Automatic Mango Leaf and Trunk Detection as Supporting Tool of Mango Pest Identifier (MPI)

Kusrini, Kusrini; Suputa, Suputa; Setyanto, Arief; Agastya, I Made Artha; Pariyasto, Sofyan; Widiyanto, Wahyu; Fadlurahman,; Priantoro, Herlambang

doi:10.1109/icoris52787.2021.9649558

Cited by 2 publications

(2 citation statements)

References 31 publications

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“…To reduce the possibility of errors due to different input data, before the process of classifying pests on fruit, the application will identify whether the image entered is a fruit image or something else. The identification model will use the results of previous research [47] by adding fruit data as one of the classes.…”

Section: Discussionmentioning

confidence: 99%

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A comparative study of mango fruit pest and disease recognition

et al. 2022

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Mango is a popular fruit for local consumption and export commodity. Currently, Indonesian mango export at 37.8 M accounted for 0.115% of world consumption. Pest and disease are the common enemies of mango that degrade the quality of mango yield. Specialized treatment in export destinations such as gamma-ray in Australia, or hot water treatment in Korea, demands pest-free and high-quality products. Artificial intelligence helps to improve mango pest and disease control. This paper compares the deep learning model on mango fruit pests and disease recognition. This research compares Visual Geometry Group 16 (VGG16), residual neural network 50 (ResNet50), InceptionResNet-V2, Inception-V3, and DenseNet architectures to identify pests and diseases on mango fruit. We implement transfer learning, adopt all pre-trained weight parameters from all those architectures, and replace the final layer to adjust the output. All the architectures are re-train and validated using our dataset. The tropical mango dataset is collected and labeled by a subject matter expert. The VGG16 model achieves the top validation and testing accuracy at 89% and 90%, respectively. VGG16 is the shallowest model, with 16 layers; therefore, the model was the smallest size. The testing time is superior to the rest of the experiment at 2 seconds for 130 testing images.

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Section: Discussionmentioning

confidence: 99%

“…This research is carried out to extend our previous work on recognizing mango pests on leaves. The recognition system for pests on mango leaves has been implemented on a mobile application [14]. Following up on suggestions on evaluation results [15], we improve the capability as an extension to the fruit.…”

Section: Introductionmentioning

confidence: 99%