• KIPS Transactions on Software and Data Engineering
• /
• v.12 no.2
• /
• pp.77-82
• /
• 2023

The Ministry of Agriculture, Food and Rural Affairs established the FarmMap, an digital map of agricultural land. In this study, using deep learning, we suggest the application of farm map reading to farmland such as paddy fields, fields, ginseng, fruit trees, facilities, and uncultivated land. The farm map is used as spatial information for planting status and drone operation by digitizing agricultural land in the real world using aerial and satellite images. A reading manual has been prepared and updated every year by demarcating the boundaries of agricultural land and reading the attributes. Human reading of agricultural land differs depending on reading ability and experience, and reading errors are difficult to verify in reality because of budget limitations. The farmmap has location information and class information of the corresponding object in the image of 5 types of farmland properties, so the suitable AI technique was tested with ResNet50, an instance segmentation model. The results of attribute reading of agricultural land using deep learning and attribute reading by humans were compared. If technology is developed by focusing on attribute reading that shows different results in the future, it is expected that it will play a big role in reducing attribute errors and improving the accuracy of digital map of agricultural land.

• Smart Media Journal
• /
• v.11 no.4
• /
• pp.30-37
• /
• 2022

Disease threatens plant growth and recognizing the type of disease is essential to making a remedy. In recent years, deep learning has witnessed a significant improvement for this task, however, a large volume of labeled images is one of the requirements to get decent performance. But annotated images are difficult and expensive to obtain in the agricultural field. Therefore, designing an efficient and effective strategy is one of the challenges in this area with few labeled data. Transfer learning, assuming taking knowledge from a source domain to a target domain, is borrowed to address this issue and observed comparable results. However, current transfer learning strategies can be regarded as a supervised method as it hypothesizes that there are many labeled images in a source domain. In contrast, unsupervised transfer learning, using only images in a source domain, gives more convenience as collecting images is much easier than annotating. In this paper, we leverage unsupervised transfer learning to perform plant disease recognition, by which we achieve a better performance than supervised transfer learning in many cases. Besides, a vision transformer with a bigger model capacity than convolution is utilized to have a better-pretrained feature space. With the vision transformer-based unsupervised transfer learning, we achieve better results than current works in two datasets. Especially, we obtain 97.3% accuracy with only 30 training images for each class in the Plant Village dataset. We hope that our work can encourage the community to pay attention to vision transformer-based unsupervised transfer learning in the agricultural field when with few labeled images.

• Proceedings of the Korean Society for Agricultural Machinery Conference
• /
• 2000.11b
• /
• pp.124-131
• /
• 2000

In this study, the NEURAL NETWORK (hereinafter referred to as NN) was applied to control of the nonlinear factors for turning movement of the crawler vehicle and experiment was carried out using a small model of crawler vehicle in order to inspect an application of NN. Furthermore, CHAOS NEURAL NETWORK (hereinafter referred to as CNN) was also applied to this control so as to compare with conventional NN. CNN is especially effective for plane in many variables with local minimum which conventional NN is apt to fall into, and it is relatively useful to nonlinear factors. Experiment of turning on the slope of crawler vehicle was performed in order to estimate an adaptability of nonlinear problems by NN and CNN. The inclination angles of the road surface which the vehicles travel on, were respectively 4deg, 8deg, 12deg. These field conditions were selected by the object for changing nonlinear magnitude in turning phenomenon of vehicle. Learning of NN and CNN was carried out by referring to positioning data obtained from measurement at every 15deg in turning. After learning, the sampling data at every 15deg were interpolated based on the constructed learning system of NN and CNN. Learning and simulation programs of NN and CNN were made by C language ("Association of research for algorithm of calculating machine (1992)"). As a result, conventional NN and CNN were available for interpolation of sampling data. Moreover, when nonlinear intensity is not so large under the field condition of small slope, interpolation performance of CNN was a little not so better than NN. However, when nonlinear intensity is large under the field condition of large slope, interpolation performance of CNN was relatively better than NN.

• Journal of Information Processing Systems
• /
• v.17 no.6
• /
• pp.1115-1126
• /
• 2021

Plant disease is one of the most irritating problems for agriculture growers. Thus, timely detection of plant diseases is of high importance to practical value, and corresponding measures can be taken at the early stage of plant diseases. Therefore, numerous researchers have made unremitting efforts in plant disease identification. However, this problem was not solved effectively until the development of artificial intelligence and big data technologies, especially the wide application of deep learning models in different fields. Since the symptoms of plant diseases mainly appear visually on leaves, computer vision and machine learning technologies are effective and rapid methods for identifying various kinds of plant diseases. As one of the fruits with the highest nutritional value, apple production directly affects the quality of life, and it is important to prevent disease intrusion in advance for yield and taste. In this study, an improved deep residual network is proposed for apple leaf disease identification in a novel way, a global residual connection is added to the original residual network, and the local residual connection architecture is optimized. Including that 1,977 apple leaf disease images with three categories that are collected in this study, experimental results show that the proposed method has achieved 98.74% top-1 accuracy on the test set, outperforming the existing state-of-the-art models in apple leaf disease identification tasks, and proving the effectiveness of the proposed method.

