Agriculture-related industry and academia have widely adopted Hyperspectral Imaging (HSI) based in-field phenotyping activities. Current HSI solutions such as airborne remote sensing platforms and handheld spectrometers have been proven effective and have become popular in various phenotyping applications. However, the quality of remote sensing systems suffers from a low signal-over-noise ratio due to the imaging distance and low resolution. Handheld leaf spectrometers are slow, labor-intensive, and only measure a small spot on the leaf, which fails to represent the canopy variation. In 2018, the Purdue ABE sensor lab developed a new handheld hyperspectral leaf imager, LeafSpec. For the first time, field phenotyping researchers were able to collect high-resolution leaf hyperspectral images without the negative impacts of ambient lighting and leaf-slope angle changes. LeafSpec has been successfully tested in field assays and showed its advantageous phenotyping quality. The goal of this study was to test the hypothesis that a robotic system could replace the human operator required to perform in-field and leaf-level HSI using LeafSpec. The system consisted of a modified version of the LeafSpec device, a machine vision system for target leaf detection, a National Instrument MyRIO as a controller and a customized cartesian robotic arm with five Degrees of Freedom (DOF). For each scan, the on-board machine vision system recognized the top leaf collar and obtained the target coordinates. The coordinates were then passed to the controller, which calculated the appropriate path and acceleration profile and drove the arm to approach the target leaf and scan the leaf with the LeafSpec device. The scanned image was then processed in real-time to calculate plant physiological features such as chlorophyll content, nitrogen content and so on. In the 2019 field test, the designed system collected data from 41 corn plants with two genotypes and three levels of nitrogen treatments with an average cycle time of 86 seconds. The nitrogen content predicted by the designed system had an R squared of 0.72 with the ground truth. The developers, therefore, concluded that the robotic gantry system was capable of replacing human operators for LeafSpec hyperspectral corn leaf imaging in the field with high quality.

Aujourd’hui, les difficultés pour établir des liens entre caractéristiques des nanomatériaux et réponses biologiques sont principalement issues du manque de contrôle de la synthèse des nanomatériaux, ne permettant pas de faire varier leurs paramètres physico-chimiques clés une à une.Pour identifier certains mécanismes gouvernant la toxicité des nanomatériaux nous avons utilisé un nanotube inorganique modèle dont la synthèse est bien contrôlée : les Ge-imogolites. Les effets de la longueur, du nombre de parois, de la cristallinité et de la composition chimique des Ge-imogolites ont été étudiés sur une bactérie des sols: Pseudomonas brassicacearum. Il a été identifié que la présence de sites réactifs (en bordure de tubes) induit une toxicité due à une interaction forte des nanotubes avec les cellules bactériennes, ainsi que la génération d’espèces réactives de l’oxygène. Ajouter des sites réactifs via la présence de défauts structuraux augmente la dégradation des tubes ainsi que la rétention d’éléments nutritifs essentiels, ce qui augmente leur toxicité. Enfin, l’ajout de fer dans leur structure transforme les Ge-imogolites en source de fer, qui sont dégradées et deviennent promoteurs de croissance. Dans tous ces cas, les interactions entre nanomatériaux et cellules ont été identifiées comme cruciales pour comprendre et prévenir les effets des nanomatériaux. Ce travail de thèse a également permis de mettre en avant la capacité de nouveaux outils pour le suivi de l’internalisation de nanomatériaux dans les organismes. / Only a few studies of (eco)toxicology linked the physico-chemical properties of nanoparticles to the toxicity mechanisms or the stress they induce. Moreover, no clear conclusions can be drawn at present because of the variability of nanoparticles used in studies. The present study used the inorganic Ge-imogolite nanotubes as a model compound. The toxic effects of length, number of walls, structural defects, and chemical composition were assessed towards the soil bacteria Pseudomonas brassicacearum. Several mechanisms modulating the toxicity of Ge-imogolite were then identified. Indeed, reactive sites at the tube ends induce a slight toxicity via a strong cell interaction and the generation of reactive oxygen species. Creating vacant sites on the surface of Ge-imogolite (ant thus increasing the number of reactive sites), appears to cause a deficiency of nutrients in the culture media correlated with a higher degradation of the tubes, leading to a high bacterial growth decrease. Finally, structural iron incorporation into Ge-imogolite transforms them into an iron source, being degraded and becoming growth promoters. In this work, the new tools capacities for the study of nanomaterials/cells interaction have been studied.

