Detection of Disease and Pest of Kenaf Plant using Convolutional Neural Network

Fajri, Diny Melsye Nurul; Mahmudy, Wayan Firdaus; Yulianti, Titiek

doi:10.25126/jitecs.202161195

Cited by 3 publications

(2 citation statements)

References 9 publications

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“…This research will introduce the types of pests and diseases on kenaf plants using image-based deep learning techniques. This research claims to continue previous research that has been carried out by Fajri [12], who instilled a similar method on the same object. Changes made in this study were to provide training for horizontal 5×5 matrix calculations on the image data in the input model that was used to carry out more effective, efficient, and maximum recognition training.…”

Section: Abstract: Convolutional Neural Network Image Recognition Kenaf Neural Network Vggnet19supporting

confidence: 67%

Detection of Disease and Pest of Kenaf Plant Based on Image Recognition with VGGNet19

Fajri

Mahmudy

Yulianti³

2021

Kno. Eng. Da. Sc.

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One of the advantages of Kenaf fiber as an environmental management product that is currently in the center of attention is the use of Kenaf fiber for luxury car interiors with environmentally friendly plastic materials. The opportunity to export Kenaf fiber raw material will provide significant benefits, especially in the agricultural sector in Indonesia. However, there are problems in several areas of Kenaf's garden, namely plants that are attacked by diseases and pests, which cause reduced yields and even death. This problem is caused by the lack of expertise and working hours of extension workers as well as farmers' knowledge about Kenaf plants which have a terrible effect on Kenaf plants. The development of information technology can be overcome by imparting knowledge into machines known as artificial intelligence. In this study, the Convolutional Neural Network method was applied, which aims to identify symptoms and provide information about disease symptoms in Kenaf plants based on images so that early control of plant diseases can be carried out. Data processing trained directly from kenaf plantations obtained an accuracy of 57.56% for the first two classes of introduction to the VGGNet19 architecture and 25.37% for the four classes of the second introduction to the VGGNet19 architecture. The 5×5 block matrix input feature has been added in training to get maximum results.

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Section: Abstract: Convolutional Neural Network Image Recognition Kenaf Neural Network Vggnet19supporting

confidence: 67%

Detection of Disease and Pest of Kenaf Plant Based on Image Recognition with VGGNet19

Fajri

Mahmudy

Yulianti³

2021

Kno. Eng. Da. Sc.

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“…The generated model's distinctive feature is that it's a brand-new CNN classification model with 99.34% training accuracy. [22] In this paper, they used CNN as a machine learning tool to deliver results for the input of leaf images into the temporary diagnosis findings. 838 images were used for 4 classes.…”

Section: Introductionmentioning

confidence: 99%

An Improved CNN model for Identifying Tomato Leaf Diseases

BHARALI

2024

tjes

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A major loss in gross domestic product, quantity and quality of products produced, as well as tomato production, is caused by diseases in tomato leaves due to which farmers have a difficult time in controlling and monitoring the health of tomato leaves, one of which is leaf disease. In our project, we developed an Enhanced CNN by using data augmentation techniques to identify the seven classes(blight ,leaf curl, leaf miner ,Alteneria, leaf spot, cutwork infected) of tomato leaf diseases. Using 27807 trainable parameters, the enhanced CNN obtains the maximum training accuracy of ninety nine point nine eight percent(99.98%) and validating accuracy of ninety eight point four percent(98.4%) .With fewer parameters, The Enhanced CNN can more accurately determine the type of illness of tomato leaf. Our Enhanced CNN model also determine the type of illness of tomato leaf when tested with the images of diseased tomato leaf collected from the internet sources.Using 152850 trainable parameters the enhanced CNN obtain the maximum training accuracy of 99.68% and validation accuracy of 89%.

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