Fungi affected fruit leaf disease classification using deep CNN architecture

Gaikwad, Sukanya S.; Rumma, Shivanand S.; Hangarge, Mallikarjun

doi:10.1007/s41870-022-00860-w

Cited by 14 publications

(3 citation statements)

References 19 publications

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“…In this study, a literature review of previous research serves as the foundation for the investigation. CNN-based models have been widely used to detect diseases in various plants, such as tomato [8], [9], apple [10], [11], grape [11], [12], and many more [13]- [16]. In detecting chili disease, the use of Convolutional Neural Networks (CNN) has also been done by previous researchers.…”

Section: Literature Reviewmentioning

confidence: 99%

Chili Leaf Health Classification using Xception Pretrained Model

Wulandari,

Munggaran,

Setiawan

et al. 2024

SISTEMASI

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As one of the high-demand horticultural crops, chili peppers have a significant impact on the economy of Indonesia. However, despite the growing demand and interest in chili peppers, their production often faces disruptions due to crop failures. One of the leading causes of such failures is pests and diseases. Among all parts of the chili plant, chili leaves are the most susceptible to damage. Distinguishing between healthy and unhealthy chili leaves can serve as an early detection step for chili diseases and preventive measures to contain their spread. Convolutional Neural Network (CNN) are effective algorithms for image classification. The development of CNN has led to the use of models previously trained on large datasets to accurately classify relatively small datasets. One such pretrained model known for its exceptional classification capabilities is Xception. By utilizing the pretrained Xception model trained on the ImageNet dataset for the classification of healthy and unhealthy chili leaf images, our model achieved an accuracy of 91% on a dataset containing 2136 images. Furthermore, the model achieved a 100% success rate by correctly predicting all 10 out of 10 given images.

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Section: Literature Reviewmentioning

confidence: 99%

Chili Leaf Health Classification using Xception Pretrained Model

Wulandari,

Munggaran,

Setiawan

et al. 2024

SISTEMASI

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“…The outcome in Table 6 also showcases that these existing studies with varied approaches introduces both beneficial aspect as well as limiting factors. [16], [26], [28] Higher accuracy Higher resource dependencies Genetic algorithm, correlation [17], [18], SVM [18] Search-based optimization simplified and faster…”

Section: Comparative Analysismentioning

confidence: 99%

Optimized deep learning-based dual segmentation framework for diagnosing health of apple farming with the internet of things

Raju,

Narasimhaiah

2024

IJ-AI

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<div align="left"><p>The high disease prevalence in apple farms results in decreased yield and income. This research addresses these issues by integrating internet of things (IoT) applications and deep neural networks to automate disease detection. Existing methods often suffer from high false positives and lack global image similarity. This study proposes a conceptual framework using IoT visual sensors to mitigate apple diseases' severity and presents an intelligent disease detection system. The system employs the augmented Otsu technique for region-aware segmentation and a colour-conversion algorithm for generating feature maps. These maps are input into U-net models, optimized using a genetic algorithm, which results in the generation of suitable masks for all input leaf images. The obtained masks are then used as feature maps to train the convolution neural network (CNN) model for detecting and classifying leaf diseases. Experimental outcomes and comparative assessments demonstrate the proposed scheme's practical utility, yielding high accuracy and low false-positive results in multiclass disease detection tasks.</p></div>

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“…Tese datasets are trained on AlexNet and SqueezeNet and use the same hyperparameters. Te recognition accuracy of the two models is basically the same, and the classifcation accuracy of color images is 86.8% and 86.6%, respectively, indicating that color images are efective for classifcation [16]. In recent years, researchers have used various deep learning networks and frameworks for experiments.…”

Section: Introductionmentioning

confidence: 99%

BLSENet: A Novel Lightweight Bilinear Convolutional Neural Network Based on Attention Mechanism and Feature Fusion Strategy for Apple Leaf Disease Classification

Fang,

Zhang,

et al. 2024

Journal of Food Quality

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Accurate identification of apple leaf diseases is of great significance for improving apple yield. The lesion area of the apple leaf disease image is small and vulnerable to background interference, which easily leads to low recognition accuracy. To solve this problem, a lightweight bilinear convolutional neural network (CNN) model named BLSENet based on attention mechanism is designed. The model consists of two subnetworks, and each subnetwork is embedded with a Squeeze-and-Excitation (SE) module. By using the feature extraction ability of the two subnetworks and combining the bilinear feature CONCAT operation, the multiscale features of the image are obtained. Compared with the unimproved model LeNet-5 (84.63%), BLSENet has higher accuracy in the test set, which indicates that SE module and bilinear feature fusion have a positive effect on the performance of the model, and BLSENet has the ability to identify apple leaf diseases. The model has achieved the expected goal and can provide technical support for accurate identification and real-time monitoring of apple disease images.

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Fungi affected fruit leaf disease classification using deep CNN architecture

Cited by 14 publications

References 19 publications

Chili Leaf Health Classification using Xception Pretrained Model

Chili Leaf Health Classification using Xception Pretrained Model

Optimized deep learning-based dual segmentation framework for diagnosing health of apple farming with the internet of things

BLSENet: A Novel Lightweight Bilinear Convolutional Neural Network Based on Attention Mechanism and Feature Fusion Strategy for Apple Leaf Disease Classification

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