• Korean Journal of Agricultural Science
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• v.50 no.3
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• pp.485-496
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• 2023

In this study, we aimed to apply multispectral imaging (713 - 920 nm, 10 bands) for maturity classification and recognition of oriental melons grown in hydroponic greenhouses. A total of 20 oriental melons were selected, and time series multispectral imaging of oriental melons was 7 - 9 times for each sample from April 21, 2023, to May 12, 2023. We used several approaches, such as Savitzky-Golay (SG), standard normal variate (SNV), and Combination of SG and SNV (SG + SNV), for pre-processing the multispectral data. As a result, 713 - 759 nm bands were preprocessed with SG for the maturity classification of oriental melons. Additionally, a Light Gradient Boosting Machine (LightGBM) was used to train the recognition model for oriental melon. R² of recognition model were 0.92, 0.91 for the training and validation sets, respectively, and the F-scores were 96.6 and 79.4% for the training and testing sets, respectively. Therefore, multispectral imaging in the range of 713 - 920 nm can be used to classify oriental melons maturity and recognize their fruits.

• Korean Journal of Agricultural Science
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• v.46 no.2
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• pp.381-393
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• 2019

A smart farm is a system that combines information and communication technology (ICT), internet of things (IoT), and agricultural technology that enable a farm to operate with minimal labor and to automatically control of a greenhouse environment. Machine learning based on recently data-driven techniques has emerged with big data technologies and high-performance computing to create opportunities to quantify data intensive processes in agricultural operational environments. This paper presents research on the application of machine learning technology to diagnose the growth status of crops and predicting the harvest time of strawberries in a greenhouse according to image processing techniques. To classify the growth stages of the strawberries, we used object inference and detection with machine learning model based on deep learning neural networks and TensorFlow. The classification accuracy was compared based on the training data volume and training epoch. As a result, it was able to classify with an accuracy of over 90% with 200 training images and 8,000 training steps. The detection and classification of the strawberry maturities could be identified with an accuracy of over 90% at the mature and over mature stages of the strawberries. Concurrently, the experimental results are promising, and they show that this approach can be applied to develop a machine learning model for predicting the strawberry harvesting time and can be used to provide key decision support information to both farmers and policy makers about optimal harvest times and harvest planning.

• Journal of Agricultural Extension & Community Development
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• v.16 no.4
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• pp.939-965
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• 2009

The Machine Learning has been identified as a promising approach to knowledge-based system development. This study aims to examine the ability of machine learning techniques for farmer's decision making and to develop the reference model for using pig farm data. We compared five machine learning techniques: logistic regression, decision tree, artificial neural network, k-nearest neighbor, and ensemble. All models are well performed to predict the sow's productivity in all parity, showing over 87.6% predictability. The model predictability of total litter size are highest at 91.3% in third parity and decreasing as parity increases. The ensemble is well performed to predict the sow's productivity. The neural network and logistic regression is excellent classifier for all parity. The decision tree and the k-nearest neighbor was not good classifier for all parity. Performance of models varies over models used, showing up to 104% difference in lift values. Artificial Neural network and ensemble models have resulted in highest lift values implying best performance among models.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1993.10a
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• pp.648-655
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• 1993

The hydraulic coconut dehusking machine consists of three main parts ie. The frame , the power unit including the hydraulic accessories, the lifting and dehusking mechanisms. Two sets of the hydraulic coconut dehusking machine were developed . their hydraulic and electrical control circuits were connected in series to enable them operating contemporaneously. Two operators are required to operate the machine. Each of them put a coconut on the lifting mechanism in order to start the working cycle automatically. As a result, the nut are immediately pushed up and seized by the holding teeth under the supplement of the hydraulic reducing circuit. After that the dehusking mechanism started operating via the sequence circuit. At the end of the cycle, both mechanisms return to their original positions. Some remaining fibrous is taken out manually from the nut subsequently . The continuous dehusking speed was found to be 22.2 seconds per 2 coconuts.

