Identification of Corn Leaves Diseases Images Using MobileNet Architecture in SmartPhones

Negrete, Pablo David Minango; Zambrano, Marcelo; Paredes-Parada, Wladimir; Tasiguano, Cristian; Ayala, Karla

doi:10.1007/978-3-031-25942-5_51

Cited by 1 publication

(1 citation statement)

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“…The performance of the MobileNetV2 network with the IncMB module opened up the possibility of implementing the model on mobile devices to facilitate its use by agricultural experts and ensure the early detection of tomato diseases. In the study by Minango et al [53], it was observed that the MobileNet architecture was also used in deployment. During the training process, the accuracy and validation loss values were evaluated to prevent overfitting or underfitting of the models.…”

Section: Discussionmentioning

confidence: 99%

Image-Based Leaf Disease Recognition Using Transfer Deep Learning with a Novel Versatile Optimization Module

Radočaj,

Martinović

2024

BDCC

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Due to the projected increase in food production by 70% in 2050, crops should be additionally protected from diseases and pests to ensure a sufficient food supply. Transfer deep learning approaches provide a more efficient solution than traditional methods, which are labor-intensive and struggle to effectively monitor large areas, leading to delayed disease detection. This study proposed a versatile module based on the Inception module, Mish activation function, and Batch normalization (IncMB) as a part of deep neural networks. A convolutional neural network (CNN) with transfer learning was used as the base for evaluated approaches for tomato disease detection: (1) CNNs, (2) CNNs with a support vector machine (SVM), and (3) CNNs with the proposed IncMB module. In the experiment, the public dataset PlantVillage was used, containing images of six different tomato leaf diseases. The best results were achieved by the pre-trained InceptionV3 network, which contains an IncMB module with an accuracy of 97.78%. In three out of four cases, the highest accuracy was achieved by networks containing the proposed IncMB module in comparison to evaluated CNNs. The proposed IncMB module represented an improvement in the early detection of plant diseases, providing a basis for timely leaf disease detection.

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Section: Discussionmentioning

confidence: 99%