Predicting the Tomato Plant Disease Using Deep Learning Techniques

Trivedi, Rishikesh Bhupendra; Mittal, Daksh; Sahani, Anuj; Fernandes, Clely Voyena; Goyal, Somya; Chatterjee, Jyotir Moy; Mehta, Vanshika

doi:10.1007/978-981-19-7524-0_50

Cited by 4 publications

(1 citation statement)

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“…Trivedi et al (2023) proposed a VGG16‐based model to predict nine types of tomato diseases using a publicly available data set, namely PlantVillage, and achieved an accuracy of 99.7% and an area under the curve (AUC) of 93.3%. A DL model TL‐MobileNetV2 was developed based on MobileNetV2 architecture (Gulzar, 2023), and a data set of 40 types of fruits was used to train and test the proposed model.…”

Section: Introductionmentioning

confidence: 99%

Implementing deep‐learning techniques for accurate fruit disease identification

Sujatha

Mahalakshmi

Chatterjee³

2023

Plant Pathology

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To overcome the problems of manual identification of fruit disease, this work proposes a deep‐learning model to analyse fruit images to detect diseases in the fruit. We are proposing here a convolutional neural network (CNN)‐based model for fruit disease classification. By including many layers, the proposed CNN model extracts numerous features from the fruit, deals with the large data set and finally evaluates it. With the MobileNetv2 model, the disease prediction accuracy for papaya, guava and citrus was 99.4%, 98.8% and 95.8% and the recall values were 99.4%, 98.8% and 93.8%, respectively. With VGG16, the disease prediction accuracy for papaya, guava and citrus was 97.7%, 99.6% and 94.2% and the recall values were 96.5%, 99.6% and 89.2%, respectively. Finally, with DenseNet121, the disease prediction accuracy for papaya, guava and citrus was 99.4%, 97.6% and 99.2%, and the recall values were 98.8%, 97.6% and 99.2%, respectively.

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Section: Introductionmentioning

confidence: 99%