Performance Evaluation of VegeCare Tool for Potato Disease Classification

Ruedeeniraman, Natwadee; Ikeda, Makoto; Barolli, Leonard

doi:10.1007/978-3-030-57811-4_47

Cited by 6 publications

(4 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…1. The proposed methodology in this paper includes the following four main steps: data acquisition, data pre-processing, data augmentation, and image classification [22]. Early Blight: Early blight is a form of plant epidemic brought on by the bacterium Alternaria solani.…”

Section: Methodsmentioning

confidence: 99%

A Deep Learning Approach to Classify the Potato Leaf Disease

Islam

Sikder

2022

JAMCS

View full text Add to dashboard Cite

Purpose: This paper aims to classify potato disease using convolutional neural network in different epochs to observe the best performance of the model. The best model will help the farmers to make different decisions to prevent the loss of potato production. Methodology: The paper implements a deep learning approach, the convolutional neural network, to explore potato disease classification. To accomplish the research objective, we collected 10000 images of potato leaves from different sources like google and raw data from potato fields. We collected a dataset of 2152 images from Kaggle and the other 7848 images from the above sources. The dataset belongs to a few classes. The classes are Potato Early Blight, Potato Late Blight, and Potato healthy leaf. The paper includes four main steps: data acquisition, data pre-processing, data augmentation, and image classification to find the output. Findings: This study found that the model performed better when we applied 40 epochs for the 10000 images dataset & we achieved 100% accuracy as we applied a total of 3 different epochs and achieved an accuracy of 99.97% and 99.98% for 30 and 50 epochs, respectively. Research Limitations: The study significantly contributed to the agriculture sector and farmers by providing suggestions to classify the Potato leaf Disease with the best output. Besides, researchers need more raw data to build the model for better output, and they also should be concerned regarding the system when working with large volumes of data as it takes longer to run the code. Originality/Value: This research paper contained high volume of the dataset, which is 10000 images of potato leaves. We collected a dataset of 2152 images from Kaggle and the rest, 7848 images from different sources like google, and raw data from potato filed. We showed different epochs to check the best performance and achieved 100% accuracy when 40 epochs were applied.

show abstract

Section: Methodsmentioning

confidence: 99%

A Deep Learning Approach to Classify the Potato Leaf Disease

Islam

Sikder

2022

JAMCS

View full text Add to dashboard Cite

show abstract

“…Nonetheless, this study, as well as [13][14][15], requires high resolution images and/or advanced features in the technological infrastructure, incompatible with the characteristics of Cuban mobiles, which are mostly on the way to obsolescence and unable to take even medium quality pictures. In [16], a mobile application called VegeCare was devised for the diagnosis of diseases in potatoes, yielding a 96% accuracy. However, it is a proprietary software, difficult to access for the Cuban community.…”

Section: Of 10mentioning

confidence: 99%

A Mobile App for Detecting Pests in Potato Crops

Pineda Medina,

Miranda Cabrera,

De la Cruz

et al. 2023

Preprint

View full text Add to dashboard Cite

Artificial intelligence techniques are now widely used in various agricultural applications, including the detection of devastating diseases such as late blight (Phytophthora infestans) and early blight (Alternaria solani) affecting potato (Solanum Tuberorsum L.) crops. In this paper, we present a mobile application for potato crop pest detection based on deep neural networks. The images were taken from the PlantVillage dataset with a batch of 1000 images for each of the three identified classes. An exploratory analysis of the architectures used for early and late blight diagnosis in potatoes was performed, achieving an accuracy of 98.76%, with MobileNetv2. Based on the results obtained, an offline mobile application was implemented, supported on devices with Android 4.1 or later, also featuring an information section on the 27 pests affecting potato crops and a gallery of symptoms. For future work, segmentation techniques will be used to highlight the damaged region in the potato leaf by evaluating its extent and possibly identifying different types of pests affecting the same plant.

show abstract

“…Nonetheless, this study, as well as [14][15][16], requires high-resolution images and/or advanced features in the technological infrastructure, incompatible with the characteristics of Cuban mobiles, which are mostly on the way to obsolescence and unable to take even medium-quality pictures. In [17], a mobile application called VegeCare was devised for the diagnosis of diseases in potatoes, yielding 96% accuracy. However, it is a proprietary software, difficult to access for the Cuban community.…”

Section: Introductionmentioning

confidence: 99%

A Mobile App for Detecting Potato Crop Diseases

Pineda Medina,

Miranda Cabrera,

de la Cruz

et al. 2024

J. Imaging

View full text Add to dashboard Cite

Artificial intelligence techniques are now widely used in various agricultural applications, including the detection of devastating diseases such as late blight (Phytophthora infestans) and early blight (Alternaria solani) affecting potato (Solanum tuberorsum L.) crops. In this paper, we present a mobile application for detecting potato crop diseases based on deep neural networks. The images were taken from the PlantVillage dataset with a batch of 1000 images for each of the three identified classes (healthy, early blight-diseased, late blight-diseased). An exploratory analysis of the architectures used for early and late blight diagnosis in potatoes was performed, achieving an accuracy of 98.7%, with MobileNetv2. Based on the results obtained, an offline mobile application was developed, supported on devices with Android 4.1 or later, also featuring an information section on the 27 diseases affecting potato crops and a gallery of symptoms. For future work, segmentation techniques will be used to highlight the damaged region in the potato leaf by evaluating its extent and possibly identifying different types of diseases affecting the same plant.

show abstract

Performance Evaluation of VegeCare Tool for Potato Disease Classification

Cited by 6 publications

References 17 publications

A Deep Learning Approach to Classify the Potato Leaf Disease

A Deep Learning Approach to Classify the Potato Leaf Disease

A Mobile App for Detecting Pests in Potato Crops

A Mobile App for Detecting Potato Crop Diseases

Contact Info

Product

Resources

About