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 A Chaotic Salp Swarm Feature Selection Algorithm for Apple and Tomato Plant Leaf Disease Detection

2021 ◽  
Vol 10 (5) ◽  
pp. 3037-3045
Keyword(s):  
Deep Learning ◽  
Short Term Memory ◽  
Detection System ◽  
Plant Diseases ◽  
Healthy Plant ◽  
Disease Detection ◽  
Test Accuracy ◽  
Plant Leaves ◽  
Leaf Disease ◽  
Deep Learning Network

Plants are seen as vital because they provide mankind with energy. Plant diseases can harm the leaf at any time between planting and harvesting, resulting in enormous losses in crop output and market value. A leaf disease detection system acts asignificant role in agricultural production. A large amount of labour is required for this process as well as an in-depth understanding of plant diseases. Determining the presence of illnesses in plant leaves requires the use of deep learning and machine learning methods, which classify the data based on a specified set. In this paper, apple and tomato leaves disease detection process is carried out by Chaotic Salp Swarm algorithm (CSSA) followed by Bi-directional Long Short Term Memory (Bi-LSTM) technique for classification. We've used the Bi-LSTM architecture to sense disease in tomato and apple leaves in studies. In order to determine the type of leaves, we trained a deep learning network using the PlantVillage dataset of damaged and healthy plant leaves. It is estimated that the trained model achieves a test accuracy of 96%.

Tomato Plant Leaves Disease Classification Using KNN and PNN

2019 ◽  
Vol 9 (1) ◽  
pp. 51-63 ◽  
Author(s):  
Balakrishna K. ◽  
Mahesh Rao
Keyword(s):  
Food Security ◽  
Tomato Plant ◽  
Tomato Leaf ◽  
Plant Diseases ◽  
Disease Classification ◽  
Plant Leaves ◽  
Second Stage ◽  
Major Threat ◽  
Fusion Approach

Plant diseases are a major threat to the productivity of crops, which affects food security and reduces the profit of farmers. Identifying the diseases in plants is the key to avoiding losses by proper feeding measures to cure the diseases early and avoiding the reduction in productivity/profit. In this article, the authors proposed two methods for identification and classification of healthy and unhealthy tomato leaves. In the first stage, the tomato leaf is classified as healthy or unhealthy using the KNN approach. Later, in the second stage, they classify the unhealthy tomato leaf using PNN and the KNN approach. The features are like GLCM, Gabor, and color are used for classification purposes. Experimentation is conducted on the authors own dataset of 600 healthy and unhealthy leaves. The experimentation reveals that the fusion approach with PNN classifier outperforms than other methods.

scholarly journals Plant Leaf Disease Prediction

2021 ◽  
Vol 9 (VII) ◽  
pp. 1295-1305
Author(s):  
Vaishnavi Monigari
Keyword(s):  
Plant Disease ◽  
Accurate Method ◽  
Significant Loss ◽  
Plant Diseases ◽  
Economic Losses ◽  
Healthy Plant ◽  
Plant Leaves ◽  
Production Economic ◽  
Agricultural Yield ◽  
Processing Techniques

The Indian economy relies heavily on agriculture productivity. A lot is at stake when a plant is struck with a disease that causes a significant loss in production, economic losses, and a reduction in the quality and quantity of agricultural products. It is crucial to identify plant diseases in order to prevent the loss of agricultural yield and quantity. Currently, more and more attention has been paid to plant diseases detection in monitoring the large acres of crops. Monitoring the health of the plants and detecting diseases is crucial for sustainable agriculture. Plant diseases are challenging to monitor manually as it requires a great deal of work, expertise on plant diseases, and excessive processing time. Hence, this can be achieved by utilizing image processing techniques for plant disease detection. These techniques include image acquisition, image filtering, segmentation, feature extraction, and classification. Convolutional Neural Network’s(CNN) are the state of the art in image recognition and have the ability to give prompt and definitive diagnoses. We trained a deep convolutional neural network using 20639 images on 15 folders of diseased and healthy plant leaves. This project aims to develop an optimal and more accurate method for detecting diseases of plants by analysing leaf images.

scholarly journals ANTIBACTERIAL ACTIVITY OF SILVER NANOPARTICLES SYNTHESIZED USING CITRUS SINENSIS PEEL EXTRACT AGAINST PATHOGENS ISOLATED FROM DISEASED TOMATO (SOLANUM LYCOPERSICUM L.)

