Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure

Aboneh, Tagel; Rorissa, Abebe; Srinivasagan, Ramasamy; Gemechu, Ashenafi

doi:10.3390/technologies9030047

Cited by 34 publications

(7 citation statements)

References 54 publications

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“…Various ML, IP, CV, and DL methods were assessed for different types of crop diseases in simple and complex background images. A framework presented in [25] used prominent DL networks such as ResNet 50, InceptionV3, and VGG19. The researchers compared the results of all these networks on a single dataset contains 1500 images representing three types of leaf diseases.…”

Section: Background Studiesmentioning

confidence: 99%

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Wheat Leaf Rust Detection Based on Multi-Scale DWT and LCS Color Thresholding Methods

Khan

Ullah

Rehman³

et al. 2023

Preprint

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Health, Technology, education, and food production are the four main issues facing developing nations like Pakistan, and it is undeniable that agriculture is the most important factor behind economic growth. In addition, implementing a strategy for food production is crucial for citizens to ensure their survival, and it is assumed that these initiatives will result in sufficient farm productivity. One strategy to make a field productive is to take significant care of its components, which starts with cultivating healthy plants or crops. Wheat leaf rust is a fatal condition that attacks young seedlings. It is a significant fungi disease. Leaf rust has 25% effect on the productivity of wheat. To mitigate this issue, a Multi-Scale Discrete Wavelet Transform (MsclDWT) using hybrid fusion rules method is proposed to obtain the complementary information from multiple input images. In second phase, Lab color space followed by color thresholding method is applied to detect and segment wheat leaf rust disease in wheat crop. The proposed model also computes the rust-affected area of the wheat crop, which assists the farmers in the post-medication (anti rust spray) process. The empirical results show that the proposed model achieved 97% of accuracy in rusted pixels detection and classification and outperformed the existing comparative methods.

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Section: Background Studiesmentioning

confidence: 99%

“…From Figs. 7 and 8, it was observed that our proposed method outclassed the methods presented in [17][18][19][20][21][22][23][24][25][26] in all cases. Furthermore, the results achieved form comparative methods were severely degraded using busy background crop led acquired images.…”

mentioning

confidence: 95%

Wheat Leaf Rust Detection Based on Multi-Scale DWT and LCS Color Thresholding Methods

Khan

Ullah

Rehman³

et al. 2023

Preprint

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“…Jiang et al [72] developed a wheat disease identification system for seven CNN models. After applying retuning training tactics, performance improvement has been noticed.…”

Section: Survey Based On Wheat Leaf Diseasesmentioning

confidence: 99%

Classification of Image-Based Wheat Leaf Diseases using Deep Learning Approach: A Survey

Sharma,

Sethi

2023

J. Sci. Res.

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This paper focuses on detecting leaf diseases in wheat plants from the beginning to the end of the plant's life cycle. It highlights the best techniques for detecting various types of wheat leaf diseases and emphasizes the use of computer vision, image processing, and machine learning. The main focus is on classifying these diseases through deep convolutional neural networks, a popular image recognition and classification approach. The paper reviews various techniques for classifying image-based wheat leaf diseases, including spot blotch, stripe rust, brown rust, and powdery mildew. The paper aims to summarize the state-of-the-art techniques for detecting wheat leaf diseases.

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“…They have discovered that the accuracy rates are more than 90 percent for three main disorders (powdery mildew, leaf rust, and stripe rust). In [7], author developed a Computer Vision Framework for the Identification and Classification of Wheat Diseases Using Jetson GPU Infrastructure demonstrating that manually identifying and interpreting wheat illnesses requires a significant amount of time and effort. They demonstrated that the VGG19 model was accurate in identifying wheat disease 99.38% of the time.…”

Section: Literature Reviewmentioning

confidence: 99%

Wheat Diseases Detection and Classification using Convolutional Neural Network (CNN)

Hossen¹,

Mohibullah²,

Muzammel³

et al. 2022

IJACSA

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Ever since the medieval era, the preponderance of our concentration has been concentrated upon agriculture, which is typically recognized to be one of the vital aspects of the economy in contemporary society. This focus on agriculture can be traced back to the advent of the industrial revolution. Wheat is still another type of grain that, in the same way as other types of harvests, satisfies the necessity for the essential nutrients that are required for our bodies to perform their functions correctly. On the other hand, the supply of this harvest is being limited by a variety of rather frequent ailments. This is making it difficult to meet demand. The vast majority of people who work in agriculture are illiterate, which hinders them from being able to take appropriate preventative measures whenever they are necessary to do so. As a direct consequence of this factor, there has been a reduction in the total amount of wheat that has been produced. It can be quite difficult to diagnose wheat illnesses in their early stages because there are so many various forms of environmental variables and other factors. This is because there are numerous distinct sorts of agricultural products, illiteracy of agricultural workers, and other factors. In the past, a variety of distinct models have been proposed as potential solutions for identifying illnesses in wheat harvests. This study demonstrates a two-dimensional CNN model that can identify and categorize diseases that affect wheat harvests. To identify significant aspects of the photos, the software employs models that have previously undergone training. The suggested method can then identify and categorize disease-affected wheat crops as distinct from healthy wheat crops by employing the major criteria described above. The reliability of the findings was assessed to be 98.84 percent after the collection of a total of 4800 images for this study. These images included eleven image classes of images depicting diseased crops and one image class of images depicting healthy crops. To offer the suggested model the capability to identify and classify diseases from a variety of angles, the photographs that help compensate for the collection were flipped at a variety of different perspectives. These findings provide evidence that CNN can be applied to increase the precision with which diseases in wheat crops are identified.

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Computer Vision Framework for Wheat Disease Identification and Classification Using Jetson GPU Infrastructure

Cited by 34 publications

References 54 publications

Wheat Leaf Rust Detection Based on Multi-Scale DWT and LCS Color Thresholding Methods

Wheat Leaf Rust Detection Based on Multi-Scale DWT and LCS Color Thresholding Methods

Classification of Image-Based Wheat Leaf Diseases using Deep Learning Approach: A Survey

Wheat Diseases Detection and Classification using Convolutional Neural Network (CNN)

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