<p>Cadmium (Cd) is a highly mobile and toxic heavy metal that negatively affects plants, soil biota, animals and humans, even in very low concentrations. Currently, Cd contamination of cocoa produced in Latin American countries is a significant problem, as concentrations can exceed acceptable levels set by the European Union (0.5 mg/kg), sometimes by more than 10 times allowable levels. In South America, <i>Theobroma cacao</i> is an essential component of the basic market basket. This crop contributes to the Latin-American trade balance, as these countries export cacao and chocolate-based products to major consumer countries such as the United States and Europe. Some soil amendments can alter the bioavailability and uptake of Cd into edible plant tissues, though cacao plants can accumulate Cd without displaying any visible symptoms of phytotoxicity, which makes it difficult to determine if potential remediation strategies are successful. Currently, the only effective way to quantify Cd accumulation in plant tissues is via destructive post-harvest practices that are time-consuming and expensive. New hyperspectral imaging (HSI) technologies developed for use in high-throughput plant phenotyping are powerful tools for monitoring environmental stress and predicting the nutritional status in plants. Consequently, the experiments described in this thesis were conducted to determine if HSI technologies could be adapted for monitoring plant stress caused by Cd, and estimating its concentration in vegetative plant tissues. Two leafy green crops were used in these experiments, basil (<i>Ocimum basilicum L.</i> var. Genovese) and kale (<i>Brassica oleracea L</i>. var. Lacinato), because they are fast growing, and therefore, could serve as indicator crops on cacao farms. In addition, we expected these two leafy green crops would differ in their morphological responses to Cd stress. Specifically, we predicted that stress responses would be visible in basil, but not kale, which is known to be a hyperaccumulator. The plants were subject to four levels of soil Cd (0, 5, 10 and 15 ppm), and half of the pots were amended with biochar at a rate of 3% (v/v), as this amendment has previously been demonstrated to improve plant health and reduce Cd uptake. The experiments were conducted at Purdue’s new Controlled Environment Phenotyping Center (CEPF). The plants were imaged weekly and manual measurements of plant growth and development were collected at the same times, and concentrations of Cd as well as many other elements were determined after harvest. Fourteen vegetation indices generated using HSI images collected from the side and top view of plants were evaluated for their potential to identify subtle signs of plant stress due soil Cd and the biochar amendment. In addition, three mathematical models were evaluated for their potential to estimate Cd concentrations in the plant biomass and determine if they exceed safe standards (0.28 mg/kg) set by the Food and Agriculture Organization (FAO) for leafy greens. Results of these studies confirm that like many plants, these leafy green crops can accumulate Cd levels that are well above safety thresholds for human health, but exhibit few visible symptoms of stress. The normalized difference vegetation index (NDVI) and the chlorophyll index at the red edge (CI_RE) were the best indices for detecting Cd stress in these crops, and the plant senescence and reflectance index (PSRI) and anthocyanin reflectance index (ARI) were the best at detecting subtle changes in plant physiology due to the biochar amendment. The heavy metal stress index (HMSSI), developed exclusively for detecting heavy metal stress, was only able to detect Cd stress in basil when images were taken from the top view. Results of the mathematical models indicated that principal components analysis (PCA) and partial least squares (PLS) models overfit despite efforts to transform the data, indicating that they are not capable of predicting Cd concentrations in these crops at these levels. However, the artificial neural networks (ANN) was able to predict whether leafy greens had levels of Cd that were above or below critical thresholds suggested by the FAO, indicating that HSI could be further developed to predict Cd concentrations in plant tissues. Further research conducted in the field and in the presence of other environmental stress factors are needed to confirm the utility of these tools, and determine whether they can be adapted to monitor Cd uptake in cacao plants.</p>

