@article{
 title = {Disease detection in Okra plant and Grape vein using image processing},
 type = {article},
 year = {2023},
 keywords = {Convolution Neural Network (CNN),Deep Learning,Grape vein disease,Internet of Things (IoT),Pesticide Prediction,Tensorflow,okra plant disease},
 publisher = {Institute of Electrical and Electronics Engineers Inc.},
 id = {ca67c5a8-3430-35f3-b0ee-fb9b21937f77},
 created = {2023-10-27T08:18:15.566Z},
 accessed = {2023-10-27},
 file_attached = {true},
 profile_id = {f1f70cad-e32d-3de2-a3c0-be1736cb88be},
 group_id = {5ec9cc91-a5d6-3de5-82f3-3ef3d98a89c1},
 last_modified = {2023-11-22T17:08:42.107Z},
 read = {false},
 starred = {false},
 authored = {false},
 confirmed = {false},
 hidden = {false},
 folder_uuids = {4dd63b9a-2e78-4e16-b45d-b692d8e0c5c3,bd3c6f2e-3514-47cf-bc42-12db8b9abe45},
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 abstract = {The control of early-stage disease in plants is an essential factor in agriculture. Identification of disease in plants at an early stage helps farmers to reduce the usage of pesticides and avoid economic losses. This also in turn helps in promoting high quality yield production. Convolutional neural networks (CNNs) are deep learning algorithms that is applied for high resolution image recognition. This study uses a deep convolution neural network algorithm to detect and classify plant diseases. RESNET50 network has been applied to improve the effectiveness. Tensor Flow algorithm is utilized for coding the CNN algorithm and for accurate classification of the disease in grape and okra plant leaf. The dataset employed comprises of 6 classes and includes 2500 images. Simulation results for the developed model had achieved an accuracy of 95.1% in training and 91.2% in validation class tests.},
 bibtype = {article},
 author = {Kavitha, R. and Harini, S. Sree and Akshatha, K.},
 doi = {10.1109/ICAECA56562.2023.10200150},
 journal = {2nd International Conference on Advancements in Electrical, Electronics, Communication, Computing and Automation, ICAECA 2023}
}

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