Exploring the Potential of Feature Selection Methods in the Classification of Urban Trees Using Field Spectroscopy Data

Mapping of vegetation at the species level using hyperspectral satellite data can be effective and accurate because of its high spectral and spatial resolutions that can detect detailed information of a target object. Its wide application, however, not only is restricted by its high cost and large data storage requirements, but its processing is also complicated by challenges of what is known as the Hughes effect. The Hughes effect is where classification accuracy decreases once the number of features or wavelengths passes a certain limit. This study aimed to explore the potential of feature selection methods in the classification of urban trees using field hyperspectral data. We identified the best feature selection method of key wavelengths that respond to the target urban tree species for effective and accurate classification. The study compared the effectiveness of Principal Component Analysis Discriminant Analysis (PCA-DA), Partial Least Squares Discriminant Analysis (PLS-DA) and Guided Regularized Random Forest (GRRF) in feature selection of the key wavelengths for classification of urban trees. The classification performance of Random Forest (RF) and Support Vector Machines (SVM) algorithms were also compared to determine the importance of the key wavelengths selected for the detection of the target urban trees. The feature selection methods managed to reduce the high dimensionality of the hyperspectral data. Both the PCA-DA and PLS-DA selected 10 wavelengths and the GRRF algorithm selected 13 wavelengths from the entire dataset (n = 1523). Most of the key wavelengths were from the short-wave infrared region (1300-2500 nm). SVM outperformed RF in classifying the key wavelengths selected by the feature selection methods. The SVM classifier produced overall accuracy values of 95.3%, 93.3% and 86% using the GRRF, PLS-DA and PCA-DA techniques, respectively, whereas those for the RF classifier were 88.7%, 72% and 56.8%, respectively

Identification of species and geographical strains of Sitophilus oryzae and Sitophilus zeamaisusing the visible/near-infrared hyperspectral imaging technique

BACKGROUND Identifying stored-product insects is essential for granary management. Automated, computer-based classification methods are rapidly developing in many areas. A hyperspectral imaging technique could potentially be developed to identify stored-product insect species and geographical strains. This study tested and adapted the technique using four geographical strains of each of two insect species, the rice weevil and maize weevil, to collect and analyse the resultant hyperspectral data. RESULTS Three characteristic images that corresponded to the dominant wavelengths, 505, 659 and 955 nm, were selected by multivariate image analysis. Each image was processed, and 22 morphological and textural features from regions of interest were extracted as the inputs for an identification model. We found the backpropagation neural network model to be the superior method for distinguishing between the insect species and geographical strains. The overall recognition rates of the classification model for insect species were 100 and 98.13% for the calibration and prediction sets respectively, while the rates of the model for geographical strains were 94.17 and 86.88% respectively. CONCLUSION This study has demonstrated that hyperspectral imaging, together with the appropriate recognition method, could provide a potential instrument for identifying insects and could become a useful tool for identification of Sitophilus oryzae and Sitophilus zeamais to aid in the management of stored-product insects

Fruit ripeness classification: A survey

Fruit is a key crop in worldwide agriculture feeding millions of people. The standard supply chain of fruit products involves quality checks to guarantee freshness, taste, and, most of all, safety. An important factor that determines fruit quality is its stage of ripening. This is usually manually classified by field experts, making it a labor-intensive and error-prone process. Thus, there is an arising need for automation in fruit ripeness classification. Many automatic methods have been proposed that employ a variety of feature descriptors for the food item to be graded. Machine learning and deep learning techniques dominate the top-performing methods. Furthermore, deep learning can operate on raw data and thus relieve the users from having to compute complex engineered features, which are often crop-specific. In this survey, we review the latest methods proposed in the literature to automatize fruit ripeness classification, highlighting the most common feature descriptors they operate on

Comparison of multispectral indexes extracted from hyperspectral images for the assessment of fruit ripening