• Journal of Biosystems Engineering
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• v.22 no.2
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• pp.163-176
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• 1997

Sesame was one of the economical crops in Korea. However, cultivation area of sesame has been decreasing rapidly due to the lack of mechanization for this crop and the opening of agricultural product market. Sesame seed is so small that ordinary seeder can not seed properly. In rural practice, farmers seed sesame with hand and do thinning after shoot emerges. Seeding and thinning in sesame cultivation take more than 40% of total labor To reduce labor in seeding and thinning, a pellet seed machine for sesame has been developed. The pellet seed machine is very simple in structure. It utilizes the chemical reaction between alginate solution and $CaCl_2$. Two material forms a membrane when they meet The uniqueness of the pellet seed machine for sesame were 1) a counter rotating roller for metering the mixture of activated carbon and alginate and 2) swinging plate for submerging seed into the mixture. The prototype machine can produce 30, 000 pellets per hour and costs ￦6, 891 per 1 km sesame.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1996.06c
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• pp.1041-1044
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• 1996

The plastic-film-covered hill planter is a new-type seeding machine, including tow types which are mounted by 11kw and 40 kw tractors. It is made up of fertilizing , plastic-film covering perforating film and hole seeding, soil sealing apparatus, and can work at 5-7kw/h. The plastic-film covering and seeding of cotton, corn and soybean can all use this machine. The plastic-film-covered hill planter is mainly composed of plastic film covering unit, drum-type hill-drop unit and furrow coverer, some other types are also equipped with fertilizer drill unit. It can do combined work of covering plastic film , sowing , plastic film perforating , soil covering at one time, and it is suitable to the covering plastic film as well as planting of the grandulated crops, such as cotton , corn, soybean and so on.

• Korean Journal of Agricultural and Forest Meteorology
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• v.19 no.3
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• pp.86-92
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• 2017

In this study, a model to classify frost occurrence and frost free day was developed using the digital weather forecast data provided by Korea Meteorological Administration (KMA). The minimum temperature, average wind speed, relative humidity, and dew point temperature were identified as the meteorological variables useful for classification frost occurrence and frost-free days. It was found that frost-occurrence date tended to have relatively low values of the minimum temperature, dew point temperature, and average wind speed. On the other hand, relatively humidity on frost-free days was higher than on frost-occurrence dates. Models based on machine learning methods including Artificial Neural Network (ANN), Random Forest(RF), Support Vector Machine(SVM) with those meteorological factors had >70% of accuracy. This results suggested that these models would be useful to predict the occurrence of frost using a digital weather forecast data.

• Journal of Biosystems Engineering
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• v.26 no.6
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• pp.571-578
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• 2001

Production of green pepper has increased for ten years in Korea, as customer's preference of a pepper tuned to fiesta one. This study was conducted to develop an on-line fading algorithm of green pepper using machine vision and aimed to develop the automatic on-line grading and sorting system. The machine vision system was composed of a professive scan R7B CCD camera, a frame grabber and sets of 3-wave fluorescent lamps. The length and curvature, which were main quality factors of a green pepper were measured while removing the stem region. The first derivative of the thickness profile was used to remove the stem area of the segmented image of the pepper. A new boundary was generated after the stem was removed and a baseline of a pepper which was used for the curvature determination was also generated. The developed algorithm showed that the accuracy of the size measurement was 86.6% and the accuracy of the bent was 91.9%. Processing time spent far grading was around 0.17 sec per pepper.

• International journal of advanced smart convergence
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• v.12 no.4
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• pp.464-469
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• 2023

The government has set the mechanization of paddy agriculture as a national task, aiming to achieve over 70% by 2025. The main objective is to stabilize the farming costs of rural households due to the aging and feminization of rural areas, as well as the shortage of agricultural labor. In response to this, the Korea Rural Economic Institute operates a farm machinery rental business. However, there are challenges in selecting and managing rental machinery, including issues related to labor, costs, verification, and time. Additionally, there is a limit to upgrades, and overseas models are being imported and used for transplanters and rice planters, which do not conform to domestic standards and face maintenance difficulties. In order to solve the difficulties of the agricultural machine rental business, we intend to develop an application that shares domestic and foreign machines purchased and used by individuals at a low cost and use them in gun-level administrative districts.

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 2001.07a
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• pp.123-128
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• 2001