Cucumber disease recognition using machine learning and transfer learning

Mia, Md. Jueal; Maria, Syeda Khadizatul; Taki, Shahrun Siddique; Biswas, Al Amin

doi:10.11591/eei.v10i6.3096

Cited by 19 publications

(4 citation statements)

References 27 publications

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“…Pre-trained VGG-16, VGG-19, ResNet-50, Xception, and Inception-V3 model weights were used. For each model that is provided, Table 1 displays the four-class resulting confusion matrix from [30], [31] (TP, FN, FP, TN). According to the classification, the procedure can unquestionably offer precise and accurate outcomes.…”

Section: Resultsmentioning

confidence: 99%

Tumor-Net: convolutional neural network modeling for classifying brain tumors from MRI images

Bitto

Bijoy

Yesmin

et al. 2023

Int. J. Adv. Intell. Informatics

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Abnormal brain tissue or cell growth is known as a brain tumor. One of the body's most intricate organs is the brain, where billions of cells work together. As a head tumor grows, the brain suffers damage due to its increasingly dense core. Magnetic resonance imaging, or MRI, is a type of medical imaging that enables radiologists to view the inside of body structures without the need for surgery. The image-based medical diagnosis expert system is crucial for a brain tumor patient. In this study, we combined two Magnetic Resonance Imaging (MRI)-based image datasets from Figshare and Kaggle to identify brain tumor MRI using a variety of convolutional neural network designs. To achieve competitive performance, we employ several data preprocessing techniques, such as resizing and enhancing contrast. The image augmentation techniques (E.g., rotated, width shifted, height shifted, shear shifted, and horizontally flipped) are used to increase data size, and five pre-trained models employed, including VGG-16, VGG-19, ResNet-50, Xception, and Inception-V3. The model with the highest accuracy, ResNet-50, performs at 96.76 percent. The model with the highest precision overall is Inception V3, with a precision score of 98.83 percent. ResNet-50 performs at 96.96% for F1-Score. The prominent accuracy of the implemented model, i.e., ResNet-50, compared with several earlier studies to validate the consequence of this introspection. The outcome of this study can be used in the medical diagnosis of brain tumors with an MRI-based expert system.

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Section: Resultsmentioning

confidence: 99%

Tumor-Net: convolutional neural network modeling for classifying brain tumors from MRI images

Bitto

Bijoy

Yesmin

et al. 2023

Int. J. Adv. Intell. Informatics

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“…Loyani et al [46] generated a dataset of tomato images to train semantic and instance segmentation models, achieving an mAP of 85.67% with Mask R-CNN and high accuracy metrics with U-Net. Mia et al (2021) [47] used conventional imaging for disease detection (downy mildew, powdery mildew, mosaic virus, belly rot, scab, and cottony leak) on cucumber plants, where DL models achieved a high accuracy of 93.23%. Mkonyi et al (2020) [8] utilized DL architectures to identify T. absoluta in tomatoes, confirming the reliability of their model with a 91.9% accuracy on test images.…”

Section: Artificial Intelligence In Agriculturementioning

confidence: 99%

A Citizen Science Tool Based on an Energy Autonomous Embedded System with Environmental Sensors and Hyperspectral Imaging

Kouzinopoulos,

Pechlivani,

Giakoumoglou

et al. 2024

JLPEA

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Citizen science reinforces the development of emergent tools for the surveillance, monitoring, and early detection of biological invasions, enhancing biosecurity resilience. The contribution of farmers and farm citizens is vital, as volunteers can strengthen the effectiveness and efficiency of environmental observations, improve surveillance efforts, and aid in delimiting areas affected by plant-spread diseases and pests. This study presents a robust, user-friendly, and cost-effective smart module for citizen science that incorporates a cutting-edge developed hyperspectral imaging (HI) module, integrated in a single, energy-independent device and paired with a smartphone. The proposed module can empower farmers, farming communities, and citizens to easily capture and transmit data on crop conditions, plant disease symptoms (biotic and abiotic), and pest attacks. The developed HI-based module is interconnected with a smart embedded system (SES), which allows for the capture of hyperspectral images. Simultaneously, it enables multimodal analysis using the integrated environmental sensors on the module. These data are processed at the edge using lightweight Deep Learning algorithms for the detection and identification of Tuta absoluta (Meyrick), the most important invaded alien and devastating pest of tomato. The innovative Artificial Intelligence (AI)-based module offers open interfaces to passive surveillance platforms, Decision Support Systems (DSSs), and early warning surveillance systems, establishing a seamless environment where innovation and utility converge to enhance crop health and productivity and biodiversity protection.

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“…Using integrated disease management strategies to reduce dependence on fungicides. Different computer visions have been adopted such as support vector machines (SVM), artificial neural networks (ANN), and convolutional neural network (CNN) for automated detection and classification of cucumber crop diseases (Pujari et al, 2016;Jiang et al, 2020, Mia et al, 2021.…”

Section: Introductionmentioning

confidence: 99%

Detecting Mildew Diseases in Cucumber Using Image Processing Technique

Eldesoky¹,

Omran

El-Nabi

2023

Misr Journal of Agricultural Engineering

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In Egypt, Cucumber is a crucial cash crop, and its farming could significantly benefit the country's agriculture-based economy. Meanwhile plant disease detection manually is costly and time consuming. This study aims to improve early identification of downy and powdery mildew diseases using machine vision by comparing the effectiveness of several detection methods and developing a real life application. This approach will involve five steps including: image acquisition, pre-processing, feature extraction, post-processing, and classification. In which the experiment was conducted on two greenhouses where 931 images were obtained and used with five key features to train and evaluate the proposed methods. The classification performance of three machine learning algorithms, named discriminant analysis (DA), support vector machine (SVM) and K-nearest neighbors (KNN), were compared. The results indicated that the fine gaussian SVM achieved the highest classification accuracy rate of 96%, where fine KNN got 95.8%, and quadratic DA obtained the lowest value 92.8%. Additionally, the suggested method has a practical application that enables automatic mildew disease detection via personal computers, eliminating the need for sample collection and laboratory analysis. This method could also be extended to identify other plant diseases and pests and track disease progression as the study moves forward.

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Cucumber disease recognition using machine learning and transfer learning

Cited by 19 publications

References 27 publications

Tumor-Net: convolutional neural network modeling for classifying brain tumors from MRI images

Tumor-Net: convolutional neural network modeling for classifying brain tumors from MRI images

A Citizen Science Tool Based on an Energy Autonomous Embedded System with Environmental Sensors and Hyperspectral Imaging

Detecting Mildew Diseases in Cucumber Using Image Processing Technique

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