The present research is focused on the application of artificial vision to assess the ripening of red skinned soft-flesh peach (‘Richlady’). Artificial vision allows a spatially detailed determination of the ripening stage of the fruit. The considered optical indexes (Ind1 and Ind2, proposed in the present research, and Ind3 and IAD, proposed by other authors) are based on the combination of wavelengths close to the chlorophyll absorption peak at 680 nm. Ind1 corresponds approximately to the depth of the absorption peak, and Ind2 corresponds to the relative absorption peak. An artificial image of each index was obtained by computing the corresponding reflectance images, which were acquired with a hyperspectral camera. All indexes were able to correct convexity (except for the just-harvested peaches and for Ind1). Ind2 is the preferred index; it showed the highest discriminating power between ripening stages and no influence of convexity. Ind2 also allowed the differentiation of ripening regions within the fruits, and it showed the evolution of those regions during ripening

Application of hyperspectral imaging combined with chemometrics for the non-destructive evaluation of the quality of fruit in postharvest

Tesis por compendio[ES] El objetivo de esta tesis doctoral es evaluar la técnica de imagen hiperespectral en el rango visible e infrarrojo cercano, en combinación con técnicas quimiométricas para la evaluación de la calidad de la fruta en poscosecha de manera eficaz y sostenible. Con este fin, se presentan diferentes estudios en los que se evalúa la calidad de algunas frutas que por su valor económico, estratégico o social, son de especial importancia en la Comunidad Valenciana como son el caqui 'Rojo Brillante', la granada 'Mollar de Elche', el níspero 'Algerie' o diferentes cultivares de nectarina. En primer lugar se llevó a cabo la monitorización de la calidad poscosecha de nectarinas 'Big Top' y 'Magique' usando imagen hiperespectral en reflectancia y transmitancia. Al mismo tiempo se evaluó la transmitancia para la detección de huesos abiertos. Se llevó a cabo también un estudio para distinguir los cultivares 'Big Top' y "Diamond Ray", los cuales poseen un aspecto muy similar pero sabor diferente. En cuanto al caqui 'Rojo Brillante', la imagen hiperespectral fue estudiada por una parte para monitorear su madurez, y por otra parte para evaluar la astringencia de esta fruta, que debe ser completamente eliminada antes de su comercialización. Las propiedades físico-químicas de la granada 'Mollar de Elche' fueron evaluadas usando imagen de color e hiperespectral durante su madurez usando la información de la fruta intacta y de los arilos. Finalmente, esta técnica se usó para caracterizar e identificar los defectos internos y externos del níspero 'Algerie'. En la predicción de los índices de calidad IQI y RPI usando imagen en reflectancia y transmitancia se obtuvieron valores de R2 alrededor de 0,90 y en la discriminación por firmeza, una precisión entorno al 95 % usando longitudes de onda seleccionadas. En cuanto a la detección de huesos abiertos, el uso de la imagen hiperespectral en transmitancia obtuvo un 93,5 % de clasificación correcta de frutas con hueso normal y 100 % con hueso abierto usando modelos PLS-DA y 7 longitudes de onda. Los resultados obtenidos en la clasificación de los cultivares 'Big Top' y 'Diamond Ray' mostraron una fiabilidad superior al 96,0 % mediante el uso de modelos PLS-DA y 14 longitudes de onda seleccionadas, superando a la imagen de color (56,9 %) y a un panel entrenado (54,5 %). Con respecto al caqui, los resultados obtenidos indicaron que es posible distinguir entre tres estados de madurez con una precisión del 96,0 % usando modelos QDA y se predijo su firmeza obteniendo un valor de R2 de 0,80 usando PLS-R. En cuanto a la astringencia, se llevaron a cabo dos estudios similares en los que en el primero se discriminó la fruta de acuerdo al tiempo de tratamiento con altas concentraciones de CO2 con una precisión entorno al 95,0 % usando QDA. En el segundo se discriminó la fruta de acuerdo a un valor de contenido en taninos (0,04 %) y se determinó qué área de la fruta era mejor para realizar esta discriminación. Así se obtuvo una precisión del 86,9 % usando la zona media y 23 longitudes de onda. Los resultados obtenidos para la granada indicaron que la imagen de color e hiperespectral poseen una precisión similar en la