Kiwifruit detection in challenging conditions

Nejati, Mahla; Penhall, Nicky; Williams, Henry; Bell, Jamie; Lim, JongYoon; Ahn, Ho Seok; MacDonald, Bruce

doi:10.48550/arxiv.2006.11729

Cited by 1 publication

(3 citation statements)

References 13 publications

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“…Figure 13(b) shows the pictures of kiwifruit in day and night conditions. 27 The moisture content present at the skin of the kiwifruit makes some changes in the night time captured images and needs additional focus.…”

Section: Role Of Night Time Imaging In Fruit Detectionmentioning

confidence: 99%

“…Processing of kiwifruit in overexposed condition has been depicted in. 27 For the detection a fruit in RGB colour space images has been converted in YCbCr colour space. From that it is observed that the blue colour space is more exposed to sunlight and hence replaced with green colour space.…”

Section: Detection Of Fruits In Overexposed Conditionsmentioning

confidence: 99%

“…(a) Day and Night time grape cluster images with histogram plot. 26 (b) Kiwifruit: (a) Day time without flash (b) with flash and (c) Night time with flash 27.…”

mentioning

confidence: 99%

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Selective fruit harvesting: Research, trends and developments towards fruit detection and localization – A review

Kumar

Mohan

2022

Proceedings of the Institution of Mechanical Engineers, Part C:

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Progressive application of multidisciplinary research and development pushes the evolution of automation in many subsectors of agriculture to increase productivity, economic growth and environmental preservation with the help of robotics and artificial intelligence. Fruit harvesting robots have been developed mainly to provide support in the field for limited labour resources, to enable selective harvesting, to improve the efficiency and to preserve the quality of fruits. Even a small delay in harvesting can cause a maximum impact to the quality of the fruit. Selective fruit harvesting is an integration of different subcomponents. This paper provides a brief analysis of the techniques in selective fruit harvesting for the past 6 years starting from 2017 to 2022, associated principles, limitations and directions for future challenges. The first subcomponent is the vision system, it captures the information about the fruit in a tree canopy to perform efficient 2D and 3D localization. Hence getting accurate information from the vision system is more essential even in a complex agricultural environment. The detection of fruit from the vision system is normally done with two major methods such as traditional image processing and deep learning approaches. The result shows that the traditional methods provide high efficiency but the colour similarity, complex backgrounds and lightning conditions often makes failure in detection. Shortage of standard dataset and high-powered processing devices hinders the development of deep learning algorithms. Also, the usage of large data sets reduces the training speed even in pre-trained networks. For fruit grasping and detachment, the detection of plucking point is more essential to preserve the quality and for further storage. The elaborate description about the framework, limitations in current sensing, fruit and picking point detection algorithms provides guidelines to the researchers in building a fully automated robotic system to increase the processing speed and production rate.

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Section: Role Of Night Time Imaging In Fruit Detectionmentioning

confidence: 99%

Section: Detection Of Fruits In Overexposed Conditionsmentioning

confidence: 99%