Plant Leaf Detection and Disease Recognition using Deep Learning

Militante, Sammy V.; Gerardo, Bobby D.; Dionisio, Nanette V.

doi:10.1109/ecice47484.2019.8942686

Cited by 136 publications

(25 citation statements)

References 8 publications

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“…Several researchers have developed automatic recognition and identification algorithms for the classification of diseased plants [5][6][7][8]. Singh et al (2019) [3] have developed an automated model for the identification of plant leaves diseases by implementing computer vision (CV), machine learning (ML), and artificial intelligence (AI).…”

Section: Introductionmentioning

confidence: 99%

Intelligent Plant Disease Identification System Using Machine Learning

Alagumariappan

Dewan

Muthukrishnan

et al. 2020

7th International Electronic Conference on Sensors and Applications

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Agriculture is the backbone of every country in the world. In India, most of the rural population still depends on agriculture. The agricultural sector provides major employment in rural areas. Furthermore, it contributes a significant amount to India’s gross domestic product (GDP). Therefore, protecting and enhancing the agricultural sector helps in the development of India’s economy. In this work, a real-time decision support system integrated with a camera sensor module was designed and developed for identification of plant disease. Furthermore, the performance of three machine learning algorithms, such as Extreme Learning Machine (ELM) and Support Vector Machine (SVM) with linear and polynomial kernels was analyzed. Results demonstrate that the performance of the extreme learning machine is better when compared to the adopted support vector machine classifier. It is also observed that the sensitivity of the support vector machine with a polynomial kernel is better when compared to the other classifiers. This work appears to be of high social relevance, because the developed real-time hardware is capable of detecting different plant diseases.

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Section: Introductionmentioning

confidence: 99%

Intelligent Plant Disease Identification System Using Machine Learning

Alagumariappan

Dewan

Muthukrishnan

et al. 2020

7th International Electronic Conference on Sensors and Applications

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“…CNN's is a conventional multi-layer neural network where the previous layer feeds one layer and outcomes can be measured and analyzed from both layers [12]. CNN is applied precisely in image-processing [10], processing of humanlanguage, computer-vision [11], self-driving automobiles. The CNNs is also a particular form of neural network architecture that depicts a conventional feed-forward neural network; it simulates a human visual processing cortex of the brain, where the filtration system is a network of cells that resembles a certain portion of a picture [7].…”

Section: A the Cnns Or Convolutional Neural Networkmentioning

confidence: 99%

“…Advancements in the field of deep learning, particularly convolutional neural networks (CNNs), have already shown remarkable success in the classification of images [10]. The key idea behind the CNNs is to create an artificial model, like a visualization area of the human brain [11].…”

Section: Introductionmentioning

confidence: 99%

“…The key idea behind the CNNs is to create an artificial model, like a visualization area of the human brain [11]. The biggest advantage of CNNs is that one can extract more important characteristics over the whole image, instead of just handcrafted attributes [10]. Researchers introduced different deep networks based on CNN, and these networks achieved the state of results in computer vision classification, segmentation, object detection, and localization.…”

Section: Introductionmentioning

confidence: 99%

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Deep Learning Implementation of Facemask and Physical Distancing Detection with Alarm Systems

Militante

Dionisio

2020

2020 Third International Conference on Vocational Education and Electrical Engineering (ICVEE)

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COVID-19 or Severe Acute Respiratory Syndrome Corona virus-2 is an extremely transmissible virus that is discharged through breathing droplets released from an infected individual who is talking, sneezing, or coughing. Close interaction with a person infected or through touching a contaminated surface and object can spread the virus rapidly. As of now, there is no vaccine to combat the COVID-19, and the best way to protect the person from a virus is to avoid being exposed to it. Wearing a facemask that covers the nose and mouth in a public setting and repeatedly cleansing of hands or the use of at least 70% alcohol-based disinfectants is a practice to avoid virus exposure. Deep Learning technology has demonstrated its achievement in recognition and classification by processing images. The research study uses deep learning techniques that identify if the person is wearing a facemask or not and check if the persons in the area observe physical distancing. The collected image data contains 20,000 images, uniformly crop images in 224x224 pixels, and attained an accuracy rate of 97% during the training of the model. The developed system is implemented using Python and OpenCV through TensorFlow that recognizes persons wearing a facemask or not wearing and measures the physical distance between each person. It signals an alarm and captures facial images upon detecting persons not wearing a mask and does not observe physical distancing. This study is beneficial in combating the spread of the virus and avoiding contact with the virus.

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“…In this research study, deep learning techniques are applied to construct a classifier that will collect images of a person wearing a face mask and not from the database and differentiate between these classes of facemask-wearing and not facemask-wearing [16]. The artificial neural network has been demonstrated to be a vigorous procedure for feature extraction from unprocessed data [17], [18]. This study proposes the use of a convolutional neural network to design the facemask classifier and to include the effect of the number of the convolutional neural layer on the prediction accuracy.…”

mentioning

confidence: 99%

Real-Time Facemask Recognition with Alarm System using Deep Learning

Militante

Dionisio

2020

2020 11th IEEE Control and System Graduate Research Colloquium (ICSGRC)

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113

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In the background of the COVID-19 pandemic, institutions such as the academy suffer a great deal from practically closed globally if the current situation is not going to change. COVID-19 also known as Serious Acute Respiratory Syndrome Corona virus-2 is an infectious disease that is released from an infected sick person who speaks, sneezes, or coughs by respiratory droplets. This spreads quickly through close contact with anyone infected, or by touching objects or surfaces affected with a virus. There's still currently no vaccine available to protect against COVID-19 and preventing exposure to the virus seems to be the only method to safeguard ourselves. Wearing a facemask that covers the nose and mouth in a public setting and often washing hands or the use of at least 70% alcohol-based sanitizers is one way to avoid being exposed to the virus. Amid the advancement of technology, Deep Learning has proven its effectiveness in recognition and classification through image processing. The research study uses deep learning techniques in distinguishing facial recognition and recognize if the person is wearing a facemask or not. The dataset collected contains 25,000 images using 224x224 pixel resolution and achieved an accuracy rate of 96% as to the performance of the trained model. The system develops a Raspberry Pi-based real-time facemask recognition that alarms and captures the facial image if the person detected is not wearing a facemask. This study is beneficial in combating the spread of the virus and avoiding contact with the virus.

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Plant Leaf Detection and Disease Recognition using Deep Learning

Cited by 136 publications

References 8 publications

Intelligent Plant Disease Identification System Using Machine Learning

Intelligent Plant Disease Identification System Using Machine Learning

Deep Learning Implementation of Facemask and Physical Distancing Detection with Alarm Systems

Real-Time Facemask Recognition with Alarm System using Deep Learning

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