• International journal of advanced smart convergence
• /
• v.13 no.3
• /
• pp.335-344
• /
• 2024

This study explores advanced machine learning techniques for improving crop yield prediction in smart farming, utilizing multi-temporal spectral data from drone-based multispectral imagery. Conducted in garlic orchards in Andong, Gyeongbuk Province, South Korea, the research examines the effectiveness of various vegetation indices and cutting-edge models, including LSTM, CNN, Random Forest, and XGBoost. By integrating these models with the Analytic Hierarchy Process (AHP), the study systematically evaluates the factors that influence prediction accuracy. The integrated approach significantly outperforms single models, offering a more comprehensive and adaptable framework for yield prediction. This research contributes to precision agriculture by providing a robust, AI-driven methodology that enhances the sustainability and efficiency of farming practices.

• Applied Biological Chemistry
• /
• v.48 no.3
• /
• pp.252-257
• /
• 2005

Three-dimensional quantitative structure-activity relationships (3D-QSARs) for the herbicidal activities against pre-emergence barnyard grass (Echinochloa crus-galli) by new 2-(4-(6-chloro-2-benzoxazolyloxy)phenoxy)-N-phenylpropion amide derivatives were studied quantitatively using comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) methodologies. The best CoMFA model (AI-2) and CoMSIA model (AII-4) were derived from an atom based fit alignment and a combination of CoMFA fields. The herbicidal activities from CoMFA and CoMSIA contour maps showed that the activity will be able to be increased according to the substituents variation on the N-phenyl ring.

• Electronics and Telecommunications Trends
• /
• v.36 no.1
• /
• pp.54-63
• /
• 2021

Since the recent COVID-19 pandemic, countries have been strengthening trade protection for their security, and the importance of securing strategic materials, such as food, is drawing attention. In addition to the cultural aspects, the global preference for food produced in Korea is increasing because of the Korean Wave. Thus, the Korean food industry can be developed into a high-value-added export food industry. Currently, Korea has a low self-sufficiency rate for foodstuffs apart from rice. Korea also suffers from problems arising from population decline, aging, rapid climate change, and various animal and plant diseases. It is necessary to develop technologies that can overcome the production structures highly dependent on the outside world of food and foster them into export-type system industries. The global agricultural industry-related technologies are actively being modified via data accumulation, e.g., environmental data, production information, and distribution and consumption information in climate and production facilities, and by actively expanding the introduction of the latest information and communication technologies such as big data and artificial intelligence. However, long-term research and investment should precede the field of living organisms. Compared to other industries, it is necessary to overcome poor production and labor environment investment efficiency in the food industry with respect to the production cost, equipment postmanagement, development tailored to the eye level of field workers, and service models suitable for production facilities of various sizes. This paper discusses the flow of domestic and international technologies that form the core issues of the site centered on the 4th Industrial Revolution in the field of agriculture, livestock, and fisheries. It also explains the environmental awareness production technologies centered on sustainable intelligence platforms that link climate change responses, optimization of energy costs, and mass production for unmanned production, distribution, and consumption using the unstructured data obtained based on detection and growth measurement data.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.65 no.6
• /
• pp.15-22
• /
• 2023

In this study, We conduct a comparative study of deep learning-based classification of agricultural field attributes using Tagged Image File (TIF) and Enhanced Compression Wavelet (ECW) images. The goal is to interpret and classify the attributes of agricultural fields by analyzing the differences between these two image formats. "FarmMap," initiated by the Ministry of Agriculture, Food and Rural Affairs in 2014, serves as the first digital map of agricultural land in South Korea. It comprises attributes such as paddy, field, orchard, agricultural facility and ginseng cultivation areas. For the purpose of comparing deep learning-based agricultural attribute classification, we consider the location and class information of objects, as well as the attribute information of FarmMap. We utilize the ResNet-50 instance segmentation model, which is suitable for this task, to conduct simulated experiments. The comparison of agricultural attribute classification between the two images is measured in terms of accuracy. The experimental results indicate that the accuracy of TIF images is 90.44%, while that of ECW images is 91.72%. The ECW image model demonstrates approximately 1.28% higher accuracy. However, statistical validation, specifically Wilcoxon rank-sum tests, did not reveal a significant difference in accuracy between the two images.

