The present research aims to investigate the extent to which sensory appeal, nutritional quality, safety, and health determinants influence purchase intention, consumption, and satisfaction of consumers towards convenience food. The non-probability purposive sampling approach was adopted for the recruitment of consumers. A pre-tested questionnaire was used to collect data from 501 consumers. Descriptive statistics, confirmatory factor analysis, and structural equation modeling were adopted to analyze the data. Factor loading, Cronbach’s alpha, composite reliability, average variance extracted, and correlations estimate of constructs revealed good internal consistency and reliability of scale items as well as convergent and discriminant validity of the constructs. The path analysis of structural model demonstrated positive relationship between sensory appeal, nutritional quality, safety attributes, healthiness, and purchase intention of convenience food. Further, the path analysis of structural model revealed that purchase intention with consumption as well as consumption with satisfaction were positively associated for convenience food. Sensory appeal was the key determinant influencing purchase intention, consumption, and satisfaction of consumers towards convenience food. The good taste, pleasant appearance, nice smell, and appealing texture within sensory appeal were the most important factors influencing purchase intention, consumption, and satisfaction of consumers towards convenience food. Further, the consumers in emerging economies such as India focus more on sensory appeal in convenience food choice.

The brown planthopper Nilaparvata lugens (BPH) is one of the most harmful insect pests in rice paddy fields, which causes considerable yield loss and consequent economic problems, particularly in the central plain of Thailand. Accurate and timely forecasting of pest population incidence would support farmers in planning effective mitigation. In this study, artificial neural network (ANN), random forest (RF) and classic linear multiple regression (MLR) analyses were applied and compared to forecast the BPH population using weather and host-plant phenology factors during the crop dry season from 2006 to 2016 in the central plain of Thailand. Data from satellite earth observation was used to monitor crop phenology factors affecting BPH population density. An ANN model with integrated ground-based meteorological variables and satellite-derived host plant variables was more accurate for short-term forecasting of the peak abundance of BPH when compared with RF and MLR, according to a reasonably validating dataset (RMSE of natural log-transformed (ln) BPH light trap catches = 1.686, 1.737, and 2.015, respectively). This finding indicates that the utilization of ground meteorological observations, satellite-derived NDVI time series, and ANN have the potential to predict BPH population density in support of integrated pest management programs. We expect the results from this study can be applied in conjunction with the satellite-based rice monitoring system developed by the Geo-Informatic and Space Technology Development Agency of Thailand (GISTDA) to support an effective pest early warning system.