1. A farmer-assistant robot for nitrogen fertilizing management of greenhouse crops
June 2017 – Computers and Electronics in Agriculture 139:153-163
Authors
Keyvan Asefpour Vakilian – Jafar Massah
Abstract
Abstract
In this study, an autonomous machine vision-based system was developed for precise nitrogen fertilizing management to improve nitrogen use efficiency in greenhouse crops. Two scenarios were considered: in the first scenario, nitrogen fertilization was done based on available instructions and protocols provided by the seed producer. The second scenario was nitrogen fertilization using a machine vision-based robot which was moving between the crop rows to monitor plants’ needs and requirements. The scenarios were examined in a hydroponic greenhouse for four varieties of cucumber. The extracted plant image features, namely entropy, energy, and local homogeneity were the markers for precise timing of nitrogen fertilization in the cucumber crops. In scenario 2, nitrogen fertilizing was based on a signal transmitted from the robot to a wireless receiver when at least one of the mean values of normalized image textural features of cucumber crops had a difference more than a (a = 10, 15, and 20%) with the same feature in scenario 1. Experimental results showed that scenario 2 for a = 15% decreased the nitrogen fertilizer consumption about 18% without lowering the fruit yield or fruit quality parameters including firmness, total soluble solids, chlorophyll and ascorbic acid contents. This scenario can be considered as an efficient nitrogen fertilizing management method in commercial greenhouses using a low-cost machine vision-based robotic system.
2.Mechanical analysis of a robotic date harvesting manipulator
January 2015 – Russian Agricultural Sciences 41(1):80-85
Authors
Keyvan Asefpour Vakilian – Jafar Massah
Abstract
Abstract
In this study, an autonomous machine vision-based system was developed for precise nitrogen fertilizing management to improve nitrogen use efficiency in greenhouse crops. Two scenarios were considered: in the first scenario, nitrogen fertilization was done based on available instructions and protocols provided by the seed producer. The second scenario was nitrogen fertilization using a machine vision-based robot which was moving between the crop rows to monitor plants’ needs and requirements. The scenarios were examined in a hydroponic greenhouse for four varieties of cucumber. The extracted plant image features, namely entropy, energy, and local homogeneity were the markers for precise timing of nitrogen fertilization in the cucumber crops. In scenario 2, nitrogen fertilizing was based on a signal transmitted from the robot to a wireless receiver when at least one of the mean values of normalized image textural features of cucumber crops had a difference more than a (a = 10, 15, and 20%) with the same feature in scenario 1. Experimental results showed that scenario 2 for a = 15% decreased the nitrogen fertilizer consumption about 18% without lowering the fruit yield or fruit quality parameters including firmness, total soluble solids, chlorophyll and ascorbic acid contents. This scenario can be considered as an efficient nitrogen fertilizing management method in commercial greenhouses using a low-cost machine vision-based robotic system.
3. An Artificial Neural Network Modeling for Force Control System of a Robotic Pruning Machine
June 2014 – Journal of Information and Organizational Sciences 38(1):35-41
Authors
Ali Hashemi – Keyvan Asefpour Vakilian – Javad Khazaei – Jafar Massah
Abstract
Nowadays, there has been an increasing application of pruning robots for planted forests due to the growing concern on the efficiency and safety issues. Power consumption and working time of agricultural machines have become important issues due to the high value of energy in modern world. In this study, different multi-layer back-propagation networks were utilized for mapping the complex and highly interactive of pruning process parameters and to predict power consumption and cutting time of a force control equipped robotic pruning machine by knowing input parameters such as: rotation speed, stalk diameter, and sensitivity coefficient. Results showed significant effects of all input parameters on output parameters except rotational speed on cutting time. Therefore, for reducing the wear of cutting system, a less rotational speed in every sensitivity coefficient should be selected.
