External defects and severity level evaluation of potato using single and multispectral imaging in near infrared region Author links open overlay panel

Non-invasive potato defects detection has been demanded for sorting and grading purpose.Researches on the classification of the defects has been available,however,investigation on the severity level calculation is limited.For the detection of the common scab,it has been found that imaging in the infrared region provide an interesting characteristic that could distinguish defected area to normal area.Thus,investigations on this wavelength range is interesting to add more knowledge and for applications.In this research,the multispectral image has been obtained and investigated especially at three wavelengths(950,1150,1600 nm).Image pre-processing and pseudo-color conversion techniques were explored to enhance the contrast between defects,normal background skin area and soil deposits.Results show that external defects,such as common scab and some mechanical damage types,appear brighter in the near infrared region,especially at 1600 nm against the normal skin background.It has been found that pseudo-color images conversion provides more information regarding type if surface characteristics compared to grayscale single imaging.Image segmentation using pseudo-color images after multiplication operation pre-processing could be used for common scab and mechanical damage detection excluding soil deposits with a Dice Sorensen coefficient of 0.64.In addition,image segmentation using single image at 1600 nm shown relatively better results with Dice Sorensen coefficient of 0.72 with note that thick soil deposits will also be segmented.Defect severity level evaluation had an R2 correlation of 0.84 against standard measurements of severity.

Static and dynamic evaluations of acoustic positioning system using TDMA and FDMA for robots operating in a greenhouse

Acoustic positioning system has great potential to be applied in a greenhouse due to its centimeter-level accuracy,low cost,and ability of extensive greenhouse coverage.Spread Spectrum Sound-based local positioning system(SSSLPS)was proposed to be a navigation tool for multiple agricultural robots by the authors'research team.However,to increase the system capacity for positioning multiple robots in a greenhouse,the near-far problem caused by the interference between speakers needs to be overcome.The use of different access methods,Time Division Multiple Access(TDMA)or Frequency Division Multiple Access(FDMA),is essential in the SSSLPS system for solving the near-far problem.The static positioning in a greenhouse was first evaluated by setting different parameters to determine the optimal signal setting for a dynamic experiment.From that,the moving robot tests were added with a motion capture system and tested the performance of TDMA and FDMA.The results demonstrated that TDMA can be used in a stationary sound-based positioning system with 12.2 mm accuracy,but it has a time delay problem in dynamic positioning.A simulation was designed to mimic the position error increases with different moving speeds.Although FDMA has the sound damping problem in high-frequency regions creating a peak detection issue,it achieved a higher accuracy with an average position error of 62.1 mm compared to 180.3 mm of TDMA.This study shows that the TDMA method is suitable for static measurements,while the FDMA method is suitable for measuring dynamic objects and controlling mobile robots.