scholarly journals An Android Application for Plant Leaf Disease Detection using Convolution Neural Network

2019 ◽  
Vol 8 (12) ◽  
pp. 4133-4137
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Agricultural Sector ◽  
Agricultural Productivity ◽  
Agricultural Product ◽  
Android Application ◽  
Plant Leaf ◽  
Important Concern ◽  
Plant Area ◽  
Detection And Recognition

Plant leaf diseases and ruinous insects are an important concern in the agricultural sector. The agriculture is dependent on the agricultural productivity by the country, the better is the agricultural productivity, the better is the economy, and hence better is the GDP. The most common and useful way of boosting this economy for any country is the identification of these diseases in the plant and agricultural product that has been obtained. Developments in Deep Learning have facilitated researchers to improve the performance and in exacting systems for object detection and recognition. In this paper, we propose an image processing and Convolutional Neural Network based approach to detect the diseases affecting plants. Our goal is to develop an Android application with a suitable algorithm that will help automate the process of monitoring and detecting plant health. The proposed android application can effectively detect and identify various types of diseases with the ability to handle complex plant-area scenarios

scholarly journals Smart Farming: IoT Based Plant Leaf Disease Detection and Prediction using Deep Neural Network with Image Processing

2019 ◽  
Vol 8 (9) ◽  
pp. 3081-3083 ◽  
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Deep Neural Network ◽  
Human Life ◽  
Disease Detection ◽  
Plant Leaf ◽  
Leaf Disease ◽  
Agricultural Laborers ◽  
Smart Farming ◽  
Texture Size

Agriculture plays a major role in human life. Almost 60% of the population is involved directly or indirectly in some agriculture activity. But Nowadays, farmers have quit agriculture and shifted to other sectors due to less adoption of automation and other reasons like increase in the requirement of agricultural laborers. So, Farmers now largely depend on adoption of cognitive solutions with technological advancements to acquire the benefits. Image processing and Internet of Things jointly produces new dimensions in the field of smart precision farming. This proposed methodology aims to create an approach for plant leaf disease detection based on deep neural network. This approach combines IoT and image processing which runs preprocessing and feature extraction techniques by considering different features such as color, texture, size and performs classification using deep learning model that expands to help identification of plant leaf disease

scholarly journals A Study on Traditional and CNN Based Computer Vision Sensors for Detection and Recognition of Road Signs with Realization for ADAS

2021 ◽  
Author(s):  
Vinay M. Shivanna ◽  
Kuan-Chou Chen ◽  
Bo-Xun Wu ◽  
Jiun-In Guo
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Computer Vision ◽  
Digital Image Processing ◽  
Digital Image ◽  
Road Signs ◽  
Vision Sensors ◽  
Detection And Recognition

The aim of this chapter is to provide an overview of how road signs can be detected and recognized to aid the ADAS applications and thus enhance the safety employing digital image processing and neural network based methods. The chapter also provides a comparison of these methods.

Pattern Recognition Using Image Processing

2018 ◽  
pp. 277-282 ◽  
Author(s):  
Gayathri J ◽  
Ramya S
Keyword(s):  
Image Processing ◽  
Rural Areas ◽  
Agricultural Sector ◽  
Processing System ◽  
Disease Detection ◽  
Bacterial Diseases ◽  
Crop Disease ◽  
Paddy Cultivation ◽  
Paddy Crop ◽  
Detection And Recognition

Paddy cultivation plays an important role in agriculture. But the growth of crop is affected by various diseases. If detection of disease is not properly done at earlier stage, then it may result in decrease of paddy production. India is agriculture based country and it provides employment to peoples in rural areas.. The agricultural sector plays major role in development of our economy by providing employment for rural peoples. Paddy is the staple food of Indians and hence it is considered as nation’s important product. Crop management is followed to protect paddy plants from fungal and bacterial diseases. The main goal is to develop an image processing system to identify and classify the various diseases affecting the growth of paddy plants. The work is divided into two parts paddy crop disease detection and recognition of paddy crop diseases. Disease detection technique is used to detect the disease affected portion in the paddy plant. The techniques used to detect diseased portions of paddy crop are Boundary localization and Haar-like features methods and neural network is employed based on diseases classification.

scholarly journals Insect Detection in Rice Crop using Google Code Lab

2021 ◽  
Vol 12 (2) ◽  
Author(s):  
K. Sumathi, Et. al.
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Agricultural Sector ◽  
Quality Analysis ◽  
Leaf Quality ◽  
Insect Identification ◽  
The Neural Network ◽  
Physical Classification ◽  
Score Map ◽  
The Moment