2021 ◽  
Vol 4 (1) ◽  
pp. 1-3
Author(s):  
ONYEMAECHI OBIAZIKWOR ◽  
Hakeem Olalekan SHITTU
Keyword(s):  
Silver Nanoparticles ◽  
Antibacterial Activity ◽  
Tomato Plant ◽  
Plant Diseases ◽  
Diffusion Method ◽  
Plant Leaves ◽  
Citrus Peel ◽  
North East ◽  
Plant Disease Management ◽  
Peel Extract

Among all the noble nanoparticles, silver nanoparticles have gained boundless interests because of their unique properties such as chemical stability, catalytic and most important antimicrobial activities. This study was carried out to investigate the antibacterial activity of phytosynthesized silver nanoparticles against bacteria pathogens isolated from diseased tomato plant leaves. Silver nanoparticles were synthesized using Citrus peel extract and the formation of nanoparticles was monitored using spectrophotometer. Diseased tomato plant leaves were obtained from a farm located at Ovia North-East Local Government Area, Edo State, Nigeria for the isolation of bacteria pathogens. The isolated bacteria include Pseudomonas sp. and Enterobacter sp. Antibacterial testing using the phytosynthesized silver nanoparticles was carried out via the agar well diffusion method on the test isolates. Zones of inhibition of 10 and 8 mm were obtained for Enterobacter and Pseudomonas species respectively by 100 µl nanoparticles treatment after 24 hours of incubation. This indicated that the phytosynthesized silver nanoparticles have antibacterial activity against the bacterial pathogens. Further studies should be carried out to determine the mode of action of silver nanoparticles and the potential of the test nanoparticles in plant disease management. The potential of members of the genus, Enterobacter as causative agents of plant diseases should be further investigated.

scholarly journals Identification of Plant-Leaf Diseases Using CNN and Transfer-Learning Approach

Electronics ◽  
2021 ◽  
Vol 10 (12) ◽  
pp. 1388
Author(s):  
Sk Mahmudul Hassan ◽  
Arnab Kumar Maji ◽  
Michał Jasiński ◽  
Zbigniew Leonowicz ◽  
Elżbieta Jasińska
Keyword(s):  
Disease Classification ◽  
Batch Size ◽  
Healthy Plant ◽  
Plant Leaves ◽  
Early Prevention ◽  
Computation Cost ◽  
Training Time ◽  
Feature Based ◽  
Deep Cnn ◽  
Accuracy Rates

The timely identification and early prevention of crop diseases are essential for improving production. In this paper, deep convolutional-neural-network (CNN) models are implemented to identify and diagnose diseases in plants from their leaves, since CNNs have achieved impressive results in the field of machine vision. Standard CNN models require a large number of parameters and higher computation cost. In this paper, we replaced standard convolution with depth=separable convolution, which reduces the parameter number and computation cost. The implemented models were trained with an open dataset consisting of 14 different plant species, and 38 different categorical disease classes and healthy plant leaves. To evaluate the performance of the models, different parameters such as batch size, dropout, and different numbers of epochs were incorporated. The implemented models achieved a disease-classification accuracy rates of 98.42%, 99.11%, 97.02%, and 99.56% using InceptionV3, InceptionResNetV2, MobileNetV2, and EfficientNetB0, respectively, which were greater than that of traditional handcrafted-feature-based approaches. In comparison with other deep-learning models, the implemented model achieved better performance in terms of accuracy and it required less training time. Moreover, the MobileNetV2 architecture is compatible with mobile devices using the optimized parameter. The accuracy results in the identification of diseases showed that the deep CNN model is promising and can greatly impact the efficient identification of the diseases, and may have potential in the detection of diseases in real-time agricultural systems.

