• Journal of Climate Change Research
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• v.7 no.2
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• pp.193-203
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• 2016

The importance of forecasting agricultural production is receiving attention while climate change is accelerating. This study suggested three types of crop yield forecasting model for major vegetable crops by using downscaled meteorological information of main production area on farmland level, which identified as limitation from previous studies. First, this study conducted correlation analysis with seven types of farm level downscaled meteorological informations and reported crop yield of main production area. After, we selected three types of meteorological factors which showed the highest relation with each crop species and regions. Parameters were deducted from meterological factor with high correlation but crop species number was neglected. After, crop yield of each crops was estimated by using the three suggested types of models. Chinese cabbage showed high accuracy in overall, while the accuracy of daikon and onion was quiet revised by neglecting the outlier. Chili and garlic showed differences by region, but Kyungbuk chili and Chungnam, Kyungsang garlic appeared significant accuracy. We also selected key meteorological factor of each crops which has the highest relation with crop yield. If the factor had significant relation with the quantity, it explains better about the variations of key meteorological factor. This study will contribute to establishing the methodology of future studies by estimating the crop yield of different species by using farmland meterological information and relatively simplify multiple linear regression models.

• Smart Media Journal
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• v.12 no.10
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• pp.29-37
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• 2023

Human lifestyle is affected by the agricultural development in the last 12,000 years ago. The development of agriculture is one of the reasons that global population surged. To ensure sufficient food production for supporting human life, pesticides as a more effective and economical tools, are extensively used to enhance the yield quality and boost crop production. This study investigated the factors that affect crop production and whether the factors of pesticide usage are the most important factors in crop production using the dataset from Kaggle that provides information based on crops harvested by various farmers. Logistic regression is used to investigate the relationship between various factors and crop production. However, the logistic regression is unable to deal with predictors that are related to each other and identifying the greatest impact factor. Therefore, causal discovery is applied to address the above limitations. The result of causal discovery showed that crop condition is greatly impacted by the estimated insects count, where estimated insects count is affected by the factors of pesticide usage. This study enhances our understanding of the influence of pesticide usage on crop production and contributes to the progress of agricultural practices.

• Proceedings of the Korean Society of Crop Science Conference
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• 2018.10a
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• pp.251-251
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• 2018

• Proceedings of the Korean Society of Crop Science Conference
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• 2018.10a
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• pp.253-253
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• 2018

• Proceedings of the Korean Society of Crop Science Conference
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• 2018.10a
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• pp.254-254
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• 2018

• Proceedings of the Korean Society of Crop Science Conference
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• 2018.10a
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• pp.255-255
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• 2018

• Journal of The Korean Society of Agricultural Engineers
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• v.47 no.7
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• pp.3-9
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• 2005

A computer system for crop information was developed using Visual Basic and ArcGIS VBA. The system is operated on ArcGIS 8.3 with Microsoft Access MDB. Landsat +ETM, KOMPSAT-1 EOC, ASTER VNIR and IKONOS panchromatic (Pan) and multi-spectral (MIS) images were included in the system to extract agricultural land use items identifiable at various spatial resolutions of images. Agriculture related data inventories using crop cover information such as texture and average pixel value of each band based on crop cultivation calendar were designed and implemented. Three IKONOS images were loaded in the system to show crop cover characteristics such as rice, pear, grape, red pepper, garlic, and surface water cover of reservoir with field surveys. GIS layers such as DEM (Digital Elevation Model), stream, road, soil, land use and administration boundary were prepared to understand the related characteristics and identify the location easily.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1199-1206
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• 2021

Accordingly, attention is also being paid to the agricultural use of remote sensing technique that non-destructively and continuously detects the growth and physiological status of crops. However, when remote sensing techniques are used for crop monitoring, it is possible to continuously monitor the abnormality of crops in real time. For this, standard growth information of crops is required and relative growth considering the cultivation environment must be identified. With the relationship between GDD (Growing Degree Days), which is the cumulative temperature related to crop growth obtained from ideal cultivation management, and the vegetation index as standard growth information, compared with the vegetation index observed with the spectralreflectance sensor(SRS_NDVI & SRS_PRI) in each rice paddy treated with standard cultivation management and non-fertilized, it was quantitatively identified as a time series. In the future, it is necessary to accumulate a database targeting various climatic conditions and varieties in the standard cultivation management area to establish a more reliable standard growth information.

• Journal of Information Processing Systems
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• v.20 no.2
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• pp.149-158
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• 2024

Traditional manual identification of crop leaf diseases is challenging. Owing to the limitations in manpower and resources, it is challenging to explore crop diseases on a large scale. The emergence of artificial intelligence technologies, particularly the extensive application of deep learning technologies, is expected to overcome these challenges and greatly improve the accuracy and efficiency of crop disease identification. Crop leaf disease identification models have been designed and trained using large-scale training data, enabling them to predict different categories of diseases from unlabeled crop leaves. However, these models, which possess strong feature representation capabilities, require substantial training data, and there is often a shortage of such datasets in practical farming scenarios. To address this issue and improve the feature learning abilities of models, this study proposes a deep transfer learning adaptation strategy. The novel proposed method aims to transfer the weights and parameters from pre-trained models in similar large-scale training datasets, such as ImageNet. ImageNet pre-trained weights are adopted and fine-tuned with the features of crop leaf diseases to improve prediction ability. In this study, we collected 16,060 crop leaf disease images, spanning 12 categories, for training. The experimental results demonstrate that an impressive accuracy of 98% is achieved using the proposed method on the transferred ResNet-50 model, thereby confirming the effectiveness of our transfer learning approach.

• Agribusiness and Information Management
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• v.10 no.2
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• pp.8-17
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• 2018

Due to changes in the agricultural market environment and both overseas and domestic farming conditions, uncertainties in agricultural production and management are becoming greater. Hence, there is a stronger need for farmers to choose crops in the optimal condition. This research aims to introduce the result and process of developing a decision support system for selecting crops, aimed to assist farmers in selecting the optimal crops most suitable in the given situation. There are basically three main factors to consider in the decision-making process for farmers when selecting a crop to introduce to their lands. First of all, one must consider how much profit crop A will produce when it is cultivated. Secondly, one must consider which crop to cultivate in order to earn a certain amount of profit. Thirdly, one must consider what is the best way to maximize Farm A's business profit. For instance, a farm may have land as its resource, and one must research which location, type of crop, level of technology, and so forth, to maximize profit.This research creates a database of the profitability of a total of 180 crop types by analyzing Rural Development Administration's survey of agricultural products income of 115 crop types, small land profitability index survey of 53 crop types, and Statistics Korea's survey of production costs of 12 crop types. Furthermore, this research presents the result and developmental process of a web-based crop introduction decision support system that provides overseas cases of new crop introduction support programs, as well as databases of outstanding business success cases of each crop type researched by agricultural institutions.