• Journal of Agricultural Extension & Community Development
• /
• v.27 no.1
• /
• pp.17-31
• /
• 2020

The study aims to identify the moderating effects of involvement on satisfaction, recommendation, and revisit intention in insect exhibition. Tourism motivation and involvement are crucial factors influencing visitors satisfaction. Particularly, the study aim to examine the moderating effects on tourism involvement between tourism motivation and visitors' satisfaction. Data were collected from 346 usable questionnaires among visitors of the pet insect competition. Results of a factor analysis yielded three dimensions of tourism motivation which are insect experience/learning, recreation/escape, and social and friendship. Hierarchical regression analysis indicates that insect experience/learning motivation and recreation/escape motivation influence visitors' satisfaction, Results also show that visitors' involvement has moderating effects on satisfaction. It was suggested that visitors' motivation and involvement be considered in insect exhibition and events.

• Journal of Agricultural Extension & Community Development
• /
• v.8 no.2
• /
• pp.235-244
• /
• 2001

The objectives of this study were to inquire the theories associated with YLD (Youth Leadership Development), and to draw implications for improving youth leadership abilities, The results of the inquiry revealed the theories associated with YLD as follows; 1. All youth have leadership potential and abilities, but there were few programs to improve it. 2. Activity-Observation-Reflection model of Hughes, Ginnett & Curphy(1993) and Awareness-Interaction-Mastery model of Linden & Fertman(1998) were the best effective YLD models. 3. Situational contingency approach was very appropriate theory associated with YLD. 4. The learning of leadership skills had occurred within an educational context known as experiential learning, so it was the best method of YLD.

• Journal of information and communication convergence engineering
• /
• v.18 no.3
• /
• pp.167-175
• /
• 2020

The net income per unit area from crop production could be the most critical consideration for agricultural producers during cultivation planning. This paper proposes a scheme for predicting the net income per unit area based on machine learning and related calculations. This scheme predicts rice production and operation costs by applying climate and price index data. The rice price is also predicted by applying rice production and operation cost data. Finally, these predicted results are employed to calculate the predicted net income, which is compared with the actual net income. Consequently, the proposed scheme shows a meaningful degree of conformity, which indicates the potential of machine learning for predicting various aspects of agricultural production.

• Journal of Agricultural Extension & Community Development
• /
• v.17 no.1
• /
• pp.45-74
• /
• 2010

The purposes of this study were to analyze the farmer education programs in Korea and to develop the strategies for improving its effectiveness. Questionnaires were used to collect data from institutes of farmer education. The framework of analysis was developed with four survey areas and 328 programs were analyzed based on it. The results show that the major learning domains of farmer education were concentrated on production skills and were overlapped with other training institutes. There was no systemic structure in whole programs for farmers. Evaluations for learning achievement were not fully implemented. It was recommended that there should be a system for improving quality of farmer education.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.61 no.3
• /
• pp.55-65
• /
• 2019

Although crack on concrete exists from its early formation, crack requires attention as it affects stiffness of structure and can lead demolition of structure as it grows. Detecting cracks on concrete is needed to take action prior to performance degradation of structure, and deep learning can be utilized for it. In this study, transfer learning, one of the deep learning techniques, was used to detect the crack, as the amount of crack's image data was limited. Pre-trained Inception-v3 was applied as a base model for the transfer learning. Web scrapping was utilized to fetch images of concrete wall with or without crack from web. In the recognition of crack, image post-process including changing size or removing color were applied. In the visualization of crack, source images divided into 30px, 50px or 100px size were used as input data, and different numbers of input data per category were applied for each case. With the results of visualized crack image, false positive and false negative errors were examined. Highest accuracy for the recognizing crack was achieved when the source images were adjusted into 224px size under gray-scale. In visualization, the result using 50 data per category under 100px interval size showed the smallest error. With regard to the false positive error, the best result was obtained using 400 data per category, and regarding to the false negative error, the case using 50 data per category showed the best result.

• Journal of Agricultural Extension & Community Development
• /
• v.7 no.2
• /
• pp.327-331
• /
• 2000

The purposes of this study were to analyze achievement of the Korea Agricultural Extension Specialist Association and suggest new directions for further development. The Korea Agricultural Extension Specialist Association was organized to develop competencies in specialized field for extension educators in 1996. About 47% of extension educators joined academy during the 5 year period, even though there were many difficulty in organizing extension educators, the Korea Agricultural Extension Specialist Association has achieved to increase independent learning activity, to share new information and improve agent’s competency. New directions for the future development of the Korea Agricultural Extension Specialist Association activities should include the following measures; 1) Academy manages to help member’s competency, 2) Every member of the Association tries to make a concrete goal of activity, 3) The Association members should find ways to enable farmers to remain informed on agricultural extension services to people.