scholarly journals Recognition of Tomato Late Blight by using DWT and Component Analysis

2017 ◽  
Vol 7 (1) ◽  
pp. 194
Author(s):  
Hiteshwari Sabrol ◽  
Satish Kumar
Keyword(s):  
Principal Component Analysis ◽  
Late Blight ◽  
Distance Measure ◽  
Recognition Accuracy ◽  
Principal Component ◽  
Component Analysis ◽  
Plant Diseases ◽  
Kernel Principal Component Analysis ◽  
Healthy Plant ◽  
Testing Dataset

Plant disease recognition concept is one of the successful and important applications of image processing and able to provide accurate and useful information to timely prediction and control of plant diseases. In the study, the wavelet based features computed from RGB images of late blight infected images and healthy images. The extracted features submitted to Principal Component Analysis (PCA), Kernel Principal Component Analysis (KPCA) and Independent Component Analysis performed (ICA) for reducing dimensions in feature data processing and classification. To recognize and classify late blight from healthy plant images are classified into two classes i.e.  late blight infected or healthy. The Euclidean Distance measure is used to compute the distance by these two classes of training and testing dataset for tomato late blight recognition and classification. Finally, the three-component analysis is compared for late blight recognition accuracy. The Kernel Principal Component Analysis (KPCA) yielded overall recognition accuracy with 96.4%.

scholarly journals Plant rotation as a means of controlling wilt disease of Lupinus luteus L. on the basis of mycological investigations

2014 ◽  
Vol 12 (2) ◽  
pp. 225-240
Author(s):  
Wanda Truszkowska
Keyword(s):  
Fusarium Oxysporum ◽  
Wilt Disease ◽  
Crop Rotations ◽  
Lupinus Luteus ◽  
Cultivated Plants ◽  
Quantitative Composition ◽  
Qualitative And Quantitative ◽  
Cultivated Soil ◽  
Fungal Populations

Having observed in 1969 an epidemic of Fusarium</i> wilt disease of lupin, the communities of fungi occurring in a given cultivated soil were examined. In the years 1970-72, the numbers of the <i>Fusarium oxysporum</i> f.sp. <i>lupini</i> population were found to change, in the others under the influence of lupin having been cultivated in the following crop rotations : 1) rye, potatoes, oats or 2) rye, maize, oats. Provoking changes in the qualitative and quantitative composition of the fungal populations in cultivated soil by means of different cultivated plants, with the aim of altering the conditions of the pathogen, should be practised in order to protect the lupin crops against <i>Fusariuni</i> wilt disease.

Plant Disease Detection Using Machine Learning Approaches

2021 ◽  
pp. 122-130
Author(s):  
Sukanta Ghosh ◽  
Shubhanshu Arya ◽  
Amar Singh
Keyword(s):  
Plant Disease ◽  
Crop Yields ◽  
Detection System ◽  
Visual Observation ◽  
Plant Diseases ◽  
Disease Detection ◽  
Learning Approaches ◽  
Factors Affecting ◽  
Plant Leaf ◽  
Market Situation

Agricultural production is one of the main factors affecting a country's domestic market situation. Many problems are the reasons for estimating crop yields, which vary in different parts of the world. Overuse of chemical fertilizers, uneven distribution of rainfall, and uneven soil fertility lead to plant diseases. This forces us to focus on effective methods for detecting plant diseases. It is important to find an effective plant disease detection technique. Plants need to be monitored from the beginning of their life cycle to avoid such diseases. Observation is a kind of visual observation, which is time-consuming, costly, and requires a lot of experience. For speeding up this process, it is necessary to automate the disease detection system. A lot of researchers have developed plant leaf detection systems based on various technologies. In this chapter, the authors discuss the potential of methods for detecting plant leaf diseases. It includes various steps such as image acquisition, image segmentation, feature extraction, and classification.

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