• KOREAN JOURNAL OF CROP SCIENCE
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• v.64 no.2
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• pp.159-164
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• 2019

Weed control is a crucial practice not only in organic farming, but also in modern agriculture because it can lead to loss in crop yield. In general, weed is distributed in patches heterogeneously in the field. These patches vary in size, shape, and density. Thus, it would be efficient if chemicals are sprayed on these patches rather than spraying uniformly in the field, which can pollute the environment and be cost prohibitive. In this sense, weed detection could be beneficial for sustainable agriculture. Studies have been conducted to detect weed patches in the field using remote sensing technologies, which can be classified into a method using image segmentation based on morphology and a method with vegetative indices based on the wavelength of light. In this study, the latter methodology has been used to detect the weed patches. As a result, it was found that the vegetative indices were easier to operate as it did not need any sophisticated algorithm for differentiating weeds from crop and soil as compared to the former method. Consequently, we demonstrated that the current method of using vegetative index is accurate enough to detect weed patches, and will be useful for farmers to control weeds with minimal use of chemicals and in a more precise manner.

• International Journal of Computer Science & Network Security
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• v.23 no.7
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• pp.155-164
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• 2023

Machine learning with its high precision algorithms, Precision agriculture (PA) is a new emerging concept nowadays. Many researchers have worked on the quality and quantity of PA by using sensors, networking, machine learning (ML) techniques, and big data. However, there has been no attempt to work on trends of artificial intelligence (AI) techniques, dataset and crop type on precision agriculture using internet of things (IoT). This research aims to systematically analyze the domains of AI techniques and datasets that have been used in IoT based prediction in the area of PA. A systematic literature review is performed on AI based techniques and datasets for crop management, weather, irrigation, plant, soil and pest prediction. We took the papers on precision agriculture published in the last six years (2013-2019). We considered 42 primary studies related to the research objectives. After critical analysis of the studies, we found that crop management; soil and temperature areas of PA have been commonly used with the help of IoT devices and AI techniques. Moreover, different artificial intelligence techniques like ANN, CNN, SVM, Decision Tree, RF, etc. have been utilized in different fields of Precision agriculture. Image processing with supervised and unsupervised learning practice for prediction and monitoring the PA are also used. In addition, most of the studies are forfaiting sensory dataset to measure different properties of soil, weather, irrigation and crop. To this end, at the end, we provide future directions for researchers and guidelines for practitioners based on the findings of this review.

• KOREAN JOURNAL OF CROP SCIENCE
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• v.68 no.1
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• pp.20-26
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• 2023

Fiber content in the storage roots of sweetpotato varies between different varieties. For examples, the high fiber content of certain types has a poor texture when steamed or roasted. This study was conducted to evaluate the optimal sieve mesh size for separating fibers, the chemical composition of fibers and differences in fiber content among different varieties. We found that the separated fiber content (dry weight) of mashed and steamed sweetpotato was higher after washing three times (143.3 mg/100 g) compared with that washed five times (128.4 mg/100 g). The Hogammi variety remained 85.9% of total fiber content at 10 mesh (2,000 ㎛) and 9.6% of total fiber content at 30 mesh (600 ㎛), and Jinyulmi remained 74.9 and 16.7% of total fiber content , respectively. Therefore, a 30 mesh sieve was considered the most suitable for fiber separation. Among the 10 studied cultivars, Jinhongmi showed the lowest amount of fiber (24.8 mg/100 g) and Hogammi had the highest amount (111.4 mg/100 g), which was 4.5 times larger than that of Jinhongmi. Cellulose, hemicellulose and lignin content of separated fibers showed no difference between the viscous-type Hogammi and powdery-type Jinyulmi varieties, with averages of 32.5, 22.3 and 29.6%, respectively. Correlation results using the Image J program showed a significant correlation between the distribution of the stained area and the fiber content (R = 0.74, p < 0.05). Staining distribution differed among varieties, suggesting that a simple fiber content test could be performed using the staining method on raw sweetpotato. These results provide useful information to help inform farmers on the fiber content of different sweetpotato varieties.

• International journal of advanced smart convergence
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• v.12 no.3
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• pp.104-108
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• 2023

This paper deals with research on innovative systems using Python-based artificial intelligence technology in the field of plant growth monitoring. The importance of monitoring and analyzing the health status and growth environment of plants in real time contributes to improving the efficiency and quality of crop production. This paper proposes a method of processing and analyzing plant image data using computer vision and deep learning technologies. The system was implemented using Python language and the main deep learning framework, TensorFlow, PyTorch. A camera system that monitors plants in real time acquires image data and provides it as input to a deep neural network model. This model was used to determine the growth state of plants, the presence of pests, and nutritional status. The proposed system provides users with information on plant state changes in real time by providing monitoring results in the form of visual or notification. In addition, it is also used to predict future growth conditions or anomalies by building data analysis and prediction models based on the collected data. This paper is about the design and implementation of Python-based plant growth monitoring systems, data processing and analysis methods, and is expected to contribute to important research areas for improving plant production efficiency and reducing resource consumption.