• Journal of Information Processing Systems
• /
• v.15 no.5
• /
• pp.1082-1095
• /
• 2019

Camera calibration is an important part of machine vision and close-range photogrammetry. Since current calibration methods fail to obtain ideal internal and external camera parameters with limited computing resources on mobile terminals efficiently, this paper proposes an improved fast camera calibration method for mobile terminals. Based on traditional camera calibration method, the new method introduces two-order radial distortion and tangential distortion models to establish the camera model with nonlinear distortion items. Meanwhile, the nonlinear least square L-M algorithm is used to optimize parameters iteration, the new method can quickly obtain high-precise internal and external camera parameters. The experimental results show that the new method improves the efficiency and precision of camera calibration. Terminals simulation experiment on PC indicates that the time consuming of parameter iteration reduced from 0.220 seconds to 0.063 seconds (0.234 seconds on mobile terminals) and the average reprojection error reduced from 0.25 pixel to 0.15 pixel. Therefore, the new method is an ideal mobile terminals camera calibration method which can expand the application range of 3D reconstruction and close-range photogrammetry technology on mobile terminals.

• The Journal of the Korea institute of electronic communication sciences
• /
• v.18 no.5
• /
• pp.999-1008
• /
• 2023

Early prediction of chronic diseases such as diabetes is an important issue, and improving the accuracy of diabetes prediction is especially important. Various machine learning and deep learning-based methodologies are being introduced for diabetes prediction, but these technologies require large amounts of data for better performance than other methodologies, and the learning cost is high due to complex data models. In this study, we aim to verify the claim that DNN using the pima dataset and k-fold cross-validation reduces the efficiency of diabetes diagnosis models. Machine learning classification methods such as decision trees, SVM, random forests, logistic regression, KNN, and various ensemble techniques were used to determine which algorithm produces the best prediction results. After training and testing all classification models, the proposed system provided the best results on XGBoost classifier with ADASYN method, with accuracy of 81%, F1 coefficient of 0.81, and AUC of 0.84. Additionally, a domain adaptation method was implemented to demonstrate the versatility of the proposed system. An explainable AI approach using the LIME and SHAP frameworks was implemented to understand how the model predicts the final outcome.

• Korean Journal of Weed Science
• /
• v.17 no.3
• /
• pp.325-333
• /
• 1997

Selection of pepper (Capsicum sp.) cultivars tolerant to acetolactate synthase (ALS)-inhibiting herbicides {imazethapyr, 2-[4,5-dihydro-4-methyl-4-(1-methylethyl)-5-oxo-1H-imidazol-2-yl]-5-ethyl-3=pyridine-carboxylic acid, and primisulfuron methyl 2-[[[[[4,6-bis(difluoromethoxy)-2-pyrimidinyl]amino] carbonyl]amino]sulfonyl]benzoate} was investigated. Pepper cultivars such as Red Top, Happy Dry, Golden Tower, and Hagyeorae showed relatively tolerant response to imazethapyr, while cultivars; Korea, Cheongyang, Oriental Glory, and Hanam were susceptible. Red Horn, Jopoong, Kwangbok, and Wangcho cultivars were tolerant to primisulfuron whereas Korea, Dahhong, Chamjoah, and Poongchon cultivars were susceptible. Determination of growth inhibition by ALS-inhibiting herbicides showed that the $I_{50}$ estimates of growth from the susceptible- and tolerant-cultivars were 0.075 and 0.20kg ai/ha for imazethapyr; 0.06 and 0.16kg ai/ha for primisulfuron, respectively. Furthermore, the $GR_{50}$ estimates of growth from the susceptible and tolerant cultivars were 0.05 and 0.20kg ai/ha for imazethapyr; 0.07 and 0.16kg ai/ha for primisulfuron, respectively. This result, based on the $GR_{50}$ and $I_{50}$ values, indicates that responses of pepper to ALS-inhibiting herbicides between tolerant- and susceptible-cultivars were different about 3- to 4-fold to imazethapyr, and 2- to 3-fold to primisulfuron.

