In the last few years robotics has been increasingly adopted in agriculture to improve productivity and efficiency. Most of the efforts in this research area have been devoted to fresh market fruit and vegetable harvesting tasks, which are generally, time consuming, tiring, and particularly demanding. For many crops, harvest labor accounts for as much as one-half to two-thirds of the total labor costs. Moreover, harvesting is expected to be automated due to a decrease in the farmer population. This chapter describes two examples of robotic system dealing with the harvest of radicchio and post-harvest of fennel, respectively. These cultivations are widely grown in Italy and their market value and production rates justify the process automation. Radicchio, which is a red, broad leaf, heading form of chicory, requires a stem cutting approximately 10 mm underground in order to avoid sudden waste and to ensure appropriate product storage. Similarly, fennel requires a cutting operation to remove the root and the upper leaves after its harvest. The quality of the ready-to-market product largely depends on the accuracy of this operation. In Section 2, a robot for the harvesting of red radicchio is presented comprising a chain of two four-bar linkages as a manipulator and an optimized gripper. The robotic harvester autonomously performs its task using a vision-based module to detect and localize the plants in the field; we call it the radicchio visual localization (RVL) module. Section 3 presents a robotic system for the automated cutting of just-harvest fennel employing an innovative mechanism controlled by a visionbased inspection system, which we call the fennel visual identification (FVI) module. The FVI module is designed to analyze fennels traveling on a conveyor in sparse order and detect accurately root and leaves, which are automatically removed. Both visual algorithms are based on intelligent morphological and color filtering optimized in each one of the two cases to gain computational efficiency and real-time performance. Section 4 concludes this chapter describing experimental results and discussions to validate our systems and asses their performance.

In the last few years robotics has been increasingly adopted in agriculture to improve productivity and efficiency. Most of the efforts in this research area have been devoted to fresh market fruit and vegetable harvesting tasks, which are generally, time consuming, tiring, and particularly demanding. For many crops, harvest labor accounts for as much as one-half to two-thirds of the total labor costs. Moreover, harvesting is expected to be automated due to a decrease in the farmer population

Robotics for Agricultural Systems

FOGLIA, Mario;GENTILE, Angelo;
2008

Abstract

In the last few years robotics has been increasingly adopted in agriculture to improve productivity and efficiency. Most of the efforts in this research area have been devoted to fresh market fruit and vegetable harvesting tasks, which are generally, time consuming, tiring, and particularly demanding. For many crops, harvest labor accounts for as much as one-half to two-thirds of the total labor costs. Moreover, harvesting is expected to be automated due to a decrease in the farmer population. This chapter describes two examples of robotic system dealing with the harvest of radicchio and post-harvest of fennel, respectively. These cultivations are widely grown in Italy and their market value and production rates justify the process automation. Radicchio, which is a red, broad leaf, heading form of chicory, requires a stem cutting approximately 10 mm underground in order to avoid sudden waste and to ensure appropriate product storage. Similarly, fennel requires a cutting operation to remove the root and the upper leaves after its harvest. The quality of the ready-to-market product largely depends on the accuracy of this operation. In Section 2, a robot for the harvesting of red radicchio is presented comprising a chain of two four-bar linkages as a manipulator and an optimized gripper. The robotic harvester autonomously performs its task using a vision-based module to detect and localize the plants in the field; we call it the radicchio visual localization (RVL) module. Section 3 presents a robotic system for the automated cutting of just-harvest fennel employing an innovative mechanism controlled by a visionbased inspection system, which we call the fennel visual identification (FVI) module. The FVI module is designed to analyze fennels traveling on a conveyor in sparse order and detect accurately root and leaves, which are automatically removed. Both visual algorithms are based on intelligent morphological and color filtering optimized in each one of the two cases to gain computational efficiency and real-time performance. Section 4 concludes this chapter describing experimental results and discussions to validate our systems and asses their performance.
Mechatronics and Machine Vision in Practice
978-3-540-74026-1
Springer
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/11589/12949
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