Plant Disease Identification from Leaf Images using Deep CNN’s EfficientNet

Prodeep, Akibur Rahman; Hoque, A. S. M. Morshedul; Kabir, Md. Mohsin; Rahman, Md. Saifur; Mridha, M. F.

doi:10.1109/dasa54658.2022.9765063

Cited by 11 publications

(1 citation statement)

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“…Figure 4 displays some sample images from the dataset. Since its release, several studies have been conducted on identifying plant diseases using this dataset [51][52][53][54]. The pre-trained models were trained with 80% of the PlantVillage dataset, and 20% was used for validation and testing.…”

Section: Data Materialsmentioning

confidence: 99%

Application of Drone Surveillance for Advance Agriculture Monitoring by Android Application Using Convolution Neural Network

et al. 2023

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Plant diseases are a significant threat to global food security, impacting crop yields and economic growth. Accurate identification of plant diseases is crucial to minimize crop loses and optimize plant health. Traditionally, plant classification is performed manually, relying on the expertise of the classifier. However, recent advancements in deep learning techniques have enabled the creation of efficient crop classification systems using computer technology. In this context, this paper proposes an automatic plant identification process based on a synthetic neural network with the ability to detect images of plant leaves. The trained model EfficientNet-B3 was used to achieve a high success rate of 98.80% in identifying the corresponding combination of plant and disease. To make the system user-friendly, an Android application and website were developed, which allowed farmers and users to easily detect diseases from the leaves. In addition, the paper discusses the transfer method for studying various plant diseases, and images were captured using a drone or a smartphone camera. The ultimate goal is to create a user-friendly leaf disease product that can work with mobile and drone cameras. The proposed system provides a powerful tool for rapid and efficient plant disease identification, which can aid farmers of all levels of experience in making informed decisions about the use of chemical pesticides and optimizing plant health.

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Section: Data Materialsmentioning

confidence: 99%