Crop Diseases and Pests Detection Using Convolutional Neural Network

Patel, Pruthvi P.; Vaghela, Dineshkumar B.

doi:10.1109/icecct.2019.8869510

Cited by 33 publications

(12 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…After that, they have summarized the applications of ELM on classification, regression, function approximation, pattern recognition, forecasting and diagnosis, and so on. At last, they have discussed several open issues of ELM, which may be worthy of exploring in the future [2] in this paper, we propose a classification method of periodontal disease based on CNN. The data to used were the actual periodontal images and non-periodontal images.…”

Section: IImentioning

confidence: 99%

Application for Solution to Identify and Solve Disease in Plants/Crops

Bardekar¹,

Lawange²,

Lavange³

et al. 2023

IJRASET

View full text Add to dashboard Cite

Despite The Indian economy depends heightening on agribusiness proficiency a divide is at stake when a plant is struck with a sickness that causes a essential hardship in era money related incidents and a reducing inside the quality and sum of agrarian things it is significant to recognize plant contaminations in orchestrate to expect the mishap of provincial abandon and sum plant diseases are challenging to screen physically since it requires a mind blowing deal of work skill on plant contaminations and expect planning time this will be fulfilled by utilizing picture planning strategies for plant ailment revelation alter illness figure system can be made utilizing machine learning strategies to absolutely anticipate the occasion of contaminations in crops the system can be arranged to dismember distinctive characteristic and weather-related factors that can impact the prosperity of crops.

show abstract

Section: IImentioning

confidence: 99%

Application for Solution to Identify and Solve Disease in Plants/Crops

Bardekar¹,

Lawange²,

Lavange³

et al. 2023

IJRASET

View full text Add to dashboard Cite

show abstract

“…Inputs are fed to these neurons, processed there and activate the corresponding output. Convolutional Neural Network (CNN) of deep learning consist of multi-layered neural network [18]. Basic implementation of deep Learning is illustrated in fig.…”

Section: Deep Learningmentioning

confidence: 99%

“…First, dataset is gathered which undergoes division into training and testing datasets, then train a model using deep learning model architecture such as AlexNet, ResNet, GoogleNet, VGGNet, etc. [18] and their training and validation plots are generated to understand the efficiency of the model. After that performance metrics and Visualization techniques are used for the classification and detection of images.…”

Section: Deep Learningmentioning

confidence: 99%

Design of deep recursive CNN model for detecting and classifying peston plant

Kalambe¹,

Awachat²,

Raipure³

2022

ijhs

View full text Add to dashboard Cite

Automatic pest on plant detection in early stage is very essential for food quality control in the agriculture industry. However, the visual method to identify pest on every plant by human is a cumbersome process and cannot be well suited in the agriculture field, because it is time consuming, less accurate and labor intensive. Pest on Plant and plant leaf disease are the major factors responsible for reducing the quality and quantity of food production. Detection at the earlier stage of pest growth and its killing would result in reducing its effect on plant and enhance the quality of food production. Various existing ways have been used to identify and classify pest on plant, but issues have not been resolved, and there is still a scope for improvement. This paper proposes a Deep Recursive Convolutional Neural Networks (DR-CNN) to improve the average running time and achieve high accuracy. DR-CNN model is integrating the convolution, ReLU and Max pooling Layer into single unit and call recursively.

show abstract

“…A salient feature of CNN is that it can perform feature extraction directly from the images without using any segmentation methods. Recently, more studies involved in applying deep learning and machine learning techniques in crops pest and disease identification 37‐43 …”

Section: Background Studymentioning

confidence: 99%

Integrated analysis of machine learning and deep learning in chili pest and disease identification

2020

View full text Add to dashboard Cite

BACKGROUND Chili is one of the most important and high‐value vegetable crops worldwide. However, pest and disease infections are among the main limiting factors in chili cultivation. These diseases cannot be eradicated but can be handled and monitored to mitigate the damage. Hence, the use of an automated identification system based on images will promote quick identification of chili disease. The features extracted from the images are of utmost importance to develop such an accurate identification system. RESULTS In this research, chili pest and disease features extracted using the traditional approach were compared with features extracted using a deep‐learning‐based approach. A total of 974 chili leaf images were collected, which consisted of five types of diseases, two types of pest infestations, and a healthy type. Six traditional feature‐based approaches and six deep‐learning feature‐based approaches were used to extract significant pests and disease features from the chili leaf images. The extracted features were fed into three machine learning classifiers, namely a support vector machine (SVM), a random forest (RF), and an artificial neural network (ANN) for the identification task. The results showed that deep learning feature‐based approaches performed better than the traditional feature‐based approaches. The best accuracy of 92.10% was obtained with the SVM classifier. CONCLUSION A deep‐learning feature‐based approach could capture the details and characteristics between different types of chili pests and diseases even though they possessed similar visual patterns and symptoms. © 2020 Society of Chemical Industry

show abstract

Crop Diseases and Pests Detection Using Convolutional Neural Network

Cited by 33 publications

References 19 publications

Application for Solution to Identify and Solve Disease in Plants/Crops

Application for Solution to Identify and Solve Disease in Plants/Crops

Design of deep recursive CNN model for detecting and classifying peston plant

Integrated analysis of machine learning and deep learning in chili pest and disease identification

Contact Info

Product

Resources

About