<p>  </p> <p>Hyperspectral Imaging is one of the most popular technologies in plant phenotyping due to its ability to predict the plant physiological features such as yield biomass, leaf moisture, and nitrogen content accurately, non-destructively, and efficiently. Various kinds of hyperspectral imaging systems have been developed in the past years for both greenhouse and field phenotyping activities. Developing the plant physiological prediction model such as relative water content (RWC) using hyperspectral imaging data requires the adoption of machine learning-based calibration techniques. Convolutional neural networks (CNNs) have been known to automatically extract the features from the raw data which can lead to highly accurate physiological prediction models. Once a reliable prediction model has been developed, sharing that model across multiple hyperspectral imaging systems is very desirable since collecting the large number of ground truth labels for predictive model development is expensive and tedious. However, there are always significant differences in imaging sensors, imaging, and environmental conditions between different hyperspectral imaging facilities, which makes it difficult to share plant features prediction models. Calibration transfer between the imaging systems is critically important. In this thesis, two approaches were taken to address the calibration transfer from the greenhouse to the field. First, direct standardization (DS), piecewise direct standardization (PDS), double window piecewise direct standardization (DPDS) and spectral space transfer (SST) were used for standardizing the spectral reflectance to minimize the artifacts and spectral differences between different greenhouse imaging systems. A linear transformation matrix estimated using SST based on a small set of plant samples imaged in two facilities reduced the root mean square error (RMSE) for maize physiological feature prediction significantly, i.e., from 10.64% to 2.42% for RWC and from 1.84% to 0.11% for nitrogen content. Second, common latent space features between two greenhouses or a greenhouse and field imaging system were extracted in an unsupervised fashion. Two different models based on deep adversarial domain adaptation are trained, evaluated, and tested. In contrast to linear standardization approaches developed using the same plant samples imaged in two greenhouse facilities, the domain adaptation extracted non-linear features common between spectra of different imaging systems. Results showed that transferred RWC models reduced the RMSE by up to 45.9% for the greenhouse calibration transfer and 12.4% for a greenhouse to field transfer. The plot scale evaluation of the transferred RWC model showed no significant difference between the measurements and predictions. The methods developed and reported in this study can be used to recover the performance plummeted due to the spectral differences caused by the new phenotyping system and to share the knowledge among plant phenotyping researchers and scientists.</p>

Optics are a powerful probe of chemical structure that can often be linked to theoretical predictions, providing robustness as a measurement tool. Not only do optical interactions like second harmonic generation (SHG), single and two-photon excited fluorescence (TPEF), and infrared absorption provide chemical specificity at the molecular and macromolecular scale, but the ability to image enables mapping heterogeneous behavior across complex systems such as biological tissue. This thesis will discuss nonlinear and linear optics, leveraging theoretical predictions to provide frameworks for interpreting analytical measurement. In turn, the causal mechanistic understanding provided by these frameworks will enable structurally specific quantitative tools with a special emphasis on application in biological imaging. The thesis will begin with an introduction to 2nd order nonlinear optics and the polarization analysis thereof, covering both the Jones framework for polarization analysis and the design of experiment. Novel experimental architectures aimed at reducing 1/f noise in polarization analysis will be discussed, leveraging both rapid modulation in time through electro-optic modulators (Chapter 2), as well as fixed-optic spatial modulation approaches (Chapter 3). In addition, challenges in polarization-dependent imaging within turbid systems will be addressed with the discussion of a theoretical framework to model SHG occurring from unpolarized light (Chapter 4). The application of this framework to thick tissue imaging for analysis of collagen local structure can provide a method for characterizing changes in tissue morphology associated with some common cancers (Chapter 5). In addition to discussion of nonlinear optical phenomena, a novel mechanism for electric dipole allowed fluorescence-detected circular dichroism will be introduced (Chapter 6). Tackling challenges associated with label-free chemically specific imaging, the construction of a novel infrared hyperspectral microscope for chemical classification in complex mixtures will be presented (Chapter 7). The thesis will conclude with a discussion of the inherent disadvantages in taking the traditional paradigm of modeling and measuring chemistry separately and provide the multi-agent consensus equilibrium (MACE) framework as an alternative to the classic meet-in-the-middle approach (Chapter 8). Spanning topics from pure theoretical descriptions of light-matter interaction to full experimental work, this thesis aims to unify these two fronts. <br>