predicción de las propiedades fisicoquímicas usando PLS-R y la información de la fruta intacta. Sin embargo, cuando se usó la información de los arilos, la imagen hiperespectral fue más precisa. En cuanto a la discriminación del estado de madurez usando PLS-DA, la imagen hiperespectral ofreció mayor precisión, 95,0 %, usando la información de la fruta intacta y del 100 % usando la de los arilos. Finalmente, los resultados obtenidos para el níspero indicaron que la imagen hiperespectral junto con el método de clasificación XGBOOST pudo discriminar entre muestras con y sin defectos con una precisión del 97,5 % y entre muestras sin defectos o con defectos internos o externos con una precisión del 96,7 %. Además fue posible distinguir entre los dife[CA] L'objectiu de la present tesi doctoral se centra en avaluar la capacitat de la imatge hiperespectral en el rang visible i infraroig pròxim, en combinació amb mètodes quimiomètrics, per a l'avaluació de la qualitat de la fruita en post collita de manera eficaç i sostenible. A aquest efecte, es presenten diferents estudis en els quals s'avalua la qualitat d'algunes fruites que pel seu valor econòmic, estratègic o social, són d'especial importància a la Comunitat Valenciana com són el caqui 'Rojo Brillante', la magrana 'Mollar de Elche', el nispro 'Algerie' o diferents cultivares de nectarina. En primer lloc es va dur a terme la monitorització de la qualitat post collita de nectarines 'Big Top' i 'Magique' per mitjà d'imatge hiperespectral en reflectància i trasnmitancia. Així mateix es va avaluar la transmitància per a la detecció d'ossos oberts. Es va dur a terme també un estudi per distingir els cultivares 'Big Top' i 'Diamond Ray', els quals posseeixen un aspecte molt semblant però sabor diferent. Pel que fa al caqui 'Rojo Brillante', la imatge hiperespectral va ser estudiada d'una banda per a monitoritzar la seua maduresa, i per un altre costat per avaluar l'astringència, que ha de ser completament eliminada abans de la seua comercialització. Les propietats fisicoquímiques de la magrana 'Mollar de Elche' van ser avaluades per la imatge de color i hiperespectral durant la seua maduresa usant la informació de la fruita intacta i els arils. Finalment, aquesta tècnica es va fer servir per caracteritzar i identificar els defectes interns i externs del nispro 'Algerie'. En la predicció dels índexs de qualitat IQI i RPI usant imatge en reflectància com en trasnmitancia es van obtindre valors de R2 al voltant de 0,90 i en la discriminació per fermesa una precisió entorn del 95,0 % utilitzant longituds d'ona seleccionades. Pel que fa a la detecció d'ossos oberts, l'ús de la imatge hiperespectral en transmitància va obtindre un 93,5 % classificació correcta de fruites amb os normal i 100 % amb os obert usant models PLS-DA i 7 longituds d'ona. Els resultats obtinguts en la classificació dels cultivares 'Big Top' i 'Diamond Ray' van mostrar una fiabilitat superior al 96,0 % per mitjà de l'ús de models PLS-DA i 14 longituds d'ona, superant a la imatge de color (56,9 %) i a un panell sensorial entrenat (54,5 %). Quant al caqui, els resultats obtinguts van indicar que és possible distingir entre tres estats de maduresa amb una precisió del 96,0 % usant models QDA i es va predir la seua fermesa obtenint un valor de R2 de 0,80 usant PLS-R. Pel que fa a l'astringència, es van dur a terme dos estudis similars en què el primer es va discriminar la fruita d'acord al temps de tractament amb altes concentracions de CO2 amb una precisió al voltant del 95,0 % usant QDA. En el segon, es va discriminar la fruita d'acord a un valor de contingut en tanins (0,04 %) i es va determinar quina part de la fruita era millor per a realitzar aquesta discriminació. Així es va obtindre una precisió del 86,9 % usant la zona mitjana i 23 longituds d'ona. Els resultats obtinguts per la magrana van indicar que la imatge de color i hiperespectral posseïxen una precisió semblant a la predicció de les propietats fisicoquímiques usant PLS-R i la informació de la fruita intacta. No obstant això, quan es va usar la informació dels arils, la imatge hiperespectral va ser més precisa. Quant a la discriminació de l'estat de maduresa usant PLS-DA, la imatge hiperespectral va oferir major precisió (95,0 %) usant la informació de la fruita intacta i del 100 % usant la dels arils. Finalment, els resultats obtinguts pel nispro indiquen que la imatge hiperespectral juntament