Potato Leaf Diseases Detection Using Deep Learning

Tiwari, Divyansh; Ashish, Mritunjay; Gangwar, Nitish; Sharma, Abhishek; Patel, Suhanshu; Bhardwaj, Suyash

doi:10.1109/iciccs48265.2020.9121067

Cited by 156 publications

(52 citation statements)

References 15 publications

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“…Pretrained weights of the MS COCO dataset have been used for the initial training of the model as attempts to train from scratch did not yield significant detection even after 70 th epoch for all of the color space datasets, probably due to the small dataset. On the other hand, the application of transfer learning towards classification of potato leaf disease was shown in [ 39 , 40 ]. To optimize the network weights, the stochastic gradient descent optimizer with momentum fixed at 0.9 was used.…”

Section: Methodsmentioning

confidence: 99%

Enhanced Field-Based Detection of Potato Blight in Complex Backgrounds Using Deep Learning

et al. 2021

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Rapid and automated identification of blight disease in potato will help farmers to apply timely remedies to protect their produce. Manual detection of blight disease can be cumbersome and may require trained experts. To overcome these issues, we present an automated system using the Mask Region-based convolutional neural network (Mask R-CNN) architecture, with residual network as the backbone network for detecting blight disease patches on potato leaves in field conditions. The approach uses transfer learning, which can generate good results even with small datasets. The model was trained on a dataset of 1423 images of potato leaves obtained from fields in different geographical locations and at different times of the day. The images were manually annotated to create over 6200 labeled patches covering diseased and healthy portions of the leaf. The Mask R-CNN model was able to correctly differentiate between the diseased patch on the potato leaf and the similar-looking background soil patches, which can confound the outcome of binary classification. To improve the detection performance, the original RGB dataset was then converted to HSL, HSV, LAB, XYZ, and YCrCb color spaces. A separate model was created for each color space and tested on 417 field-based test images. This yielded 81.4% mean average precision on the LAB model and 56.9% mean average recall on the HSL model, slightly outperforming the original RGB color space model. Manual analysis of the detection performance indicates an overall precision of 98% on leaf images in a field environment containing complex backgrounds.

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Section: Methodsmentioning

confidence: 99%

Enhanced Field-Based Detection of Potato Blight in Complex Backgrounds Using Deep Learning

et al. 2021

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“…In this work, we try to design an inception-v3 based transfer learning model for potato leave disease detection to build high performance detection for small data using pre trained on large datasets. In plant disease identification experiment, convolutional neural network is an appropriate learning technique in deep learning approach in which it can accurately recognize plant diseases (15) . The main steps in this work are image acquisition, image preprocessing, segmentation, feature extraction and identification of potato diseases as depicted in the following Figure 1.…”

Section: Methodsmentioning

confidence: 99%

Potato Plant Leaf Diseases Identification Using Transfer Learning

Gardie¹,

Asemie²,

Azezew³

et al. 2022

IJST

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Background/Objectives: Agriculture is a major food source for Ethiopian population. Plant diseases contribute a great production loss, which can be addressed with continuous monitoring. Early plant disease identification using computer vision and Artificial Intelligence (AI) helps the farmers to take preventive course of action to increase production quality. Manual plant disease identification is strenuous and error-prone. Methods: In this study, we present a convolutional neural network architecture inception-v3 model to detect potato leaf diseases using a deep learning-based transfer learning technique. We used separable convolution in the inception block that can minimize the number of parameters by an outsized margin and to utilize resource efficiently. The inception-V3 model have a higher training accuracy and needs less training time than the main CNN architecture, as the used parameters are fewer. Findings: In this study, there is an improvement on the little noisy on sample images which leads to misidentification of diseases. In our experiment, we have used an RGB color channel image dataset to train model, which yields an overall accuracy performance of 98.7% on the heldout test set. Novelty: In order to identify potato leave diseases, we conducted transfer learning for high performance classification with pixel-wise operation to enhance the number of leaf images. A model based on inception-v3 transfer learning approach is presented in this study for disease identification of potato leave images, thus provide an effective computer-aided recognition model for potato disease classification in the absence of large data.

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“…When existing studies in the literature were analyzed, plant diseases were determined by using attributes such as color, texture, shape and shape of plant lesions (Prashar et al, 2017;Singh & Misra, 2017;Tiwari et al, 2020).…”

Section: Related Workmentioning

confidence: 99%

Öneri̇len Konvolüsyon Si̇ni̇r Aği Yaklaşimi Kullanarak Elma Yapraği Hastaliklarinin Siniflandirilmasi

ÇETİNER

2021

Mühendislik Bilimleri Ve Tasarım Dergisi

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It is difficult to constantly control apple trees in farmland. In case of a disease on tree leaves, the risk of disease transmission to other leaves is high. It is necessary to prevent further deterioration of the plant by performing automatic detection of the disease in the early period. If the disease detection is delayed, the planned production cannot be realized. It is too late if diseases are detected by a farmer or agronomist. In addition, as the agricultural lands grow, the number of experts needed increases accordingly. For these reasons, leaf images of apple trees are grouped into 4 different classes: apple peel, leaf rust, healthy apple and multiple disease states. In the proposed method, noise removal in the images, detection of the relevant area and histogram equalization on the YUV color space are performed. Due to the unbalanced class distribution in the data set used, data augmentation was applied for the minority classes with the SMOTE method. Afterwards, features are extracted using pre-trained network models DenseNet121, DenseNet201, InceptionResNetV2, InceptionV3, ResNet50V2. Extracted features were classified with a CNN-based method developed with an accuracy of 99%.

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Potato Leaf Diseases Detection Using Deep Learning

Cited by 156 publications

References 15 publications

Enhanced Field-Based Detection of Potato Blight in Complex Backgrounds Using Deep Learning

Enhanced Field-Based Detection of Potato Blight in Complex Backgrounds Using Deep Learning

Potato Plant Leaf Diseases Identification Using Transfer Learning

Öneri̇len Konvolüsyon Si̇ni̇r Aği Yaklaşimi Kullanarak Elma Yapraği Hastaliklarinin Siniflandirilmasi

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