Extraction of pectin from watermelon rinds using sequential ultrasound-microwave technique: Optimization using RSM and ANN modeling and characterization.

This study aimed to optimize pectin extraction from watermelon (Citrullus lanatus) rind using sequential ultrasound-microwave assisted extraction (UMAE) with artificial neural network (ANN) and response surface methodology (RSM). The effects of pH, sonication time, microwave power, and irradiation time on pectin yield were evaluated. The ANN model showed higher precision in predicting yield compared to the RSM model.

Automated detection of selected tea leaf diseases in Bangladesh with convolutional neural network.

Globally, tea production and its quality fundamentally depend on tea leaves, which are susceptible to invasion by pathogenic organisms. Precise and early-stage identification of plant foliage diseases is a key element in preventing and controlling the spreading of diseases that hinder yield and quality. Image processing techniques are a sophisticated tool that is rapidly gaining traction in the agricultural sector for the detection of a wide range of diseases with excellent accuracy.