Analyzing Computational Response and Performance of Deep Convolution Neural Network for Plant Disease Classification using Plant Leave Dataset

Kumar, Lalit; Singh, Dushyant Kumar

doi:10.1109/csnt51715.2021.9509632

Cited by 9 publications

(3 citation statements)

References 10 publications

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“…In [18], a CNN-based algorithm with 97.61%b accuracy is suggested to assist Indian coffee plant growers in promptly identifying plant diseases. [19] shows that the model's accuracy can reach 97.90% using a plant village dataset with five classes and 4197 training and 430 test photos. For predicting plant diseases, the model employs a modest variation of a deep convolutional network.…”

Section: Related Workmentioning

confidence: 99%

PlantDoc-Plant Disease Detection using AI

Khan

Srivastava

2023

J. Infor. Electr. Electron. Eng.

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Gardening is a hobby which requires dedication and consistency. It is something more than just watering a plant. Taking care of Garden plants is very important as most of the plants are prone to diseases frequently. Plant Diseases ruin the plant and ultimately may kill it with time so timely identification and treatment of the disease is required for a healthy plant. This also helps to preserve many threatened species of plants. PlantDoc uses Artificial Intelligence model created on Convolution Neural Network algorithm of Deep Learning to solve this problem. The model is trained with images of different plant leaves to identify defected plants. PlantDoc helps in disease detection. It uses computer vision concept of AI to find the disease of plant and provide solution for that automatically. PlantDoc uses MERN stack. PlantDoc web application successfully helps to identify plant diseases of various plants by analyzing plant leaf image and suggests cure to treat it. This helps in treatment of plants timely which helps to stop the further spread of dis-ease and provides cure.

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Section: Related Workmentioning

confidence: 99%

PlantDoc-Plant Disease Detection using AI

Khan

Srivastava

2023

J. Infor. Electr. Electron. Eng.

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“…Another side, the Nvidia Jetson's hardware response is quite good compared to the Raspberry Pi 3B+ because it has a large number of CUDA cores of Maxwell generation GPU, that may allow it to be used in place of a desktop PC where space and cost are constraints. In contrast, the Raspberry Pi 3 B+ can also be used in place of a desktop PC, only for small and medium level AI tasks [12,13].…”

Section: Nvidia Jetson Nano Boardmentioning

confidence: 99%

“…The Nvidia Jetson contains greater number of computational units compared to the Raspberry Pi 3 B+. This makes Nvidia Jetson more reliable for handling complex computer vision tasks [12,13]. The Nvidia Jetson Nano board runs on a quad core ARM Cortex-A57 with 4 GB of SDRAM and consumes a maximum power of 10 W. The Nvidia Jetson Nano board runs on the Jetpack SDK (Ubuntu 18.4), which is stored on the SD card.…”

Section: Hardware Setupmentioning

confidence: 99%

Hardware Response and Performance Analysis of Multicore Computing Systems for Deep Learning Algorithms

Kumar

Singh

2022

Cybernetics and Information Technologies

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With the advancement in technological world, the technologies like Artificial Intelligence (AI), Machine Learning (ML), and Deep Learning (DL) are gaining more popularity in many applications of computer vision like object classification, object detection, Human detection, etc., ML and DL approaches are highly compute-intensive and require advanced computational resources for implementation. Multicore CPUs and GPUs with a large number of dedicated processor cores are typically the more prevailing and effective solutions for the high computational need. In this manuscript, we have come up with an analysis of how these multicore hardware technologies respond to DL algorithms. A Convolutional Neural Network (CNN) model have been trained for three different classification problems using three different datasets. All these experimentations have been performed on three different computational resources, i.e., Raspberry Pi, Nvidia Jetson Nano Board, & desktop computer. Results are derived for performance analysis in terms of classification accuracy and hardware response for each hardware configuration.

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Machine learning for leaf disease classification: data, techniques and applications

Yao,

Tran,

Sawyer

et al. 2023

Artif Intell Rev

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The growing demand for sustainable development brings a series of information technologies to help agriculture production. Especially, the emergence of machine learning applications, a branch of artificial intelligence, has shown multiple breakthroughs which can enhance and revolutionize plant pathology approaches. In recent years, machine learning has been adopted for leaf disease classification in both academic research and industrial applications. Therefore, it is enormously beneficial for researchers, engineers, managers, and entrepreneurs to have a comprehensive view about the recent development of machine learning technologies and applications for leaf disease detection. This study will provide a survey in different aspects of the topic including data, techniques, and applications. The paper will start with publicly available datasets. After that, we summarize common machine learning techniques, including traditional (shallow) learning, deep learning, and augmented learning. Finally, we discuss related applications. This paper would provide useful resources for future study and application of machine learning for smart agriculture in general and leaf disease classification in particular.

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Analyzing Computational Response and Performance of Deep Convolution Neural Network for Plant Disease Classification using Plant Leave Dataset

Cited by 9 publications

References 10 publications

PlantDoc-Plant Disease Detection using AI

PlantDoc-Plant Disease Detection using AI

Hardware Response and Performance Analysis of Multicore Computing Systems for Deep Learning Algorithms

Machine learning for leaf disease classification: data, techniques and applications

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