amb el mètode de classificació XGBOOST va poder discriminar entre mostres amb i sense defectes amb una precisió del 97,5 % i entre mostres sense defectes o amb defectes interns o externs amb una precisió del 96,7 %. A més, va ser possible distingir entre[EN] The objective of this doctoral thesis is to evaluate the potential of the hyperspectral imaging in the visible and near infrared range in combination with chemometrics for the assessment of the postharvest quality of fruit in a non-destructive, efficient and sustainable manner. To this end, different studies are presented in which the quality of some fruits is evaluated. Due to their economic, strategic or social value, the selected fruits are of special importance in the Valencian Community, such as Persimmon 'Rojo Brillante', the pomegranate 'Mollar de Elche', the loquat 'Algerie' or different nectarine cultivars. First, the quality monitoring of 'Big Top' and 'Magique' nectarines was carried out using reflectance and transmittance images. At the same time, transmittance was evaluated for the detection of split pit. In addition, a classification was performed to distinguish the 'Big Top' and 'Diamond Ray' cultivars, which look very similar but have different flavour. Whereas that for the 'Rojo Brillante' persimmon, the hyperspectral imaging was studied on the one hand to monitor its maturity, and on the other hand to evaluate the astringency of this fruit, which must be completely eliminated before its commercialization. The physicochemical properties of the 'Mollar de Elche' pomegranate were evaluated by means of hyperspectral and colour imaging during its maturity using the information from the intact fruit and arils. Finally, this technique was used to characterise and identify the internal and external defects of the 'Algerie' loquat. In the prediction of the IQI and RPI quality indexes using reflectance and transmittance images, R2 values around 0.90 were obtained and in the discrimination according to firmness, accuracy around 95.0 % using selected wavelengths was obtained. Regarding the split pit detection, the use of the hyperspectral image in transmittance mode obtained a 93.5 % of fruits with normal bone correctly classified and 100% with split pit using PLS-DA models and 7 wavelengths. The results obtained in the classification of 'Big Top' and 'Diamond Ray' fruits show accuracy higher than 96.0 % by using PLS-DA models and 14 selected wavelengths, higher than the obtained with colour image (56.9 %) and a trained panel (54.5 %). According to persimmon, the results obtained indicated that it is possible to distinguish between three states of maturity with an accuracy of 96.0 % using QDA models and its firmness was predicted obtaining a R2 value of 0.80 using PLS-R. Regarding astringency, two similar studies were carried out. In the first study, the fruit was classified according to the time of treatment with high concentrations of CO2 with a precision of around 95.0 % using QDA. In the second, the fruit was discriminated according to a threshold value of soluble tannins (0.04 %) and was determined what fruit area was better to perform this discrimination. Thus, an accuracy of 86.9 % was obtained using the middle area and 23 wavelengths. The results obtained for the pomegranate indicated that the use of colour and hyperspectral images have a similar precision in the prediction of physicochemical properties using PLS-R and the intact fruit information. However, when the information from the arils was used, the hyperspectral image was more accurate. Regarding the discrimination by the state of maturity using PLS-DA, the hyperspectral image offered greater precision, of 95.0 % using the information from the intact fruit and 100 % using that from the arils. Finally, the results obtained for the 'Algerie' loquat indicated that the hyperspectral image with the XGBOOST classification method could discriminate between sound samples and samples with defects with accuracy of 97.5 % and between sound samples or samples with internal or external defects with an accuracy of 96.7 %. It was also possible to distinguish between the different defects with an accuracy of 95.9 %.Munera Picazo, SM. (2019). Application of hyperspectral imaging combined with chemometrics for the non-destructive evaluation of the quality of fruit in postharvest [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/125954TESISCompendi

