orōmāhara, oro ohorere, ororite, kupu whakahuarua me wērā atu tauira whakaahua i ngā kū o te kupu me ngā oro o te reo Māori.

Ko ngā hua o tēnei rangahau, o tēnei tuhingaroa ka kitea i roto i te upoko
tuawhitu. Kōinei te whakakopinga o tēnei mahi.

year: 2000