Image processing-based intelligent robotic system for assistance of agricultural crops

Paliwal, Nikhil; Vanjani, Pankhuri; Liu, Jing Wei; Saini, Sandeep; Sharma, Abhishek

doi:10.1504/ijshc.2019.101602

Cited by 18 publications

(11 citation statements)

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“…Processing Technology e structure of the special module for recognizing general images is shown in Figure 1: e concept of drawing develops through the analysis of images, the combination of cognitive abilities, the knowledge of the nature of each object in art, the relationship between them, a better understanding of the meaning of the diagram, and the explanation of similar goals and instructions, as planned. Image comprehension is a high-performance process based on the knowledge of image content as well as the use of marketing expertise and the identification of information contained in images [11]. Between the two links of image pretreatment and image analysis, image segmentation is generally required to extract the object of interest from the original image.…”

Section: Intelligent Image Recognitionmentioning

confidence: 99%

Realization of an Intelligent Image Processing Technology for the Analysis of Chinese Painting Color Education

2022

Mobile Information Systems

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An intelligent information recognition technology based on picture image processing was proposed to further improve the application effect of intelligent image technology in the color teaching of painting major and improve the level of Chinese painting color teaching with the aid of intelligent image processing technology. Special image recognition technology was used to effectively identify the color of Chinese paintings based on traditional teaching methods. MATLAB software for simulation processing of Chinese painting was also utilized through the fuzzy set theory test sample image recognition. The results show that the method is based on feature vector extraction and orthogonal basis transformation histogram. The former takes 0.072288 seconds in image recognition time, while the latter takes 0.134939 seconds, which is nearly double the former time. Therefore, the software and image processing technology mentioned in the article can further improve the effect and time of image color recognition.

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Section: Intelligent Image Recognitionmentioning

confidence: 99%

Realization of an Intelligent Image Processing Technology for the Analysis of Chinese Painting Color Education

2022

Mobile Information Systems

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“…In Equation (11), Rrecall means the relative rate of the accurately detected target object regions for the total target regions existing in an input color image. To quantitatively evaluate and compare the performance of the proposed deep neural network-based target object blocking technique, this study utilized such accuracy scale ways as those in Equations (10) and (11), where N TP means the number of accurately detected target regions, N FP is the number of regions which are not target regions but are incorrectly detected as target areas, and N FN is the number of regions which are target areas but are not detected. In Equation (10), R precision represents the relative rate of the accurately detected target regions for the total detected target object regions.…”

Section: Resultsmentioning

confidence: 99%

“…In terms of implementation, we took full advantage of the source code published on GitHub to implement the existing methods. Figures 13 and 14 illustrate the accuracy measurement graphs of the target object area blocking algorithms, which were obtained in Equation (10) and (11). The proposed method improves the precision rate by about 0.05 and 0.02, respectively, and the recall rate by 0.09 and 0.02, respectively, compared to the conventional skin color-based and general learning-based methods.…”

Section: Resultsmentioning

confidence: 99%

“…Since smart devices with good performance were penetrated, since high-speed wired and wireless network technologies were developed, and since small-sized display devices with good image quality were developed, people have easily acquired various types of media content, such as pictures, animation, music files, user created contents (UCC), and videos, at any time and any place through the Internet [1][2][3][4][5]. Such acquired media content is usefully applied to various application fields, including big data analysis, image security, deep learning-based artificial intelligence, sustainable anticipatory computing, media content indexing and retrieval, and Internet of Things (IoT) [6][7][8][9][10][11][12].…”

Section: Introductionmentioning

confidence: 99%

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Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Jang

Lee

2020

Sustainability

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High-speed wired and wireless Internet are one of the useful ways to acquire various types of media data easily. In this circumstance, people also can easily get media data including objects with exposed personal information through the Internet. Exposure of personal information emerges as a social issue. This paper proposes an effective blocking technique that makes it possible to robustly detect target objects with exposed personal information from various types of input images with the use of deep neural computing and to effectively block the detected objects' regions. The proposed technique first utilizes the neural computing-based learning algorithm to robustly detect the target object including personal information from an image. It next generates a grid-type mosaic and lets the mosaic overlap the target object region detected in the previous step so as to effectively block the object region that includes personal information. Experimental results reveal that the proposed algorithm robustly detects the target object region with exposed personal information from a variety of input images and effectively blocks the detected region through grid-type mosaic processing. The object blocking technique proposed in this paper is expected to be applied to various application fields such as image security, sustainable anticipatory computing, object tracking, and target blocking.

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“…This study uses color recognition to identify real-time reagents realizing the central tube positioning, color extraction, and classification in a biochemical reaction. Paliwal et al [24] emphasized that image processing methods could be used to calculate the infected percentage in crops and develop elementary machine learning algorithms for classifying the agricultural fields incorporated in the robotic system. The studies included maize, bell pepper, and tomato for image experimentation, leading to the development of algorithms to increase the yield of crops.…”

Section: Introductionmentioning

confidence: 99%

Soil parameter detection of soil test kit-treated soil samples through image processing with crop and fertilizer recommendation

Montanez¹

2021

IJEECS

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<span>Standard laboratory soil testing is deemed to be expensive and time-consuming. Utilizing a soil test kit is considered to be a cost-efficient and time-saving way of soil testing. This project study aims to develop a prototype that detects soil parameters (i.e., soil pH, nitrogen, phosphorus, and potassium) and gives crop and fertilizer recommendations after the soil sample has undergone a soil treatment test kit and its acceptability for possible users. The prototype development primarily used image processing to detect the needed parameters that lead to crop and fertilizer recommendations. In the evaluation of the effectiveness of the prototype, 50 trials were conducted per parameter. All of the said parameters were recorded as highly effective except for nitrogen Low, which is interpreted as effective only. There were 30 possible users invited to assess the acceptability of the prototype. A survey based on the technology acceptance model was administered to the 30 respondents garnering a 4.85 weighted mean interpreted as excellent. The prototype was proven effective and accepted as a device that can detect soil pH and primary macronutrient levels. It gives the appropriate crop and fertilizer recommendations based on the gathered data.</span>

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Image processing-based intelligent robotic system for assistance of agricultural crops

Cited by 18 publications

References 0 publications

Realization of an Intelligent Image Processing Technology for the Analysis of Chinese Painting Color Education

Realization of an Intelligent Image Processing Technology for the Analysis of Chinese Painting Color Education

Robust Blocking of Human Faces with Personal Information Using Artificial Deep Neural Computing

Soil parameter detection of soil test kit-treated soil samples through image processing with crop and fertilizer recommendation

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