Hyperspectral Imaging from Ground Based Mobile Platforms and Applications in Precision Agriculture

This thesis focuses on the use of line scanning hyperspectral sensors on mobile ground based platforms and applying them to agricultural applications. First this work deals with the geometric and radiometric calibration and correction of acquired hyperspectral data. When operating at low altitudes, changing lighting conditions are common and inevitable, complicating the retrieval of a surface's reflectance, which is solely a function of its physical structure and chemical composition. Therefore, this thesis contributes the evaluation of an approach to compensate for changes in illumination and obtain reflectance that is less labour intensive than traditional empirical methods. Convenient field protocols are produced that only require a representative set of illumination and reflectance spectral samples. In addition, a method for determining a line scanning camera's rigid 6 degree of freedom (DOF) offset and uncertainty with respect to a navigation system is developed, enabling accurate georegistration and sensor fusion. The thesis then applies the data captured from the platform to two different agricultural applications. The first is a self-supervised weed detection framework that allows training of a per-pixel classifier using hyperspectral data without manual labelling. The experiments support the effectiveness of the framework, rivalling classifiers trained on hand labelled training data. Then the thesis demonstrates the mapping of mango maturity using hyperspectral data on an orchard wide scale using efficient image scanning techniques, which is a world first result. A novel classification, regression and mapping pipeline is proposed to generate per tree mango maturity averages. The results confirm that maturity prediction in mango orchards is possible in natural daylight using a hyperspectral camera, despite complex micro-illumination-climates under the canopy

Applications of visible and near infrared spectroscopy for sorting and identification of tree seeds

Seeds are the most commonly used regeneration material for reforestation purpose; hence interest in “precision sowing” among nurseries is high due to the high cost of containerized seedling production. In addition, the increased interest in growing hybrid larch in commercial forestry has raised concerns about the purity of hybrid larch seed lots, as there are large proportions of pure parental seeds mixed with hybrid larch seed lots. The aims of the studies presented in this thesis were to evaluate the application of visible (Vis) and/or near infrared (NIR) spectroscopy combined with multivariate modelling for sorting filled-viable, empty and petrified seeds of Larix sibirica Ledeb., verification of hybrid larch, Larix × eurolepis Henry seeds, identification of seeds of Betula pendula Roth and Betula pubescens Ehrh., and authentication of the origin of Picea abies (L.) Karst seed lots. For these purposes, reflectance spectra were recorded on single seeds using XDS Rapid Content Analyzer (FOSS NIRSystems, Inc.) from 400 – 2500 nm with a resolution of 0.5 nm, and multivariate classification models were developed. The results showed that filled-viable, empty and petrified seeds of L. sibirica can be sorted with 98%, 82% and 87% accuracies, respectively. When the seed lot was sorted into viable and non-viable (empty and petrified combined) classes, the predicted class membership reached 100% for both classes. The technique could separate the hybrid larch seeds from pure parental seeds with 100% accuracy. Seeds of B. pubescens and B. pendula were differentiated with 100% and 99% classification accuracy, respectively. Also, the overall classification accuracy among three B. pendula families was 93% and that of B. pubescens was 98%. NIR spectroscopy discriminated Swedish, Finnish, Norwegian, Polish and Lithuanian seed lots of P. abies with 92% - 100% accuracy. Absorption bands that were accounted for distinguishing the various seed lots examined in this thesis were attributed to differences in seed color, moisture content and chemical composition of the seeds, presumably polysaccharides, proteins and fatty acids, which are the common seed storage reserves. The findings demonstrate the feasibility of Vis + NIR spectroscopy as a robust technique for sorting seed lots according to their viability and for certification of seed lots by species and origin. Thus, concerted efforts should be made to scale-up the technique to on-line sorting system for large-scale tree seed handling operations

A Simplified, Light Emitting Diode (LED) based, modular system to be used for the rapid evaluation of fruit and vegetable quality: development and validation on dye solutions

NIR spectroscopy has proven to be one of the most efficient and ready to transfer tools to monitor product's quality. Portable VIS/NIR instruments are particularly versatile and suitable for field use to monitor the ripening process or quality parameters. The aim of this work is to develop and evaluate a new simplified optoelectronic system for potential measurements on fruit and vegetables directly in the field. The development, characterization and validation of an operative prototype is discussed. LED technology was chosen for the design, and spectral acquisition at four specific wavelengths (630, 690, 750 and 850 nm) was proposed. Nevertheless, attention was given to the modularity and versatility of the system. Indeed, the possibility to change the light sources module with other wavelengths allows one to adapt the use of the same device for different foreseeable applications and objectives, e.g., ripeness evaluation, detection of particular diseases and disorders, chemical and physical property prediction, shelf life analysis, as well as for different natures of products (berry, leaf or liquid). Validation tests on blue dye water solutions have shown the capability of the system of discriminating low levels of reflectance, with a repeatability characterized by a standard deviation proportional to the measured intensity and in general limited to 2%-4%

Implementação de modelos de espectroscopia hiperespectral e nanossatélite na identificação de cultivares de vitis vinifera e suas variações regionais