• Korean Journal of Environmental Agriculture
• /
• v.11 no.2
• /
• pp.146-154
• /
• 1992

DDVP, malathion and diazinon ECs which differ in chemical compositions and moisture contents were formulated with nine emulsifiers, three solvents(xylene, cyclohexanone and DMF) and epichlorohydrin. For the studies of decomposition characteristics, these technicals and ECs were subjected to the test under elevated temperature at $54^{\circ}C$ for 15 days and $38^{\circ}C$ for 90 days respectively. DDVP technical was rapidly decomposed in early stage of thermoaccelerated test at $54^{\circ}C$, but the decomposition rate slowed down with time. As for malathion and diazinon technicals, the longer they were incubated, the more decomposed. The decomposed AI in ECs increased with solvent polarity. The increment of moisture content in ECs accelerated the decomposition of AI, and that was remarkable especially in diazinon ECs. Addition of emulsifiers increased the moisture content to be accelerated the decomposition of AI, but the decomposition of AI was more affected by the kind of emulsifier than by the moisture content of emulsifier, Stabilizing effect by epichlorohydrin was distingished in malathion and diazinon ECs, but there was no effect in other solvent-based formulation except xylene.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
• /
• v.13 no.1
• /
• pp.145-155
• /
• 2018

The matter of cultivating entrepreneurship and will to start a business of university students majoring in agriculture and life sciences and college students majoring in agriculture as a future leader in the sector is a very important object of study. However, the discussion on entrepreneurship, establishment of a business and venture based on creative technology and innovative management have been scarcely had, because traditionally the majority of agricultural business has been a small-sized and simple business run by a small farmer. Education for starting an agricultural business in agriculture industry has been ignored even in the developed countries. ICT and AI(artificial intelligence)-based smart agriculture in the 4th Industrial Revolution Age is emerging as a new growth potential of our agriculture industry. Thus, the interest in farmers to start a business and venture agriculture is growing in the agriculture industry. Accordingly, the study draws the influence factors regarding the effect of the planned behavior of the university students who take part in the education course for starting an agricultural business and an agricultural venture business on entrepreneurship and will to start the business and conducts the empirical analysis. The businessmen who newly join the agriculture industry should perform the technical innovation and the creative business activities to be able to compete in the agriculture industry.

• Korean Journal of Weed Science
• /
• v.32 no.3
• /
• pp.230-239
• /
• 2012

Coconut (Cocos nucifera L) is one of the predominant plantation crops in Sri Lankan economy which is known to have existed for over thousands of years. During the past decades coconut production had been reduced by a significant quantity. The usage of poor quality planting materials is a major reason for the low coconut production. Thus much attention needs to be paid in coconut nurseries. Weed management is a critical management practice in the nursery. Though glyphosate application is becoming popular in nurseries it can affect weeds as well as coconut seedlings growth. Therefore the effects of glyphosate were evaluated by determining the growth of shoot and root of coconut seedlings. Poly bag nursery was prepared and three treatments were used. Treatments were no glyphosate and manual weeding ($T_1$), application of glyphosate 1.08 ai kg $ha^{-1}$ at 2 monthly interval ($T_2$) and application of glyphosate 1.44 ai kg $ha^{-1}$ at 2 monthly interval ($T_3$). Application of glyphosate at early stage of seedling growth had a no significant effect on growth parameters tested. However, the concentrations of glyphosate negatively affected numbers, volumes and dry weights of secondary, tertiary and quaternary roots at the latter stage of seedling growth. The leaf area and the height of seedling were significantly reduced by the highest concentration of glyphosate. Among the growth parameters tested, seedling girth and shoot dry weight were not affected by the application of glyphosate. These results revealed that the usage of glyphosate at both concentrations negatively affected root growth of coconut seedlings. Based on these results, the both concentration levels of glyphosate should be applied to coconut nurseries before sprouting the seed nuts.