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RISS 인기검색어
- 검색키워드
- 저자 : Mingle Xu (검색결과 3 건)
- 무료
- 기관 내 무료
- 유료
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Intelligent Water and Nutrient Supply for Tomato Plant in Greenhouse
Mingle Xu,Alvaro Fuentes,Jongbin Park,Sook Yoon,Dong Sun Park 제어로봇시스템학회 2021 제어로봇시스템학회 각 지부별 자료집 Vol.2021 No.12
One of the challenges in the agricultural field is the method to supply nutrition and water. Excessive water and nutrition result in waste, high cost, and harm to our environment. The deficiency of them degrades the quality and yield of the plant and reduces the income of farmers. Currently, the water and nutrition supply is fixed in the greenhouse, instead of intelligent as the plant environment is not taken into consideration. In this paper, we aim to learn the relationship between the plant environment and the water nutrition supply curve deployed in current farms, which can be utilized to reduce drainage in the future. To achieve the goal, we leverage a long short-term memory network given time-series data from multimodal sensors. The experimental results show that the goal is achieved by our method
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Unsupervised Transfer Learning for Plant Anomaly Recognition
Mingle Xu,윤숙,박동선,이재수 (사)한국스마트미디어학회 2022 스마트미디어저널 Vol.11 No.4
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.
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Predicting Desired Fertigation for Rose Using Internet of Things Sensors and Time-Series Model
Mingle Xu,Sook Yoon,Jongbin Park,Jeonghyun Baek,Dong Sun Park (사)한국스마트미디어학회 2024 스마트미디어저널 Vol.13 No.2
Greenhouse provides opportunities to have big yield effectively and efficiently. However, many resources are required, such as fertigation, a kind of solution of nutrient. Resources supply is essential to cultivate crops. Inadequate supply will hinder plant growth whereas the surplus results in waste. In this paper, we are especially interested in the fertigation supply. Further, excess fertigation leads to drainage which is difficult to purify and threatens the environment. To address this challenge, we aim to predict the desired amount of fertigation. To achieve this objective, we first establish a prototype to record the climate conditions inside a rose greenhouse using Internet of Things sensors. Simultaneously, the desired fertigation amount is obtained with the help of weight scale and historical data of fertigation supply and drainage. Second, a method is proposed to predict the desired fertigation by taking the sensors’ data as input, with a time-series model. Extensive experimental results suggest the potential of our objective and method. To be specific, our method achieves an average MAE 0.032 in the validation datasets.
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