Comparison of visible-near infrared and mid-infrared spectroscopy for classification of Huanglongbing and citrus canker infected leaves

In this study, visible-near infrared spectroscopy and mid-infrared spectroscopy were compared to evaluate their applicability in classifying citrus leaves infected with canker and HLB from healthy citrus leaves.  The visible-near infrared spectra in the range 350-2,500 nm and mid-infrared spectra in the range of 5.15-10.72 µm were collected from healthy and diseased (canker, HLB) leaves.  Following the spectral data collection, the data were preprocessed and classification was performed using two classifiers, quadratic discriminant analysis (QDA) and k-nearest neighbor (kNN).  The classifiers (QDA, kNN) resulted in an average overall and individual class classification accuracy of about 90% or more.  Mid-infrared spectroscopy provided high classification accuracy especially in identifying HLB-infected leaves; while, visible-near infrared spectroscopy was better suited for canker detection.  Both methods have their own merits such as visible-near infrared spectroscopy offers non-invasive disease detection; while mid-infrared spectroscopy represents the chemical profile of the leaf structure, which may allow potential detection in asymptomatic stages.   Keywords: disease detection, classification, quadratic discriminant analysis, k-nearest neighbo

"When the going gets tough, the tough get going" : motivation towards closure and effort investment in the performance of cognitive tasks

Previous studies have demonstrated that the need for closure (NFC), which refers to an individual's aversion toward uncertainty and the desire to quickly reduce it, leads to reluctance to invest effort in judgments and decision making. However, we argue that NFC may lead to either an increase or a decrease in effort depending on the availability of easy vs. difficult means to achieve closure and perceived importance of the task goal. We found that when closure could be achieved via both less and more demanding means, NFC was associated with decreased effort unless the task was perceived as important (Study 1). However, when attaining closure was possible via demanding means only, NFC was associated with increased effort, regardless of the task importance (Study 2). Moreover, NFC was related to choosing a more instrumental strategy for the goal of closure, even if this strategy required effort (Study 3). The results are discussed in the light of cognitive energetics theory

The role of Indian caste identity and caste inconsistent norms on status representation

The Indian caste system is a complex social structure wherein social roles like one’s profession became "hereditary," resulting in restricted social mobility and fixed status hierarchies. Furthermore, we argue that the inherent property of caste heightens group identification with one’s caste. Highly identified group members would protect the identity of the group in situations when group norms are violated. In this paper, we were interested in examining the consequence of caste norm violation and how an individual’s status is mentally represented. High caste norms are associated with moral values while the lower caste norms are associated with immorality. We predicted a ‘black sheep effect,’ that is, when high caste individuals’ group identity (caste norm violation condition) is threatened their salient high caste identity would increase, thereby resulting in devaluing the status of their fellow in-group member if the latter is perceived as perpetrator. We presented participants with a social conflict situation of a victim and a perpetrator that is ‘Caste norm consistent’ (Lower caste individual as a perpetrator and higher caste individual as a victim) and vice versa ‘Caste norm inconsistent’ condition (higher caste individual as perpetrator and lower caste individual as a victim). Then, participants had to choose from nine pictorial depictions representing the protagonists in the story on a vertical line, with varying degrees of status distance. Results showed evidence for the black sheep effect and, furthermore, revealed that no other identity (religious, national, and regional) resulted in devaluing the status of fellow in-group member. These results help us understand the ‘black sheep’ effect in the context of moral norms and status representation and are discussed in the framework of the Indian society

Does threat trigger prosociality? The relation between basic individual values, threat appraisals, and prosocial helping intentions during the COVID-19 pandemic

Prosociality is often considered as quintessential in coping with the threats of health emergencies. As previous research has suggested, prosocial behaviors are shaped by both dispositional factors and situational cues about the helping situation. In the present research, we investigated whether “bonding” types of prosociality, helping directed towards close others within one’s social network, and “bridging” types of prosociality, helping directed towards vulnerable people across group boundaries, are predicted by basic individual values and threat appraisals concerning COVID-19. During the pandemic, we conducted a cross-sectional study in the US and India (Ntotal = 954), using the Schwartz value inventory and a multifaceted measure of threat assessment to predict prosocial helping intentions. After controlling for other value and threat facets, self-transcendence values and threat for vulnerable groups uniquely predicted both bonding and bridging types of prosociality. Furthermore, threat for vulnerable groups partially mediated the effect of self-transcendence on prosocial helping intentions: People who endorsed self-transcendent values were particularly concerned by the effect of the pandemic on vulnerable groups, and thus willing to engage in prosocial behaviours to help those in need. Our findings support the idea that prosociality is stimulated by empathic concerns towards others in need and underline the importance for future research to consider the broad spectrum of threats appraised by people during health emergencies

