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- W4316659539 abstract "Fruit harvesting is the most crucial task which requires accuracy, experience and right judgment. It directly impacts profit or loss of the farmers. Early harvesting or very late harvesting may cause severe losses to the farmer's income. Harvesting of fruit depends on many parameters like color, shape, firmness, day time, market need, market distance, availability of labours' and transport facilities. More than 70% percent of farmers in India are poor and cannot afford high end machineries or technologies for the fruit harvesting. Low-cost fruit pickers are available in the Indian local market to ease the jobs which are better than manual hand-picking but they do not address the problem of accuracy or right decision whether the fruit is harvestable. This work proposes a low cost; IoT based smart system which can be integrated on any fruit picker easily. The proposed system helps farmers as the decision support tool in fruit harvesting. To detect the fruit on tree and to decide whether fruit is harvestable three state-of-art pretrained image classification models InceptionV3, ResNet50, and MobileNet with transfer learning were used. The model was trained on 400 images collected form internet. The results show that ResNet-50 has performed better in terms of accuracy as compared to InceptionV3 and MobileNet. As the model is deployed on microcomputer, MobileNet is best suited for the embedded device even though its accuracy is less." @default.
- W4316659539 created "2023-01-17" @default.
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- W4316659539 date "2022-08-26" @default.
- W4316659539 modified "2023-10-13" @default.
- W4316659539 title "Smart Low Cost Fruit Picker for Indian Farmers" @default.
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- W4316659539 doi "https://doi.org/10.1109/iccubea54992.2022.10010984" @default.
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