A deep learning based approach for the detection of diseases in pepper and potato leaves

Abstract

The present study proposes a Faster R-CNN Object Detection Approach with GoogLeNet Classifier (Faster R-CNN-GC) using image stitching, Faster R-CNN and GoogLeNet to detect pepper and potato leaves as well as leaf diseases in them. It is widely known that for a successful object detection performance, Faster R-CNN requires performing image labelling on a very high number of data, which will later train Faster R-CNN. However, this process is often very time-consuming. The present study mainly aims to shorten this process by designing an object detection approach which benefits from Faster R-CNN and GoogLeNet architecture. Firstly, Faster R-CNN and GoogLeNet were trained. Later, for the testing process, some of two-piece images were combined using an image stitching approach. Finally, using Faster R-CNN and GoogLeNet, pepper and potato leaves are detected and diseases are written on them. In addition, the proposed system was compared with Faster R-CNN Object Detection Approach with AlexNet Classifier (Faster R-CNN-AC), Faster R-CNN Object Detection Approach with SequezeNet Classifier (Faster R-CNN-SC) and Faster R-CNN. The findings of the experimental studies demonstrated that Faster R-CNN-GC displayed a higher object detection performance compared to other approaches.Biber ve patates yapraklarındaki hastalıkların saptanması için derin öğrenme temelli bir yaklaşım

AI-based object detection latest trends in remote sensing, multimedia and agriculture applications

“…There is still 5×5 convolution kernels to increase the network operation; including more complex hyperparameters, each transformation needs to specify the size and number of convolution kernels (Ding et al, 2019;Eser, 2021;Diwan et al, 2022) ResNet…”

Applications of Computer Vision on Automatic Potato Plant Disease Detection: A Systematic Literature Review

“…To be more specific, some of the algorithms used by previous authors are k-NN, CNN, SVM, and decision trees. Nonetheless, the potential advantage of CNN and R-CNN is not fully discovered [ 92 ].…”

“…This technique simplified image processing on those leaf images taken from a wider angle. To sum up, the authors targeted using GoogLeNet for improving the performance of Faster R-CNN [ 92 ].…”

“…Sert [ 92 ] used the PlantVillage image dataset for training together with other remaining leaf images collected from fields of pepper and potato. In general, a total of 544 images were used from the open repository and locally captured images.…”

MFCC Yöntemi ve Önerilen Derin Model ile Çevresel Seslerin Otomatik Olarak Sınıflandırılması

“…Doğruluk, Hassaslık, Duyarlılık ve F1-ölçümü bu çalışmada önerilen modelin performansını ölçmek için kullanılan değerlendirme ölçütleridir. Bu performans ölçme metriklerinin çoğu karışıklık matrisi kullanılarak hesaplanmaktadır [20].…”