• International Journal of Computer Science & Network Security
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• v.23 no.6
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• pp.115-120
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• 2023

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• International Journal of Computer Science & Network Security
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• v.24 no.4
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• pp.107-112
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• 2024

Plant leaf diseases and destructive insects are major challenges that affect the agriculture production of the country. Accurate and fast prediction of leaf diseases in crops could help to build-up a suitable treatment technique while considerably reducing the economic and crop losses. In this paper, Convolutional Neural Network based model is proposed to detect leaf diseases of a plant in an efficient manner. Convolutional Neural Network (CNN) is the key technique in Deep learning mainly used for object identification. This model includes an image classifier which is built using machine learning concepts. Tensor Flow runs in the backend and Python programming is used in this model. Previous methods are based on various image processing techniques which are implemented in MATLAB. These methods lack the flexibility of providing good level of accuracy. The proposed system can effectively identify different types of diseases with its ability to deal with complex scenarios from a plant's area. Predictor model is used to precise the disease and showcase the accurate problem which helps in enhancing the noble employment of the farmers. Experimental results indicate that an accuracy of around 93% can be achieved using this model on a prepared Data Set.

• Journal of Broadcast Engineering
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• v.26 no.1
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• pp.26-38
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• 2021

In this paper, DLKF (Deep Learning Keypoint Filtering), the deep learning-based keypoint filtering method for the rapidization of the image registration method for remote sensing images is proposed. The complexity of the conventional feature-based image registration method arises during the feature matching step. To reduce this complexity, this paper proposes to filter only the keypoints detected in the artificial structure among the keypoints detected in the keypoint detector by ensuring that the feature matching is matched with the keypoints detected in the artificial structure of the image. For reducing the number of keypoints points as preserving essential keypoints, we preserve keypoints adjacent to the boundaries of the artificial structure, and use reduced images, and crop image patches overlapping to eliminate noise from the patch boundary as a result of the image segmentation method. the proposed method improves the speed and accuracy of registration. To verify the performance of DLKF, the speed and accuracy of the conventional keypoints extraction method were compared using the remote sensing image of KOMPSAT-3 satellite. Based on the SIFT-based registration method, which is commonly used in households, the SURF-based registration method, which improved the speed of the SIFT method, improved the speed by 2.6 times while reducing the number of keypoints by about 18%, but the accuracy decreased from 3.42 to 5.43. Became. However, when the proposed method, DLKF, was used, the number of keypoints was reduced by about 82%, improving the speed by about 20.5 times, while reducing the accuracy to 4.51.

• Journal of the Korean Association of Geographic Information Studies
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• v.22 no.4
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• pp.86-101
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• 2019

Remote sensing technology has been continuously developed both quantitatively and qualitatively, including platform development, exploration area, and exploration functions. Recently, the use cases and related researches in the agricultural field are increasing. Also, since it is possible to detect and quantify the condition of cropland and establish management plans and policy support for cropland and agricultural environment, it is being studied in various fields such as crop growth abnormality determination and crop estimation based on time series information. The purpose of this study was to analyze the vegetation index for agricultural land reclamation area using a UAV equipped with a multi-spectral sensor. In addition, field surveys were conducted to evaluate the accuracy of vegetation indices calculated from multispectral image data obtained using UAV. The most appropriate vegetation index was derived by evaluating the correlation between vegetation index calculated by field survey and vegetation index calculated from UAV multispectral image, and was used to analyze vegetation index of the entire area.

• Journal of Bio-Environment Control
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• v.29 no.4
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• pp.381-387
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• 2020

Various studies have been attempted to estimate and measure the fresh weight of crops. However, no studies have used raw images of sweet peppers to estimate fresh weight. Recently, image processing research using convolution neural network (CNN) that can use raw data is increasing. In this study, the crop fresh weight was estimated by using the images of sweet peppers as inputs of CNN. The experiment was performed in a greenhouse growing sweet pepper (Capsicum annuum L.). The fresh weight, the output of the CNN, was regressed based on the data collected through destructive investigation. The highest coefficient of determination (R²) of the trained CNN was 0.95. The estimated fresh weight showed a very similar trend to the actual measured value.

• Agricultural and Biosystems Engineering
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• v.6 no.2
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• pp.47-53
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• 2005

AMachine vision is a promising tool for the autonomous guidance of farm machinery. Conventional CCD camera for the machine vision needs a desktop PC to install a frame grabber, however, a web camera is ready to use when plugged in the USB port. A web camera with a notebook PC can replace existing camera system. Autonomous steering control system of this research was intended to be used for combine harvester. If the web camera can recognize cut/uncut edge of crop, which will be the reference for steering control, then the position of the machine can be determined in terms of lateral offset and heading angle. In this research, a white line was used as a cut/uncut edge of crop for steering control. Image processing algorithm including capturing image in the web camera was developed to determine the desired travel path. An experimental vehicle was constructed to evaluate the system performance. Since the vehicle adopted differential drive steering mechanism, it is steered by the difference of rotation speed between left and right wheels. According to the position of vehicle, the steering algorithm was developed as well. Evaluation tests showed that the experimental vehicle could travel within an RMS error of 0.8cm along the desired path at the ground speed of $9\sim41cm/s$. Even when the vehicle started with initial offsets or tilted heading angle, it could move quickly to track the desired path after traveling $1.52\sim3.5m$. For turning section, i.e., the curved path with curvature of 3 m, the vehicle completed its turning securely.