Influence of Mushroom Strains and Population Density on the Performance of a Robotic Harvester

Noble, R.; Reed, J.N.; Miles, S.J.; Jackson, Alvin; Butler, J.

doi:10.1006/jaer.1997.0195

Cited by 19 publications

(6 citation statements)

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“…By comparing these parameters, the following can be seen: ( 1) The algorithm proposed in this paper has the best multiarm task distribution uniformity and the highest utilization of multiarm cooperation. The greater the number of fruits to be harvested, the more obvious the advantages compared with the other two algorithms; (2) The harvesting success rate after using the improved algorithm in this paper can always The other results obtained by the three algorithms and the important parameters are shown in Table 4. By comparing these parameters, the following can be seen: (1) The algorithm proposed in this paper has the best multiarm task distribution uniformity and the highest utilization of multiarm cooperation.…”

Section: Experiments Of Multiobjective Optimization Algorithm For Mul...mentioning

confidence: 80%

“…The red circle represents the fruit to be picked, and the black circle represents the immature fruit. The picking robot has three arms, and the working area is divided into five accessible areas, where S 1 (1,1) represents the exclusive area for Arm1, and the fruits in this area can only be harvested by arm1, S 2 (1,2) is the partial shared area that can only be harvested by Arm 1 and Arm 2, S 3 (1,3) is the fully shared area that can be harvested by all three arms, S 4 (2,3) is the partial shared area that can be harvested by Arm 2 and Arm 3, and S 5 (3,3) is the exclusive area that can only be harvested by arm 3. Most of the fruits to be picked are distributed in the exclusive area S 1 (1,1) and the partial shared area S 2 (1,2), and there is a large fruit cluster C1 over the two areas, meanwhile.…”

Section: Discussionmentioning

confidence: 99%

“…This kind of fruit usually needs to be harvested by selective harvesting, and the aggregated mature fruits need to be harvested in the specified order; otherwise, the fruit is easy to be damaged. Therefore, it is difficult to achieve effective automatic nondestructive harvesting [2]. Under the trend of serious labor shortages, although the cultivation of such crops can achieve factory-like and intensive production, the labor-intensive harvesting process [3] still relies on manual labor, which has become the main bottleneck affecting further production and efficiency improvement.…”

Section: Introductionmentioning

confidence: 99%

“…Agriculture 2022, 12, x FOR PEER REVIEW 2 of 25 automatic nondestructive harvesting [2]. Under the trend of serious labor shortages, although the cultivation of such crops can achieve factory-like and intensive production, the labor-intensive harvesting process [3] still relies on manual labor, which has become the main bottleneck affecting further production and efficiency improvement.…”

Section: Introductionmentioning

confidence: 99%

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Research on Multiobjective Optimization Algorithm for Cooperative Harvesting Trajectory Optimization of an Intelligent Multiarm Straw-Rotting Fungus Harvesting Robot

Yang

Jia

et al. 2022

Agriculture

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In view of the difficulties of fruit cluster identification, the specific harvesting sequence constraints of aggregated fruits, and the balanced harvesting task assignment for the multiple arms with a series-increasing symmetric shared (SISS) region, this paper proposes a multi-objective optimization algorithm, which combines genetic algorithm (GA) and ant colony optimization (ACO) stepwise, to optimize the multiarm cooperative harvesting trajectory of straw-rotting fungus to effectively improve the harvesting efficiency and the success rate of non-destructive harvesting. In this approach, firstly, the multiarm trajectory optimization problem is abstracted as a multiple travelling salesman problem (MTSP). Secondly, an improved local density clustering algorithm is designed to identify the cluster fruits to prepare data for harvesting aggregated fruits in a specific order later. Thirdly, the MTSP has been decomposed into M independent TSP (traveling salesman problem) problems by using GA, in which a new DNA (deoxyribonucleic acid) assignment rule is designed to resolve the problem of the average distribution of multiarm harvesting tasks with the SISS region. Then, the improved ant colony algorithm, combined with the auction mechanism, is adopted to achieve the shortest trajectory of each arm, which settles the difficulty that the clustered mature fruits should be harvested in a specified order. The experiments show that it can search for a relatively stable optimal solution in a relatively short time. The average harvesting efficiency is up to 1183 pcs/h and the average harvesting success rate is about 97%. Therefore, the proposed algorithm can better plan the harvesting trajectory for multiarm intelligent harvesting, especially for areas with many aggregated fruits.

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Section: Experiments Of Multiobjective Optimization Algorithm For Mul...mentioning

confidence: 80%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Research on Multiobjective Optimization Algorithm for Cooperative Harvesting Trajectory Optimization of an Intelligent Multiarm Straw-Rotting Fungus Harvesting Robot

Yang

Jia

et al. 2022

Agriculture

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“…However, in the present experiment, such cap detachment and stipe splitting occurred for 16-50% of the mushrooms tested, depending on the compost and casing depth. This indicates that a twist action alone would result in unacceptable losses using a robotic harvester: a twist combined with a bending action as described by Reed et al (1995) and Noble et al ( 1997) is therefore necessary.…”

Section: Discussionmentioning

confidence: 98%

Influences of compost and casing layer depths on the mechanical properties of mushrooms

Noble¹,

Rämä²,

Miles

et al. 1997

Annals of Applied Biology

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The mushroom growing bed consists of an underlying compost which is covered with casing (a peat and chalk or lime mixture) to induce fruit-body formation. The influences of the depths of the compost and casing layers on the mushroom texture, susceptibility to bruising, and ease of detachment from the growing bed surface were investigated. Textural properties were determined on cubes of cap tissue and detachment torque was determined by using an instrumented rotating suction cup which was lowered on to the caps. The proportion of mushrooms which detached at the base from the bed surface was recorded, since this is an important parameter for an automatic (robotic) harvesting system. Shallow casing resulted in mushrooms that were firmer and less likely to deform plastically, but that showed greater discolouration after a standard bruising treatment. Shallower casing also increased the proportion of mushrooms that detached at the base. Increasing the depth of compost produced firmer mushrooms, significantly increased the detachment torque and reduced the proportion of mushrooms detached at the base. The proportion of mushrooms that detached at the base correlated negatively with the plastic deformation of cap tissue. A positive correlation was found between mushroom dry matter content and tissue firmness.

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Harvesting and Processing of Mushrooms

Valverde

2017

Edible and Medicinal Mushrooms

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Influence of Mushroom Strains and Population Density on the Performance of a Robotic Harvester

Cited by 19 publications

References 1 publication

Research on Multiobjective Optimization Algorithm for Cooperative Harvesting Trajectory Optimization of an Intelligent Multiarm Straw-Rotting Fungus Harvesting Robot

Research on Multiobjective Optimization Algorithm for Cooperative Harvesting Trajectory Optimization of an Intelligent Multiarm Straw-Rotting Fungus Harvesting Robot

Influences of compost and casing layer depths on the mechanical properties of mushrooms

Harvesting and Processing of Mushrooms

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