Identification of Plant Disease using Image Processing Technique

Devaraj, Abirami; Rathan, Karunya; Jaahnavi, Sarvepalli; Indira, K.

doi:10.1109/iccsp.2019.8698056

Cited by 112 publications

(38 citation statements)

References 10 publications

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“…The following is a brief description of the three common types of plant diseases [28] that are illustrated in Figure 1 and described in Table 1:…”

Section: Taxonomy Of Crop Diseases and Their Symptomsmentioning

confidence: 99%

“…Foliar images are an excellent and rich source of data on plant pathology and morphological behavior; thus, these data must be thoroughly extracted and analyzed. Image processing plays [28] a crucial role in the diagnosis and analysis of leaf diseases. The procedure adopted in this leaf disease identification process is illustrated in Figure 2, showing an insight into the different techniques employed by the authors to detect the disease by means of image processing and artificial intelligence.…”

Section: Application Of Machine Learning and Image Processing In Disease Identificationmentioning

confidence: 99%

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On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

2021

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The agricultural sector remains a key contributor to the Moroccan economy, representing about 15% of gross domestic product (GDP). Disease attacks are constant threats to agriculture and cause heavy losses in the country’s economy. Therefore, early detection can mitigate the severity of diseases and protect crops. However, manual disease identification is both time-consuming and error prone, and requires a thorough knowledge of plant pathogens. Instead, automated methods save both time and effort. This paper presents a contemporary overview of research undertaken over the past decade in the field of disease identification of different crops using machine learning, deep learning, image processing techniques, the Internet of Things, and hyperspectral image analysis. Additionally, a comparative study of several techniques applied to crop disease detection was carried out. Furthermore, this paper discusses the different challenges to be overcome and possible solutions. Then, several suggestions to address these challenges are provided. Finally, this research provides a future perspective that promises to be a highly useful and valuable resource for researchers working in the field of crop disease detection.

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“…The following is a brief description of the three common types of plant diseases [28] that are illustrated in Figure 1 and described in Table 1:…”

Section: Taxonomy Of Crop Diseases and Their Symptomsmentioning

confidence: 99%

Section: Application Of Machine Learning and Image Processing In Disease Identificationmentioning

confidence: 99%

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

2021

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“…Some examples of edge detection techniques running on MATLAB include CANNY, SOBEL, and LAPLACIAN. To promote the growth and productivity of crop field, authors in [23]- [25] proposed a MATLAB-based image processing technique for plant disease detection and monitoring. Sing and Chetia, [24] investigated the use of MATLAB-based image processing technique for the detection of plant-leaf disease.…”

Section: Review Of Related Workmentioning

confidence: 99%

Image processing system using MATLAB-based analytics

et al. 2021

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Owing to recent technological advancement, computers and other devices running several image editing applications can be further exploited for digital image processing operations. This paper evaluates various image processing techniques using matrix laboratory (MATLAB-based analytics). Compared to the conventional techniques, MATLAB gives several advantages for image processing. MATLAB-based technique provides easy debugging with extensive data analysis and visualization, easy implementation and algorithmic-testing without recompilation. Besides, MATLAB's computational codes can be enhanced and exploited to process and create simulations of both still and video images. Moreover, MATLAB codes are much concise compared to C++, thus making it easier for perusing and troubleshooting. MATLAB can handle errors prior to execution by proposing various ways to make the codes faster. The proposed technique enables advanced image processing operations such as image cropping/resizing, image denoising, blur removal, and image sharpening. The study aims at providing readers with the most recent MATLAB-based image processing application-tools. We also provide an empirical-based method using two-dimensional discrete cosine transform (2D-DCT) derived from its coefficients. Using the most recent algorithms running on MATLAB toolbox, we performed simulations to evaluate the performance of our proposed technique. The results largely present MATLAB as a veritable approach for image processing operations.

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“…Abirami Devaraj, et.al (2019) stated that images processing techniques were used to detect plant infections. The major objective of this study was to develop a software system for automatic detection and classification of plant infections [3]. The image processing approach used various steps to identify plant diseases such as image acquisition, image preprocessing, image segmentation, feature extraction and image classification.…”

Section: Related Workmentioning

confidence: 99%

Image Processing and Classification, A Method for Plant Disease Detecion

2019

IJITEE

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The plant disease detection is the major issue of the computer vision and machine learning. The plant disease detection has the various phases like pre-processing, segmentation, feature extraction and classification. In the existing technique support vector machine is used for the classification. The support vector machine approach has the low accuracy for the plant disease detection and also it can classify data into two classes which affect its performance. The proposed methodology is based on the region based segmentation, textual feature analysis and k-nearest neighbor method is applied for the classification. The proposed method is implemented in MATLAB and results are analyzed in terms of accuracy. The proposed technique has high accuracy and compared to existing technique.

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Identification of Plant Disease using Image Processing Technique

Cited by 112 publications

References 10 publications

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

On Using Artificial Intelligence and the Internet of Things for Crop Disease Detection: A Contemporary Survey

Image processing system using MATLAB-based analytics

Image Processing and Classification, A Method for Plant Disease Detecion

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