Crops Disease Diagnosing Using Image-Based Deep Learning Mechanism

Park, Hyeon; Eun, Jee-Sook; Kim, Se-Han

doi:10.1109/coconet.2018.8476914

Cited by 29 publications

(20 citation statements)

References 3 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…However, distortion removal could improve image quality making it easy for further processing. While analyzing the colour space conversion (CSC) techniques like hue, saturation, and value HSV, it was noted that the CSC has a resembles that of a human sensing property [12]. A similar technique to HSV is HIS, where I stand for intensity [13].…”

Section: Literature Reviewmentioning

confidence: 99%

Machine Vision Based Plant Disease Classification Through Leaf Imagining

Rehman¹,

Anwar²,

Tufail³

2020

ISI

View full text Add to dashboard Cite

Plants disease identification plays a major role in agriculture yield prevention. Traditionally, manual plant surface examination is conducted which is time-consuming and relatively less efficient. Therefore, this study incorporates machine vision-based techniques, for plant disease identification (i.e. healthy leaf, Alternaria Alternate, and bacterial blight). The developed method employs a dataset comprised of more than 10,000 data points. Initially, Image processing is performed followed by image pre-processing techniques for noise removal. Subsequently, image segmentation is performed for the region of interest (ROI). A multiclass classifier is introduced for plant disease analysis, which results in a percentage of disease spreading in healthy leaf. The results were compared with other methods and it is evident that the developed method has shown 95% accuracy in plant disease identification.

show abstract

Section: Literature Reviewmentioning

confidence: 99%

Machine Vision Based Plant Disease Classification Through Leaf Imagining

Rehman¹,

Anwar²,

Tufail³

2020

ISI

View full text Add to dashboard Cite

show abstract

“…CNN stands for convolutional neural network, it is a class of deep learning which aims to analyze visual imagery. In CNN, the convolution is done between kernel filters and the input matrix by the means of shifting; hence, it is also known as shift invariant or space invariant artificial neural networks (SIANN) [8,9]. This convolution process brought feature maps which contain important and key points from the input and pooling operation are done on it to collect the important characteristics.…”

Section: Proposed Methodologiesmentioning

confidence: 99%

Automated Visual Inspecting System for Fruit Quality Estimation Using Deep Learning

Mohapatra

Das

Mohanty

et al. 2021

Lecture Notes in Electrical Engineering

View full text Add to dashboard Cite

Automated visual inspection using deep learning is widely used in recent years. In the field of agriculture, deep learning can be deployed to reduce effective man power, best time utilization, and supreme classification with improved accuracy. In agriculture, DL can be imported in many applications like soil identification, disease classification, fruit grading, and many more. Fruit quality classification is an essential part in farming as it implies to the return directly. Hence, an automated system is much needed to improve the classification of fruits with high accuracy and less time. In this paper, three different CNN models are proposed, namely simple CNN, ResNet50, and VGG19 for the said purpose. A comparison between CNN and two other forms of CNN (ResNet50 and VGG19) is presented in terms of performance. This work has achieved the optimum result with grading accuracy over 94% using VGG19 model.

show abstract

“…Through deep learning methodologies, leaf images are classified as healthy and affected [17]. A method to dynamically analyze the images of the disease is proposed in [18]. The output is sent to the farmer, and the feedback is reflected in the model.…”

Section: Related Work In the Literaturementioning

confidence: 99%

Image-Based Crop Leaf Disease Identification Using Convolution Encoder Networks

Bharathi¹,

Sonai²

2022

Artificial Intelligence

View full text Add to dashboard Cite

Nowadays, agriculture plays a major role in the progress of our nation’s economy. However, the advent of various crop-related infections has a negative impact on agriculture productivity. Crop leaf disease identification plays a critical role in addressing this issue and educating farmers on how to prevent the spread of diseases in crops. Researchers have already used methodologies such as decision trees, random forests, deep neural networks, and support vector machines. In this chapter, we proposed a hybrid method using a combination of convolutional neural networks and an autoencoder for detecting crop leaf diseases. With the help of convolutional encoder networks, this chapter presents a unique methodology for detecting crop leaf infections. Using PlantVillage dataset, the model is trained to recognize crop infections based on leaf images and achieves an accuracy of 99.82%. When compared with existing work, this chapter achieves better results with a suitable selection of hyper tuning parameters of convolution neural networks.

show abstract

Crops Disease Diagnosing Using Image-Based Deep Learning Mechanism

Cited by 29 publications

References 3 publications

Machine Vision Based Plant Disease Classification Through Leaf Imagining

Machine Vision Based Plant Disease Classification Through Leaf Imagining

Automated Visual Inspecting System for Fruit Quality Estimation Using Deep Learning

Image-Based Crop Leaf Disease Identification Using Convolution Encoder Networks

Contact Info

Product

Resources

About