Herb plants are essential in the medical field today and can help humans. Phyllanthus Elegans Wall is used in this study to analyse and categorize whether it is a safe or unhealthy leaf. At the moment, most insect identification methods rely on physical classification, making it difficult to automatically, quickly, and reliably identify in stored grains. The concept of this research is to ascertain the quality of leaves by combining technology with pesticide classification in the agricultural sector. Picture collection, image processing, and classification are the first steps in enhancing leaf quality analysis. The segmentation using HSV to input RGB image for the colour alteration structure is the most significant image processing method for this section. The colour and shape of a leaf disease image are used to analyse it. Insect detection in complex backgrounds is more versatile with the score map that is decision alternate highly interconnected layer, and our detection speed has upgraded. Finally, the taxonomy approach employs an algorithm that feeds directly that employs formation backwards techniques. The result shows a Many-layer Preceptor and Nonlinear Activation Feature comparison, as well as a percentage of accuracy contrast between MLP and RBF. MLP and RBF are neural network algorithms. Clearly, the Neural Network classifier has a better presentation and precision.  

scholarly journals Classification Techniques for Plant Disease Detection

2020 ◽  
Vol 8 (6) ◽  
pp. 5423-5430
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Convolutional Neural Network ◽  
Plant Disease ◽  
Agricultural Sector ◽  
Early Stage ◽  
Support Vector ◽  
Disease Detection ◽  
Neural Network Approach ◽  
Traditional Approaches

Production of crops with better quality is the necessary attribute for the economic growth of any country. The agricultural sector provides employment to many people and accounts for major portion of gross domestic product in many countries around the world. Therefore, for enhanced agricultural productivity the detection of diseases in plants at an early stage is quite significant. The traditional approaches for disease detection in plants required considerable amount of time, intense research, and constant monitoring of the farm. However, optimized solutions have been obtained over the past few years due to technological advances that have resulted in better yields for the farmers. Machine learning and image processing are used to detect the disease on the agricultural harvest. The image processing steps for plant disease identification include acquiring of images, pre-processing, segmentation and feature extraction. In this review paper, we focused mainly on the most utilized classification mechanisms in disease detection of plants such as Convolutional Neural Network, Support Vector Machine, KNearest Neighbor, and Artificial Neural Network. It has been observed from the analysis that Convolutional Neural Network approach provides better accuracy compared to the traditional approaches.

scholarly journals Plant Leaf Segmentation and Phenotypic Analysis Based on Fully Convolutional Neural Network

2021 ◽  
Vol 37 (5) ◽  
pp. 929-940
Author(s):  
Liying Cao ◽  
Hongda Li ◽  
Helong Yu ◽  
Guifen Chen ◽  
Heshu Wang
Keyword(s):  
Neural Network ◽  
Image Processing ◽  
Image Segmentation ◽  
Deep Learning ◽  
High Throughput ◽  
Loss Function ◽  
Small Sample ◽  
List Type ◽  
Plant Leaf ◽  
Segmentation Accuracy

HighlightsModify the U-Net segmentation network to reduce the loss of segmentation accuracy.Reducing the number of layers U-net network, modifying the loss function, and the increase in the output layer dropout.It can be well extracted after splitting blade morphological model and color feature.Abstract. From the perspective of computer vision, the shortcut to extract phenotypic information from a single crop in the field is image segmentation. Plant segmentation is affected by the background environment and illumination. Using deep learning technology to combine depth maps with multi-view images can achieve high-throughput image processing. This article proposes an improved U-Net segmentation network, based on small sample data enhancement, and reconstructs the U-Net model by optimizing the model framework, activation function and loss function. It is used to realize automatic segmentation of plant leaf images and extract relevant feature parameters. Experimental results show that the improved model can provide reliable segmentation results under different leaf sizes, different lighting conditions, different backgrounds, and different plant leaves. The pixel-by-pixel segmentation accuracy reaches 0.94. Compared with traditional methods, this network achieves robust and high-throughput image segmentation. This method is expected to provide key technical support and practical tools for top-view image processing, Unmanned Aerial Vehicle phenotype extraction, and phenotype field platforms. Keywords: Deep learning, Full convolution neural network, Image segmentation, Phenotype analysis, U-Net.

scholarly journals Implementation of Digital Signal Processing for Monitoring Plant Health

2019 ◽  
Vol 8 (12) ◽  
pp. 3480-3482
Keyword(s):  
Image Processing ◽  
Signal Processing ◽  
Digital Signal Processing ◽  
Agricultural Productivity ◽  
Digital Signal ◽  
Plant Health ◽  
Android Application ◽  
Processing Steps ◽  
Diagnosis Of Diseases

the diseases of plants is one of the major reason behind the reduction in the amount and quality of agricultural productivity. Great difficulties are encountered by farmers for the control and diagnosis of diseases of plants. Thus it becomes crucial to detect the diseases of plants during the initial stages for the suitable and timely action in order to avoid further losses. The approach of image processing is followed for detecting the diseases of cashew plants in this project. The image of leaf is uploaded on the system for the identification of cashew disease. A set of algorithms are used in a system for the identification of type of disease. The several processing steps are followed at the image given as input for the detection of disease and results are displayed to the user via android application.

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