TY  - CONF
AV  - none
KW  - Deep learning; Food supply; Fruits; Plants (botany); Support vector machines; Viruses
KW  -  Accuracy; Agricultural yields; Convolutional neural network; Disease detection; Diseased and healthy leaf; Food consumption; Image-based; Plant disease; Population expansion; Resnet
KW  -  Convolutional neural networks
ID  - scholars18926
TI  - Image-Based Plant Disease Detection Using Deep Learning
N2  - Due to the nation's rapid population expansion and rising food consumption, agriculture is essential to India's economy and people. Consequently, agricultural yields need to rise. Crops produce so little as a result of diseases brought on by bacteria, viruses, and fungi. Adopt techniques for identifying plant diseases to stop it. To diagnose diseases since they lay a high emphasis on the data themselves and provide significance to the results of certain tasks using algorithms. Here, a method for employing CNN to find plant diseases has been proposed. To ascertain the size of the sick area and the time complexity, simulation studies and analyses are conducted on sample photos. It is accomplished using image processing methods. There are 32 different classes, including three courses focused on plants (apple, potato, and tomato), have been fed to the model. The total number of sick plant leaves is 24, including those that are impacted by diseases like Apple Rust and others. Different performance matrices are built and jointly trained using the ResNet architecture to categorize the images of sick and healthy leaves for the same produced datasets of leaves. When compared to other models like the Random Forest and SVM, the model's output exhibits superior accuracy, which is 99.2. © 2023 IEEE.
N1  - cited By 0; Conference of 2023 Intelligent Computing and Control for Engineering and Business Systems, ICCEBS 2023 ; Conference Date: 14 December 2023 Through 15 December 2023; Conference Code:198193
SN  - 9798350394580
PB  - Institute of Electrical and Electronics Engineers Inc.
Y1  - 2023///
UR  - https://www.scopus.com/inward/record.uri?eid=2-s2.0-85189136285&doi=10.1109%2fICCEBS58601.2023.10449229&partnerID=40&md5=afafb6aedf16a0d269ed6f5289bad62e
A1  - Gayathri, S.
A1  - Pradeep, S.
A1  - Aziz, A.A.
ER  -