Parameter optimization for selective harvesting in cauliflower (Brassica oleracea) using response surface methodology
391 / 376
Keywords:
Chain-saw cutting mechanism, Operating parameters, Response surface methodology, Selective cauliflower harvestingAbstract
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.
Downloads
References
Anonymous. 2018. Scientists develop harvesting robots that could revolutionize farming practices. University of Plymouth, Plymouth, United Kingdom.
Birrell S, Hughes J, Cai J Y and Iida F. 2020. A field-tested robotic harvesting system for iceberg lettuce. Journal of Field Robotics 37(2): 225–45. DOI: https://doi.org/10.1002/rob.21888
Blok P M, Barth R and Van Den Berg W. 2016. Machine vision for a selective broccoli harvesting robot. International Federation of Automatic Control-Papers On Line 49(16): 66–71. DOI: https://doi.org/10.1016/j.ifacol.2016.10.013
Dixit J and Rawat N J. 2022. Development and evaluation of self-propelled cabbage/cauliflower harvester. NASS Journal of Agricultural Sciences 4(1). DOI: https://doi.org/10.36956/njas.v4i1.471
El Didamony M I and El Shal A M. 2020. Fabrication and evaluation of a cabbage harvester prototype. Agriculture 10(12): 631. DOI: https://doi.org/10.3390/agriculture10120631
FAO. 2020. The State of Food and Agriculture. Rome, Italy.
Jabbar S, Abid M, Wu T, Hashim M M, Saeeduddin M, Hu B, Lei S and Zeng X. 2015. Ultrasound-assisted extraction of bioactive compounds and antioxidants from carrot pomace: A response surface approach. Journal of Food Processing and Preservation 39(6): 1878–888. DOI: https://doi.org/10.1111/jfpp.12425
Kanamitsu M and Yamamoto K. 1996. Development of Chinese cabbage [Brassica chinensis] harvester. Japan Agricultural Research Quarterly 30(1): 35–41.
Malenga E N, Mulaba-Bafubiandi A F and Nheta W. 2022. Application of the response surface method (RSM) based on central composite design (CCD) and design space (DS) to optimize the flotation and the desliming conditions in the recovery of PGMs from mine sludge. Separation Science and Technology 57(18): 2960–983. DOI: https://doi.org/10.1080/01496395.2022.2092514
Mehmood T, Ahmed A, Ahmad A, Ahmad M S and Sandhu M A. 2018. Optimization of mixed surfactants-based β-carotene nano-emulsions using response surface methodology: An ultrasonic homogenization approach. Food Chemistry 253: 179–84. DOI: https://doi.org/10.1016/j.foodchem.2018.01.136
MoA and FW. 2022. Cauliflower area, production in India. Ministry of Agriculture and Farmers Welfare, India.
Ramirez R A. 2006. 'Computer vision based analysis of broccoli for application in a selective autonomous harvester'. PhD Thesis, Virginia Tech, Virginia, US.
Sarkar P and Raheman H. 2021. A comprehensive review of mechanized cabbage harvesting systems and its present status in India. Journal of The Institution of Engineers (India): Series A 102(3): 861–69. DOI: https://doi.org/10.1007/s40030-021-00557-6
Singh N, Tewari V K, Biswas P K, Pareek C M and Dhruw L K. 2021. Image processing algorithms for in-field cotton boll detection in natural lighting conditions. Artificial Intelligence in Agriculture 5: 142–56. DOI: https://doi.org/10.1016/j.aiia.2021.07.002
Tewari V K, Pareek C M, Lal G, Dhruw L K and Singh N. 2020. Image processing based real-time variable-rate chemical spraying system for disease control in paddy crop. Artificial Intelligence in Agriculture 4: 21–30. DOI: https://doi.org/10.1016/j.aiia.2020.01.002
Weicai Q, Xinyu X, Longfei C, Qingqing Z, Zhufeng X and Feilong C. 2016. Optimization and test for spraying parameters of cotton defoliant sprayer. International Journal of Agricultural and Biological Engineering 9(4): 63–72.
Downloads
Submitted
Published
Issue
Section
License
Copyright (c) 2023 The Indian Journal of Agricultural Sciences

This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
The copyright of the articles published in The Indian Journal of Agricultural Sciences is vested with the Indian Council of Agricultural Research, which reserves the right to enter into any agreement with any organization in India or abroad, for reprography, photocopying, storage and dissemination of information. The Council has no objection to using the material, provided the information is not being utilized for commercial purposes and wherever the information is being used, proper credit is given to ICAR.