Root Rot Lentil and Healthy Lentil Detection Using Image Processing

• Autorzy: Tasnia, Noshin , Halder, Moon , Ara, Jiasmin , Karim, Md. Rejaul , Mahmud, Shakik
• Języki publikacji: EN

Abstrakty

EN

The hardest thing to do in agriculture is to figure out which leaves are healthy and which ones are damaged. Bangladesh makes 80\\% of its money from farming. Most farmers cannot read or write. They didn't know how much fertilizer to put on a lentil with root rot or a healthy lentil. They sometimes spray medicine on the plants, which is terrible for them. As a result, agriculture has become much less productive. In this paper, a picture-segmenting algorithm is given that can automatically find and classify plant leaf diseases. Also included are surveys of different ways to classify diseases that can be used to find plant leaf diseases. The Convolution Neural Network model is used to segment images, an essential part of finding plant leaf diseases. Every country's growth is based on its agricultural production. To keep agricultural production at a certain level and keep growing sustainably, scientists need to study how to find and treat diseases. Standard methods in the literature for classifying leaf images involve extracting attributes and training classifier models, which makes them less accurate. The technique suggested gets rid of any unnecessary data from the image collection. Using the mixture model for region growth, we first find the area of interest based on the colors of the leaves in the image. After extracting the features, a deep convolution neural network model is used to classify the leaf photos. A convolutional neural network model can be used with the deep learning model to find different patterns in color photos. Examining the execution strategy of the proposed model using an unauthorized dataset. According to the results of the simulating replica, the suggested model outperforms the well-known current methods in the field, with mean classification accuracy and area under the characteristics curve of 95.35\\% and 94.7\\%, respectively.

Słowa kluczowe

EN

image processing; Root Rot lentil; healthy lentil; CNN; tensorflow; plant disease detection; classification

• Czasopismo: Annals of Computer Science and Information Systems
• Rocznik: 2022
• Tom: Vol. 33
• Strony: 227--234
• Opis fizyczny: Bibliogr. 18 poz., rys., tab., wykr.

Twórcy

autor

Tasnia, Noshin

• Applied statistics and Data Science Jahangirnagar University Dhaka, Bangladesh,

autor

Halder, Moon

• American International University–Bangladesh Dhaka, Bangladesh,

autor

Ara, Jiasmin

• Biology Department Cantonment English School and College Chattogram, Bangladesh,

autor

Karim, Md. Rejaul

• Farm Machinery and Postharvest Process Engineering Division Bangladesh Agricultural Research Institute Gazipur, Bangladesh,

autor

Mahmud, Shakik

• Computer Science and Engineering United International University Dhaka, Bangladesh,

Bibliografia

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• [16] N. C. Minh, T. H. Dao, D. N. Tran, N. Q. Huy, N. T. Thu and D. T. Tran, Evaluation of Smartphone and Smartwatch Accelerometer Data in Activity Classification, 8th NAFOSTED Conference on Information and Computer Science (NICS), 33-38, 2021
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Uwagi

PL

Opracowanie rekordu ze środków MNiSW, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2024).

Identyfikatory

DOI

10.15439/2022R08