scholarly journals Hybrid Multi-Core Algorithm Based Image Segmentation for Plant Disease Identification using Mobile Application

2017 ◽  
Vol 3 (2) ◽  
pp. 17-26
Author(s):  
Vignesh Janarthanan ◽  
◽  
Venkata Reddy Medikonda ◽  
Keyword(s):  
Image Segmentation ◽  
Mobile Application ◽  
Plant Disease ◽  
Disease Identification

scholarly journals Disease Identification in Maize Plant Leaf

2020 ◽  
Vol 9 (4) ◽  
pp. 1239-1244
Keyword(s):  
Neural Network ◽  
Image Segmentation ◽  
Plant Disease ◽  
Early Stage ◽  
Plant Leaves ◽  
Disease Identification ◽  
Neural Network Algorithm ◽  
The One ◽  
Automatic Technique

Agricultural productivity is that issue there on Indian Economy extremely depends. this is often the one altogether the reasons that malady detection in plants plays a really important role within the agriculture field, as having the malady in plants are quite natural. If correct care isn't taken during this space then it causes serious effects on plants and since of that various product quality, amount or productivity is affected. Detection of disease through some automatic technique is useful because it reduces an oversized work of watching in huge farms of crops, and at terribly early stage itself detects the symptoms of diseases means after they appear on plant leaves. This paper presents a neural network algorithm for image segmentation technique used for automatic detection still because the classification of plants and survey on completely different diseases classification techniques which will be used for plant disease detection. Image segmentation, that is a really important facet for malady detection in disease is completed by victimization genetic algorithm.

scholarly journals LeLePhid: An Image Dataset for Aphid Detection and Infestation Severity on Lemon Leaves

Data ◽  
2021 ◽  
Vol 6 (5) ◽  
pp. 51
Author(s):  
Jorge Parraga-Alava ◽  
Roberth Alcivar-Cevallos ◽  
Jéssica Morales Carrillo ◽  
Magdalena Castro ◽  
Shabely Avellán ◽  
...  
Keyword(s):  
Image Segmentation ◽  
Plant Disease ◽  
Automatic Identification ◽  
Health State ◽  
Classification Problems ◽  
Affected Area ◽  
White Spots ◽  
The World ◽  
Image Dataset ◽  
Plant Sap

Aphids are small insects that feed on plant sap, and they belong to a superfamily called Aphoidea. They are among the major pests causing damage to citrus crops in most parts of the world. Precise and automatic identification of aphids is needed to understand citrus pest dynamics and management. This article presents a dataset that contains 665 healthy and unhealthy lemon leaf images. The latter are leaves with the presence of aphids, and visible white spots characterize them. Moreover, each image includes a set of annotations that identify the leaf, its health state, and the infestation severity according to the percentage of the affected area on it. Images were collected manually in real-world conditions in a lemon plant field in Junín, Manabí, Ecuador, during the winter, by using a smartphone camera. The dataset is called LeLePhid: lemon (Le) leaf (Le) image dataset for aphid (Phid) detection and infestation severity. The data can facilitate evaluating models for image segmentation, detection, and classification problems related to plant disease recognition.

scholarly journals Leaf Image-Based Plant Disease Identification Using Color and Texture Features

2021 ◽  
Author(s):  
Nisar Ahmad ◽  
Hafiz Muhammad Shahzad Asif ◽  
Gulshan Saleem ◽  
Muhammad Usman Younus ◽  
Sadia Anwar ◽  
...  
Keyword(s):  
Plant Disease ◽  
Texture Features ◽  
Disease Identification ◽  
Leaf Image ◽  
Color And Texture Features

scholarly journals SISTEM IDENTIFKASI PENYAKIT TANAMAN CABAI MENGGUNAKAN METODE SIMPLE ADDITIVE WEIGHTING (SAW) BERBASIS ANDROID

2021 ◽  
Vol 9 (1) ◽  
Author(s):  
Hendire S B ◽  
Machudor Yusman
Keyword(s):  
Expert System ◽  
National Economy ◽  
Plant Disease ◽  
Low Level ◽  
Disease Identification ◽  
Simple Additive Weighting ◽  
High Level

Chili is an important commodity in Indoneisa’s economy. The fluctuating price causes chili to contribute to inflation for national economy. Chili’s price could increase because of demand in high level and production of chili in low level. The emergence of disease causes production of chili decreas. To solve the problem in chili disease, the disease needs to be diagnose as soon as possible. To diagnose the disease as soon as possible there is a need of chili expert system using android. Chili’s disease will be indentificated by inputting the symptoms which are shown by the plan. Expert system of chili plant disease identification design is based Android. Android Mobile is used as a device to insert the symptoms which are shown by the plant. The system will manage the symptoms that have been selected and show the diagnoses of the disease and how to control the disease. This system was designed using the Simple Additive Weighting (SAW) method and tested using Blackbox method. Testing using Blackbox method to show the system is able to identify the diseases of chili plant

Optimized fuzzy neural network for Jatropha Curcas plant disease identification

2017 ◽  
Author(s):  
Diny Melsye Nurul Fajri ◽  
Triando Hamonangan Saragih ◽  
Andi Hamdianah ◽  
Wayan Firdaus Mahmudy ◽  
Yusuf Priyo Anggodo
Keyword(s):  
Neural Network ◽  
Jatropha Curcas ◽  
Fuzzy Neural Network ◽  
Plant Disease ◽  
Disease Identification ◽  
Fuzzy Neural

Deep Learning-Based Mobile Application for Plant Disease Diagnosis

2019 ◽  
pp. 242-271 ◽  
Author(s):  
Shradha Verma ◽  
Anuradha Chug ◽  
Amit Prakash Singh ◽  
Shubham Sharma ◽  
Puranjay Rajvanshi
Keyword(s):  
Machine Learning ◽  
Deep Learning ◽  
Mobile Application ◽  
Plant Disease ◽  
Prediction Models ◽  
Disease Diagnosis ◽  
Tomato Leaf ◽  
Plant Diseases ◽  
Learning Tools ◽  
Class Labels

With the increasing computational power, areas such as machine learning, image processing, deep learning, etc. have been extensively applied in agriculture. This chapter investigates the applications of the said areas and various prediction models in plant pathology for accurate classification, identification, and quantification of plant diseases. The authors aim to automate the plant disease identification process. To accomplish this objective, CNN has been utilized for image classification. Research shows that deep learning architectures outperform other machine learning tools significantly. To this effect, the authors have implemented and trained five CNN models, namely Inception ResNet v2, VGG16, VGG19, ResNet50, and Xception, on PlantVillage dataset for tomato leaf images. The authors analyzed 18,160 tomato leaf images spread across 10 class labels. After comparing their performance measures, ResNet50 proved to be the most accurate prediction tool. It was employed to create a mobile application to classify and identify tomato plant diseases successfully.

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