Deep Network with Score Level Fusion and Inference-Based Transfer Learning to Recognize Leaf Blight and Fruit Rot Diseases of Eggplant

Haque, Md. Reduanul; Sohel, Ferdous

doi:10.3390/agriculture12081160

Cited by 11 publications

(2 citation statements)

References 27 publications

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“…It was based on CNN-SNM and CNN softmax pipeline to infer classes. Its results achieved better accuracy and lower false positives than others [19]. Features were extracted from hyper-spectral and chlorophyll fluorescence images and end-to-end fused to detect the hazardous substance.…”

Section: Related Workmentioning

confidence: 99%

An Early and Smart Detection of Corn Plant Leaf Diseases Using IoT and Deep Learning Multi-Models

Rashid,

Aslam,

Aziz

et al. 2024

IEEE Access

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Plant leaf diseases have various causes, leading to severe disorders. The early and accurate detection and classification of these diseases are fundamental for fostering healthy crop production. In recent years, smart agricultural systems have garnered significant attention due to their capability to enhance efficiency by deploying sensor networks and Internet of Things (IoT) devices that collect and analyze environmental data. However, traditional plant disease detection methods are manual, time-consuming, and often need help handling the data's complexity and dynamism. These manual methods do not use heterogeneous data to make better decisions. Corn holds significant importance yet it faces numerous diseases that include main three diseases such as blight, common rust, and grey leaf spot. The advancement of computer technology has led to a pivotal focus on corn leaf diseases classification application based on deep learning. Convolutional Neural Networks (CNNs) have revealed remarkable achievements within Precision Agriculture (PA) due to their ability to enhance information. To this end, this work introduces a CNN-based architecture, the Multi-Model Fusion Network (MMF-Net). Its primary objective is to classify diseases within the realm of PA. MMF-Net integrates multi-contextual features using RL-block and PL-blocks 1 & 2, thus effectively combining different model streams trained on heterogeneous data. The RLblock uses spatial range to process coarse grained images to convolve the local context, while PL-block 1 extracts fine-grained global context by expanding the perceptual area of images. PL-block 2 deals with real-life environmental parameters as features. The extracted features are syndicated using multiple classifiers that ensemble three individual blocks at the decision level to improve the accuracy. After fusion, it uses adaptively the majority voting scheme to generate the final decision probability score of the base model. Multiple experiments are conducted involving the corn leaf diseases dataset and a reallife numerical dataset, generating an impressive 99.23% accuracy in the classification of corn leaf diseases. Overall, MMF-Net provides a promising and smart solution to identify plant leaf diseases in PA effectively.

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Section: Related Workmentioning

confidence: 99%

An Early and Smart Detection of Corn Plant Leaf Diseases Using IoT and Deep Learning Multi-Models

Rashid,

Aslam,

Aziz

et al. 2024

IEEE Access

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“…Finally, the best model with optimal hyperparameters has been deployed into the mobile operating system for designing a mobile app to detect the different types of diseases in real-time. In [ 28 ], the researchers have proposed a novel deep fusion stream architecture (CNN-SVM, CNN-Softmax) based inference model to identify nine different types of diseases (aphids, bacterial wilt, cercospora melongenae, collar rot, Colorado potato beetle, little leaf, spider mites, Phomopsis blight, tobacco mosaic virus) in Eggplant using two different datasets and achieved a maximum mean classification rate of 98.9% compared to the state-of-the-art methods (Inception V3, VGG19, MobileNet, NasNetMobile, VGG16, and ResNet50). A novel 14-layer DCNN architecture has been proposed to classify 42 different types of leaf diseases from 12 plants using a large dataset of images (139,000) [ 29 ].…”

Section: State-of-the-art Work Related To Leaf Disease Detectionmentioning

confidence: 99%

Brinjal leaf diseases detection based on discrete Shearlet transform and Deep Convolutional Neural Network

Abisha¹,

Mutawa

Murugappan

et al. 2023

PLoS ONE

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Different diseases are observed in vegetables, fruits, cereals, and commercial crops by farmers and agricultural experts. Nonetheless, this evaluation process is time-consuming, and initial symptoms are primarily visible at microscopic levels, limiting the possibility of an accurate diagnosis. This paper proposes an innovative method for identifying and classifying infected brinjal leaves using Deep Convolutional Neural Networks (DCNN) and Radial Basis Feed Forward Neural Networks (RBFNN). We collected 1100 images of brinjal leaf disease that were caused by five different species (Pseudomonas solanacearum, Cercospora solani, Alternaria melongenea, Pythium aphanidermatum, and Tobacco Mosaic Virus) and 400 images of healthy leaves from India’s agricultural form. First, the original plant leaf is preprocessed by a Gaussian filter to reduce the noise and improve the quality of the image through image enhancement. A segmentation method based on expectation and maximization (EM) is then utilized to segment the leaf’s-diseased regions. Next, the discrete Shearlet transform is used to extract the main features of the images such as texture, color, and structure, which are then merged to produce vectors. Lastly, DCNN and RBFNN are used to classify brinjal leaves based on their disease types. The DCNN achieved a mean accuracy of 93.30% (with fusion) and 76.70% (without fusion) compared to the RBFNN (82%—without fusion, 87%—with fusion) in classifying leaf diseases.

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Plant disease recognition in a low data scenario using few-shot learning

Rezaei,

Diepeveen,

Laga

et al. 2024

Computers and Electronics in Agriculture

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Deep Network with Score Level Fusion and Inference-Based Transfer Learning to Recognize Leaf Blight and Fruit Rot Diseases of Eggplant

Cited by 11 publications

References 27 publications

An Early and Smart Detection of Corn Plant Leaf Diseases Using IoT and Deep Learning Multi-Models

An Early and Smart Detection of Corn Plant Leaf Diseases Using IoT and Deep Learning Multi-Models

Brinjal leaf diseases detection based on discrete Shearlet transform and Deep Convolutional Neural Network

Plant disease recognition in a low data scenario using few-shot learning

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