Early detection of basal stem rot disease (Ganoderma) in oil palms based on hyperspectral reflectance data using pattern recognition algorithms

Basal stem rot (BSR) is a fatal fungal (Ganoderma) disease of oil palm plantations and has a significant impact on the production of palm oil in Malaysia. Because there is no effective treatment to control this disease, early detection of BSR is vital for sustainable disease management. The limitations of visual detection have led to an interest in the development of spectroscopically based detection techniques for rapid diagnosis of this disease. The aim of this work was to develop a procedure for early and accurate detection and differentiation of Ganoderma disease with different severities, based on spectral analysis and statistical models. Reflectance spectroscopy analysis ranging from the visible to near infrared region (325–1075 nm) was applied to analyse oil palm leaf samples of 47 healthy (G0), 55 slightly damaged (G1), 48 moderately damaged (G2), and 40 heavily damaged (G3) trees in order to detect and quantify Ganoderma disease at different levels of severity. Reflectance spectra were pre-processed, and principal component analysis (PCA) was performed on different pre-processed datasets including the raw dataset, first derivative, and second derivative datasets. The classification models: linear and quadratic discrimination analysis, k-nearest neighbour (kNN), and Naïve–Bayes were applied to PC scores for classifying four levels of stress in BSR-infected oil palm trees. The analysis showed that the kNN-based model predicted the disease with a high average overall classification accuracy of 97% with the second derivative dataset. Results confirmed the usefulness and efficiency of the spectrally based classification approach in rapid screening of BSR in oil palm

Advanced Imaging for Quantitative Evaluation of Aphanomyces Root Rot Resistance in Lentil

Aphanomyces root rot (ARR) is a soil-borne disease that results in severe yield losses in lentil. The development of resistant cultivars is one of the key strategies to control this pathogen. However, the evaluation of disease severity is limited to visual scores that can be subjective. This study utilized image-based phenotyping approaches to evaluate Aphanomyces euteiches resistance in lentil genotypes in greenhouse (351 genotypes from lentil single plant/LSP derived collection and 191 genotypes from recombinant inbred lines/RIL using digital Red-Green-Blue/RGB and hyperspectral imaging) and field (173 RIL genotypes using unmanned aerial system-based multispectral imaging) conditions. Moderate to strong correlations were observed between RGB, multispectral, and hyperspectral derived features extracted from lentil shoots/roots and visual scores. In general, root features extracted from RGB imaging were found to be strongly associated with disease severity. With only three root traits, elastic net regression model was able to predict disease severity across and within multiple datasets (R2 = 0.45–0.73 and RMSE = 0.66–1.00). The selected features could represent visual disease scores. Moreover, we developed twelve normalized difference spectral indices (NDSIs) that were significantly correlated with disease scores: two NDSIs for lentil shoot section – computed from wavelengths of 1170, 1160, 1270, and 1280 nm (0.12 ≤ |r| ≤ 0.24, P < 0.05) and ten NDSIs for lentil root sections – computed from wavelengths in the range of 630–670, 700–840, and 1320–1530 nm (0.10 ≤ |r| ≤ 0.50, P < 0.05). Root-derived NDSIs were more accurate in predicting disease scores with an R2 of 0.54 (RMSE = 0.86), especially when the model was trained and tested on LSP accessions, compared to R2 of 0.25 (RMSE = 1.64) when LSP and RIL genotypes were used as train and test datasets, respectively. Importantly, NDSIs – computed from wavelengths of 700, 710, 730, and 790 nm – had strong positive correlations with disease scores (0.35 ≤r ≤ 0.50, P < 0.0001), which was confirmed in field phenotyping with similar correlations using vegetation index with red edge wavelength (normalized difference red edge, 0.36 ≤ |r| ≤ 0.57, P < 0.0001). The adopted image-based phenotyping approaches can help plant breeders to objectively quantify ARR resistance and reduce the subjectivity in selecting potential genotypes

The Impact of COVID-19 on the Majority Population, Ethno-Racial Minorities, and Immigrants : A Systematic Literature Review on Threat Appraisals From an Inter-Group Perspective

The COVID-19 pandemic constitutes an unprecedented threat for individuals and societies, revealing stark inequalities in preparedness, exposure, and consequences. The present systematic literature review complements extant knowledge on disasters and pandemic diseases with programmatic research on the COVID-19 pandemic. Building upon an integrative definition of threat, we merge intra-personal threat regulation with group dynamics and inter-group relations. Via streamlined methods of knowledge synthesis, we first map out a broad taxonomy of threats, as appraised by the majority population and ethno-racial and immigrant minorities. Second, we delve into research linking threat appraisals with either conflict or prosociality within and across group boundaries. To conclude, we propose some guidelines for researchers to involve ethno-racial and immigrant minorities actively and for societies to cope cohesively with the impact of COVID-19