Maturity and disease detection in tomato using computer vision

Mehra, Tanvi; Kumar, Vinay; Gupta, Pragya

doi:10.1109/pdgc.2016.7913228

Cited by 39 publications

(7 citation statements)

References 6 publications

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“…Для цього здійснено перехід до використання алгоритму кластеризації k-середнього, що дало змогу зробити метод сегментації більш універсальним в різних умовах. В статях багато уваги приділяють застосуванню методів сегментації для виявлення ознак таких як зміна кольору та текстурних особливостей [7].…”

Section: ʑ˔˕˖˒unclassified

Recognition of Damaged Forest With the Help of Convolutional Models in Remote Sensing

Rusyn¹,

Lutsyk²,

Kosarevych³

et al. 2021

UJIT

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The article provides a detailed review of the problem of deforestation, which in recent years has become uncontrolled. The main causes of forest damage are analyzed, among which the most well-known are climate change, diseases and pests. The losses of forestry as a result of tree diseases, which are large-scale and widespread in other countries, are given. The solution of these problems is possible under the condition of high-quality monitoring with the involvement of automated remote sensing tools and modern methods of image analysis, including artificial intelligence approaches such as neural networks and deep learning. The article proposes an approach to automatic localization and recognition of trees affected by drought, which is of great practical importance for environmental monitoring and forestry. A fully connected convolutional model of deep learning using the tensorflow and keras libraries has been developed for localization and recognition. This model consists of a detector network and a separate classifier network. To train and test the proposed network based on images obtained by remote sensing, a training database containing 8500 images was created. A comparison of the proposed model with the existing methods is based on such characteristics as accuracy and speed. The accuracy and speed of the proposed recognition system were evaluated on a validation sample of images, consisting of 1700 images. The model has been optimized for practical use with CPU and GPU due to pseudo quantization during training. This helps to distribute the values of the weights in the learning process and bring their appearance closer to a uniform distribution law, which in turn allows more efficient application of quantization to the original model. The average operating time of the algorithm is also determined. In the Visual C++ environment, based on the proposed model, an expert program has been created that allows to perform the ecological monitoring and analysis of dry forests in the field in real time. Libraries such as OpenCV and Direct were used in software development, and the code supports object-oriented programming standards. The results of the work and the developed software can be used in remote monitoring and classification systems for environmental monitoring and in applied tasks of forestry.

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Section: ʑ˔˕˖˒unclassified

Recognition of Damaged Forest With the Help of Convolutional Models in Remote Sensing

Rusyn¹,

Lutsyk²,

Kosarevych³

et al. 2021

UJIT

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“…In paper [5], detection of unhealthy plant leaves is done using image processing and genetic algorithm with Ardunio system. Paper [6] includes tomato disease detection using computer vision in which , depending on threshold value, a gray scale image is turned into binary image. The methodology for cucumber disease detection is presented in paper [7].…”

Section: Plantvillage Image Dataset For Tomato Leaf Disease Classificationmentioning

confidence: 99%

Machine Learning Approach towards Tomato Leaf Disease Classification

Gadade¹

2020

IJATCSE

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India in an agricultural country and the detection of diseases in first stage is very important to increase the crop yield. The bacterial spot, late blight, septoria leaf spot and yellow curved leaf diseases affect the crop quality of tomatoes. In this paper, to detect symptoms of disease, we have developed a module that classifies the plant leaf disease automatically. This paper presents a performance measure for different feature extraction techniques for tomato leaf disease detection including GLCM, Gabor and SURF and classification techniques including decision trees, SVM, KNN and Naïve Bayes. The dataset contains 500 images of tomato leaves with seven symptoms of diseases. We have modeled a system for automatic feature extraction and classification. We have evaluated the performance of the system using different performance measures to conclude with appropriate features set and classification technique for tomato leaf disease classification. The experimental results validate that Gabor features effectively recognizes different types of tomato leaf diseases. Accuracy of SVM is better as compared to other classification techniques but the execution time is more.

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“…The study is mainly done for bacterial and leaf spot, tomato blight, mosaic virus and yellow leaf curl virus and in the image processing methods-the most used method image augmentation is used and found more than 90% accuracy for the above mentioned diseases. In Thapar University, [14] the study was done to detect the tomato diseases using image thresholding to segment the infected part and k-means clustering to detect the disease and then the percentage of infected portion in a leaf was detected. Four layer deep CNN [15], was used with different filters in every layer like 10 filters in first layer, 20 in second, 64 in third and 30 in fourth layer to detect the various plant diseases that is, tomato, pepper and potato and got an accuracy of more than 98%.…”

Section: Introductionmentioning

confidence: 99%

Extracting features of tomato viral leaf diseases using image processing techniques

Sagar,

Singh

2024

IJ-AI

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<span>Agriculture is the main livelihood of Indians. More than 50% of Indian population Is dependent on it and it contributes about 18% of Indian gross domestic product (GDP). According to Inc42, the agricultural sector of India is predicted to increase to US$ 24 billion by 2025. With the increase in population, the demand for food also increases, but more than 30% of crops get affected due to crop diseases. Overall, India lost approximately five million hectares of crop area to flash floods, cyclonic storms, floods, cloudbursts, and landslides till 2021. In that case, there is a need to prevent crops from diseases to fulfil demand supply ratio. This paper presents the feature extraction of tomato viral leaf diseases using various image processing techniques. Most of the research uses Convolutional Neural networks to extract the features of these diseases, but these neural networks are not performing much accurately in real scenarios, so there is a need to extract the features using image processing methods. During the study, it is found that these diseases have different colours, shapes and textures and these features can be used with convolution neural networks to bring more accurate results in real scenarios.</span>

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Maturity and disease detection in tomato using computer vision

Cited by 39 publications

References 6 publications

Recognition of Damaged Forest With the Help of Convolutional Models in Remote Sensing

Recognition of Damaged Forest With the Help of Convolutional Models in Remote Sensing

Machine Learning Approach towards Tomato Leaf Disease Classification

Extracting features of tomato viral leaf diseases using image processing techniques

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