O Brasil é destaque na produção mundial de uvas e demonstra uma constante evolução ao longo de sua histórica, desde 1980, com o Estado do Rio Grande do Sul, no topo da lista de produtores. Diversas regiões produtoras de uvas e vinhos no Brasil tem organizado suas atividades no sentido de se tornarem reconhecidas como “Indicações de Procedência” (IP), dando tipicidade e caráter regional aos seus produtos. Esta caracterização requer descrições dos impactos das condições ambientais e do trabalho humano. A utilização de dados adquiridos por sensoriamento remoto, incluindo dados proximais hiperespectrais e de satélites, permitem classificar e caracterizar as variedades de uvas e suas respectivas unidades produtoras de diversas localidades, sob condições climáticas e antrópicas diferenciadas. Esta tese tem como principal objetivo desenvolver uma metodologia para aquisição de dados, treinamento de modelos de hiperespectrais por sensor proximal e imagens via nanossatélite. A área de estudo é composta por oito vinhedos comerciais localizados no Rio Grande do Sul, Brasil. Na primeira fase deste estudo, a unidade de análise foi a folha isolada da videira em diferentes regiões. Posteriormente foi realizado o levantamento dos parâmetros de clorofila, Teor de Sólidos Totais (TST) ou °Brix da uva, espectros de reflectância hiperespectral e imagens de nanossatélite em parcelas de Cabernet Sauvignon em uma vinícola da Serra Gaúcha. Os modelos Light Gradient Booster Machine (LGBM) e Random Forest (RF) obtiveram as melhores acurácias na discriminação espectral em regiões do ultravioleta (UV) e visível (VIS). As estimativas apresentaram elevados R² com o modelo de regressão RF. O índice de Gini teve maiores valores para comprimentos de onda no UV/VIS/NIR e o índice de vegetação Plant Senescence Reflectance Index (PSRI) teve melhor desempenho para predição dos parâmetros de clorofila, e o Triangular Greenness Index (TGI)/Normalized Difference Vegetation Index (NDVI) para o ºBrix da uva, utilizando como dados a reflectância hiperespectral e a reflectância de superfície. Desenvolvimentos futuros incluem o levantamento de dados com maior número de planta e variedades, auxiliando a compreender as assinaturas espectrais de cada variedade como subsídio para um melhor manejo da produção.Brazil has had an increasing prominence in the production of grapes in the world and the country's production history since the 80's demonstrates this constant evolution. At the top of the list of producers is the State of Rio Grande do Sul. Several grape and wine producing regions in Brazil have organized their activities in order to become recognized as “Indications of Origin” (IO), giving their products typicality and regional character. This characterization requires descriptions of environmental conditions and the impacts of these conditions and human work. The use of remote sensing data, including proximal hyperspectral and satellite data, allow us to classify and characterize grape varieties and their respective producing units from various locations, under different climatic and anthropic conditions. The main objective of this thesis is to develop a methodology for data acquisition, training of plant spectroscopy models with a hyperspectral proximal sensor and for nanosatellite imaging. . The study area consists of eight commercial vineyards found in Rio Grande do Sul, Brazil. In the first phase of this study, the unit of analysis was the leaf isolated from the vine in different regions. Subsequently, a survey of chlorophyll parameters, Total Solids Content (°Bx) of the grape, hyperspectral reflectance spectra and nanosatellite images were conducted in Cabernet Sauvignon plots in a Serra Gaúcha winery. Machine learning algorithms were applied in the discrimination of vineyards by region and by variety, and in the estimation of the chlorophyll and Brix parameters of the grape. The Light Gradient Booster Machine (LGBM) and Random Forest (RF) models obtained the best accuracies in spectral discrimination in the ultraviolet (UV) and visible (VIS) regions. The estimates showed high R² with the RF regression model. The Gini index had higher values for UV/VIS/NIR wavelengths, and the Plant Senescence Reflectance Index (PSRI) had better performance for predicting chlorophyll parameters, and the Triangular Greenness Index (TGI)/Normalized Difference Vegetation Index (NDVI) for the degree Brix, using as data the hyperspectral reflectance and the surface reflectance. Future developments include collecting data with a greater number of plants and varieties, helping to understand the spectral signatures of each variety as a subsidy for better production management