Parameter optimization for selective harvesting in cauliflower (Brassica oleracea) using response surface methodology

Abstract

Manual harvesting of cauliflower (Brassica oleracea var. botrytis) is time-consuming, costly, and labour-intensive, necessitating the development of mechanized solutions. This research focuses on optimizing the operating parameters, namely the shaft angular speed and forward speed, for developing the intelligent selective harvesting prototype of cauliflower to enhance harvest success, reduce damage, and minimize cycle time. A laboratory setup was established at ICAR-Indian Agricultural Research Institute, New Delhi during 2022–23, which consisted of a prototype harvesting unit, prototype pulling unit, and plant holding unit. The physical properties of two cauliflower varieties, Pusa Meghna, and Pusa Sharad, were measured. An experimental plan was designed to optimize the operating parameters using response surface methodology (RSM) to enhance performance. The optimized forward speed and shaft angular speed were found to be 0.62 km/h, and 0.36 rad/s, respectively. Regression models were developed to predict all responses for varieties and all prediction errors were found to be less than ±10%, indicating the reliability of the developed models. The study aimed to help in the development of an intelligent cauliflower harvester suitable for small-scale growers in India.