A comparative study of mango fruit pest and disease recognition

Abstract

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.

Implementation of Convolutional Neural Network (CNN) for Image Classification of Leaf Disease In Mango Plants Using Deep Learning Approach

“…VGG16 is a model developed by the Vision Geometry Group at Oxford University [15]. This model managed to win the Imagenet competition in 2014 [9]. The VGG16 architecture consists of a series of uniform convolution blocks followed by an integrated pooling layer.…”

“…one of them is in research conducted by Samala, et al by identifying diseases in tomato leaves using InceptionV3 architecture which produces an accuracy rate of 99% [8]. His further research was conducted by Kusrini, et diseases in mangoes, it was found that the accuracy results using the VVG16 model achieved top validation with accuracy of 89% and 90% respectively [9]. Another study conducted by Tsabitah Ayu, et al using a model (CNN) to classify the types of mango leaves affected by pests and healthy obtained accuracy results of 0.96% [10].…”

Classification of Spotted Disease on Sugarcane Leaf Image Using Convolutional Neural Network Algorithm

“…Sementara pelatihan dilakukan untuk melatih lapisan yang terakhir (Fully Connected Layer) dengan fungsi softmax. Dengan membatasi bobot untuk lapisan terakhir jaringan, kami dapat mempercepat waktu pelatihan tanpa mengorbankan akurasi pengklasifikasi [17]. Pembekuan bobot tersebut dinamankan Fine-tuning.…”