scholarly journals Novel fusion of color balancing and superpixel based approach for detection of tomato plant diseases in natural complex environment

2020 ◽  
Author(s):  
Saiqa Khan ◽  
Meera Narvekar
Keyword(s):  
Tomato Plant ◽  
Plant Diseases ◽  
Complex Environment ◽  
Natural Complex

scholarly journals A Robust Deep-Learning-Based Detector for Real-Time Tomato Plant Diseases and Pests Recognition

Sensors ◽  
2017 ◽  
Vol 17 (9) ◽  
pp. 2022 ◽  
Author(s):  
Alvaro Fuentes ◽  
Sook Yoon ◽  
Sang Kim ◽  
Dong Park
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Tomato Plant ◽  
Plant Diseases

scholarly journals Detection of Tomato Plant Diseases Using Deep Convolutional Neural Network

2020 ◽  
pp. 464-465
Author(s):  
Vijayaganth V ◽  
Naveenkumar M ◽  
Mohan M
Keyword(s):  
Neural Network ◽  
Early Diagnosis ◽  
Convolutional Neural Network ◽  
Tomato Plant ◽  
Learning Rate ◽  
Deep Convolutional Neural Network ◽  
Plant Diseases ◽  
Batch Size ◽  
Diagnosis Of Diseases

The disease in tomato leaves affects the quality and quantity of the crops. To overcome this problem an early diagnosis of diseases will benefit the farmers. This work uses PlantVillage dataset of 9 tomato leaves and fed to AlexNet and VGG16. It focuses on accuracy of the model by using hyperparameters like batch size, learning rate and optimizer.

scholarly journals Leaf Disease Identification using Convolution Neural Network (CNN)

2020 ◽  
Vol 9 (9) ◽  
pp. 477-481
Keyword(s):  
Neural Network ◽  
Late Blight ◽  
Tomato Plant ◽  
Extraction Methods ◽  
Plant Diseases ◽  
Viral Diseases ◽  
Leaf Disease ◽  
Proposed Model ◽  
Reliable Performance ◽  
First Time

The tomato plant is the most broadly cultivated produce in India. As the Convolutional Neural Network (CNN) which comes under the field of image classification is performing the progressive work, thus using an approach of deep learning which mainly centers on achieving high accuracy of leaf disease of the tomato plant. Therefore, the main objective of this paper is to acquire more reliable performance in the identification of diseases. Amidst various plant diseases that affect leaf comprise of Late blight, bacterial and viral diseases have been chosen to differentiate infected leaves from that of the healthy leaves includes Late blight, bacterial and viral diseases. As we know, none of the other method has been proposed earlier which helps in detecting plant leaf diseases for the first time. Hence the proposed model is designed in such a way that it effectively identifies specific diseases that affect leaves of tomato plants through the use of a dataset containing about 4000 leaf images. CNN achieves an overall accuracy of 96% without implementing any pre-processing and feature extraction methods.

scholarly journals Improving Accuracy of Tomato Plant Disease Diagnosis Based on Deep Learning With Explicit Control of Hidden Classes

2021 ◽  
Vol 12 ◽  
Author(s):  
Alvaro Fuentes ◽  
Sook Yoon ◽  
Mun Haeng Lee ◽  
Dong Sun Park
Keyword(s):  
Deep Learning ◽  
Tomato Plant ◽  
Plant Disease ◽  
High Efficiency ◽  
Recognition Rate ◽  
Disease Diagnosis ◽  
Plant Diseases ◽  
Improve Model ◽  
Improving Accuracy ◽  
And Control

Recognizing plant diseases is a major challenge in agriculture, and recent works based on deep learning have shown high efficiency in addressing problems directly related to this area. Nonetheless, weak performance has been observed when a model trained on a particular dataset is evaluated in new greenhouse environments. Therefore, in this work, we take a step towards these issues and present a strategy to improve model accuracy by applying techniques that can help refine the model’s generalization capability to deal with complex changes in new greenhouse environments. We propose a paradigm called “control to target classes.” The core of our approach is to train and validate a deep learning-based detector using target and control classes on images collected in various greenhouses. Then, we apply the generated features for testing the inference of the system on data from new greenhouse conditions where the goal is to detect target classes exclusively. Therefore, by having explicit control over inter- and intra-class variations, our model can distinguish data variations that make the system more robust when applied to new scenarios. Experiments demonstrate the effectiveness and efficiency of the proposed approach on our extended tomato plant diseases dataset with 14 classes, from which 5 are target classes and the rest are control classes. Our detector achieves a recognition rate of target classes of 93.37% mean average precision on the inference dataset. Finally, we believe that our study offers valuable guidelines for researchers working in plant disease recognition with complex input data.

scholarly journals Flawless Identification of Fusarium Oxysporum in Tomato Plant Leaves by Machine Learning Algorithm

2021 ◽  
Vol 2 (4) ◽  
pp. 194-201
Author(s):  
Dhaya R
Keyword(s):  
Fusarium Oxysporum ◽  
Tomato Plant ◽  
Learning Algorithm ◽  
Visual Observation ◽  
Fungal Diseases ◽  
Plant Diseases ◽  
Cultivated Plants ◽  
Healthy Plant ◽  
Plant Leaves ◽  
Sweet Potatoes

In the olden days, plant diseases could be measured by visual observation and based on the level and severity of the symptoms on plant leaves. Over the day, it became a high-level degree of complexity due to the huge volume of cultivated plants. Now a day, the diseases are very different due to diverted manure procedures, and its diagnosis will be very tough even experienced farmers and agronomists too. Even though, after diagnosis, there is a lack of perfect remedy or mistaken treatment for that. The plants are affecting by many vascular fungal diseases which are widespread in many crops. Fusarium wilt (FW) is one of the fungal diseases in many plants. Mostly the tomato, sweet potatoes, tobacco, legumes, cucurbits plants are affected by this Fusarium oxysporum (FO) disease often due to its soil. The main goal of this research article is used to determine FO disease in the tomato plant leaves. Besides, the proposed algorithm constructs model with two times classifying and identifying the disease for better accuracy. The open database consists of 87k images with 60% affected leaves images, 40% healthy plant leaves too. Our proposed hybrid algorithm is found the disease with 96% accuracy with the huge amount of dataset.

scholarly journals Computer Vision Based Detection And Classification of Tomato Leaf Diseases

2019 ◽  
Vol 8 (11) ◽  
pp. 3100-3102
Keyword(s):  
Tomato Plant ◽  
Nearest Neighbor ◽  
Plant Diseases ◽  
Support Vector ◽  
K Nearest Neighbor ◽  
Excellent Performance ◽  
Initial Stage ◽  
Image Capturing ◽  
Processing Steps

Indian Economy mainly determined by the agriculture. Tomato is one of the highest used food crops in India. Due to which detection of disease on tomato plant becomes essential. The manual detection of plant diseases are very complex and high cost. Hence, image processing based detection of plant diseases gives the solution. Disease detection involves the steps like image capturing , various processing steps and classification. Most of the diseases of tomato plant detected at initial stages as they affect leaves first. By detecting the diseases at initial stage on leaves will surely avoid impending loss. The classifier, the classification is performed to classify the healthy and disease affected tomato leaves. Finally, the performance of K-nearest neighbor (KNN) and multi class Support Vector machine (SVM) are compared. The proposed system assured an excellent performance to farmers and researchers in admissible way.

Sign in / Sign up

Export Citation Format

Share Document