The yield of the crop is a complex function of a number of dependent traits, which makes yield prediction a statistically difficult task. A number of work on yield prediction using morphological characters already exists in the literature. Most of the work used statistical techniques such as linear regression and crop yield models, which assume a linear relationship between yield and the morphological traits; in actual practice, such a linear relationship is seldom achieved. With the advancement in the field of machine learning techniques, these methods can provide a viable alternative for dealing with nonlinear relationships for yield prediction. Globally, apples are the most consumed fruit. In this paper, attempts have been made to predict the yield of the apple crop using morphological traits. PCA was used for selection of the significant variables. These variables were later used as input variables in the ANN model with different hidden layers for predicting crop yield. The predictive performance of the model was evaluated using standard statistical tests. Sensitivity analysis was performed to find out the individual effects of each character on the apple yield. The study contributes to a better understanding of the complex relationships between crop yield and morphological traits.

Background: Sericulture, the practice of rearing silkworms for silk production, has been carried out in India for centuries and represents a sustainable and environmentally friendly livelihood option. Owing to the country’s rich diversity of host plants and the presence of economically important silk species across different regions, sericulture holds significant potential, particularly for poor and forest-dependent communities, by offering income diversification and reducing reliance on conventional agriculture. Methods: The study analyzed production trends of major silk varieties in India using compound growth rate models and identified the leading silk-producing states. Export and import trends were examined to locate major shifts and understand the underlying factors influencing trade dynamics. Instability in export and import time series data was assessed using the Cuddy-Della Valle Instability Index (CDVI). Additionally, a SWOT analysis was conducted to evaluate the internal strengths and weaknesses, as well as the external opportunities and threats, of the Indian sericulture sector. Result: The results indicate that sericulture exhibits favorable production trends in key silk varieties and provides a relatively stable source of income. Trade analysis revealed identifiable patterns and variability in exports and imports, while instability measures highlighted moderate fluctuations over time. The SWOT analysis further underscored the sector’s strong potential for livelihood diversification, especially for smallholders and forest dwellers, confirming sericulture as a viable and sustainable alternative to traditional agricultural livelihoods.