Preprocessing method of night vision image application in apple harvesting robot

Due to the low working efficiency of apple harvesting robots,there is still a long way to go for commercialization.The machine performance and extended operating time are the two research aspects for improving efficiencies of harvesting robots,this study focused on the extended operating time and proposed a round-the-clock operation mode.Due to the influences of light,temperature,humidity,etc.,the working environment at night is relatively complex,and thus restricts the operating efficiency of the apple harvesting robot.Three different artificial light sources(incandescent lamp,fluorescent lamp,and LED lights)were selected for auxiliary light according to certain rules so that the apple night vision images could be captured.In addition,by color analysis,night and natural light images were compared to find out the color characteristics of the night vision images,and intuitive visual and difference image methods were used to analyze the noise characteristics.The results showed that the incandescent lamp is the best artificial auxiliary light for apple harvesting robots working at night,and the type of noise contained in apple night vision images is Gaussian noise mixed with some salt and pepper noise.The preprocessing method can provide a theoretical and technical reference for subsequent image processing.

Ergonomic and efficiency analysis of conventional apple harvest process

Apples are still manually harvested by workers using ladders and buckets.Though it is known that manual apple harvest would probably lead to occupational injuries(e.g.,back,neck,and shoulder strains),there has been little research that focuses on identifying the awkward activities/postures of pickers during the harvest process.After categorizing apple harvest work into 12 activities,this study used the method of Rapid Upper Limb Assessment(RULA)to identify awkward postures/activities that occurred during apple harvest.Awkward activities confirmed include descending a ladder,dumping apples,picking high and low apples on a ladder as well as on the ground,and moving a ladder,with potential reasons for each awkward activity provided.Meanwhile,it was demonstrated that pickers spent approximately 64%of working time under awkward postures that would lead to occupational diseases.In addition,this study analyzed picker harvest efficiency in terms of general and detail manners.The general mode assessed harvest activity in terms of picking and non-picking,with results showing that pickers averagely spent 76%(±7%)of harvest time in picking apples.Further analysis evaluated picking activities in terms of reaching,detaching,and transporting apples,with results showing that pickers spent averagely 30%(±6%)of time in detaching apples,which is the value time during apple harvest.Furthermore,valuable picking time ratio was obtained as the multiplication of picking time ratio and detaching time ratio.With a valuable ratio of 22%(±5%),it theoretically demonstrated the low harvest efficiency of the traditional harvest method.Since a majority of the awkward activities and the low efficiency were because of the ladders/buckets,using a harvest-assist unit may be a potential solution.Additionally,more efforts should be spent on the development of innovative mechanism to replace worker in placing attached apples to the bucket.Once the time for transporting apples is eliminated,the time for reaching apples is also removed,indicating the harvest efficiency would be improved significantly.

Models for economic evaluation of multi-purpose apple harvest platform and software development

An increasing number of U.S.apple growers are now interested in using harvest platforms to increase harvest productivity,expand labor pool,and alleviate tough working conditions.To maximize benefits,functions of thinning,pruning&training,and infield sorting have been or are to be incorporated into harvest platforms.Though growers are most concerned with economic benefits,few cost-benefit studies had been conducted on different platforms.In the meantime,economic analysis procedure is complex and each analysis is for one specific machine(not for general purposes).No software has been developed as a general and ready-to-use tool for growers and researchers for the platform economic analysis.In this study,platforms,both available on the marketplace and developed in lab as pilot trials,were reviewed.Costs and benefits models were then established,based on which multi-purpose apple harvest platform economic evaluation software(iMPAHP)was developed(capable of evaluating a wide variety of apple harvest platforms).A case study(machine cost of$100000,accommodating 6 workers,processing apple incidence of 10%with 90%sort-out rate,and harvest,thinning,and pruning&training productivity increase by 40%,50%,and 60%,respectively)based on iMPAHP demonstrated that infield sorting,harvest,thinning,and pruning and training accounted for 48.4%,23.9%,14.3%,and 13.4%of the total benefits,respectively.In the case that the platform was in all-four-purpose-application,the net present value(NPV)analysis of a 10-year investment showed a positive return of$60547.However,without infield sorting function,the NPV resulted in a negative value,indicating a loss for the machine investment.Though incorporating the modular infield sorting system certainly increased the overall machine investment by$30000,the benefits outweighed the costs.