An Improved Deep Learning Model for Plant Disease Detection
Anjanadevi B1, Charmila I2, Akhil NS3, Anusha R4
1Anjanadevi B, Asst Professor, Information Technology,MVGR College of Engg, Vizianagaram, India.
2Charmila I, btech student, MVGR, AP, India.
3Akhil NS,btechstudent MVGR, AP, India.
4Anusha R,btech student,MVGR, AP, India.
Manuscript received on February 28, 2020. | Revised Manuscript received on March 22, 2020. | Manuscript published on March 30, 2020. | PP: 5389-5392 | Volume-8 Issue-6, March 2020. | Retrieval Number: F1110038620/2020©BEIESP | DOI: 10.35940/ijrte.F1110.038620
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: In current era, Deep Convolution Neural Networks (DCNNs) are desperately improved localization, identification and detection of objects. Recent days, Big data is evolved which leads huge data generation through modern tools like surveillance video cameras. In this paper, we have focused on plant data images in agricultural field. Agriculture is one of major living source in India. To increase the yield by preventing diseases and detection of diseases place major role in agriculture domain. By using Improved and customized DCNN model (improved-detect), We trained plantdoc and plant village datasets. Mainly we used Tomato, Corn and potato plant for model training and testing. we have experimented on plant image data set-tomato leaves both healthy and diseased ones. Experimental results are compared with state of the architectures like Mobile Net, Dark Net-19, ResNet-101and proposed model out PERFORMS in location and detection of plant diseases. obtains best results in computation and accuracy. In the below results sections, we have presented the results with suitable models.
Keywords: Deep Learning, Single shot detection, Residual blocks, Improved-yolo, object localization, object detection
Scope of the Article: Deep Learning.