A Self-Supervised Learning-Based Intelligent Greenhouse Orchid Growth Inspection System for Precision Agriculture

Chen, Liang-Bi; Huang, Guan-Zhi; Huang, Xiang-Rui; Wang, Wei-Chien

doi:10.1109/jsen.2022.3221960

Cited by 12 publications

(5 citation statements)

References 36 publications

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“…Therefore, the tropospheric single differencing error and ionospheric single differencing error between two receivers are equal, and both are 0. Here, (5) is simplified as (6).…”

Section: Ppp-sdmentioning

confidence: 99%

“…It offers precise positioning and navigation services for air, sea, land transportation, and military operations. With the integration of modern information and traditional agriculture, the GNSS has become the most critical technology for PA [4][5][6]. However, the sensitivity of the GNSS signals to obstacles like buildings, trees, and mountains results in low accuracy.…”

Section: Introductionmentioning

confidence: 99%

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Multi-Scenario Variable-State Robust Fusion Algorithm for Ranging Analysis Framework

Xie,

Zhang,

Wang

2024

Agriculture

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Integrating modern information technology with traditional agriculture has made agricultural machinery navigation essential in PA (precision agriculture). However, agricultural equipment faces challenges such as low positioning accuracy and poor algorithm adaptability due to the complex farmland environment and various operational requirements. In this research, we proposed a generalized ranging theoretical framework with multi-scenario variable-state fusion to improve the GNSS (Global Navigation Satellite System) observation exchange performance among agricultural vehicles, and accurately measure IVRs (inter-vehicular ranges). We evaluated the effectiveness of three types of GNSS observations, including PPP-SD (precise single point positioning using single difference), PPP-TCAR (precise single point positioning using double difference based on three-carrier ambiguity resolution), and PPP-LAMBDA (precise single point positioning using double difference based on least-squares ambiguity decorrelation adjustment). Moreover, we compared the accuracy of IVRs measurements. Our framework was validated through field experiments in different scenarios. It provides insights into the appropriate use of different positioning algorithms based on the application scenario, application objects, and motion states.

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“…Therefore, the tropospheric single differencing error and ionospheric single differencing error between two receivers are equal, and both are 0. Here, (5) is simplified as (6).…”

Section: Ppp-sdmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Multi-Scenario Variable-State Robust Fusion Algorithm for Ranging Analysis Framework

Xie,

Zhang,

Wang

2024

Agriculture

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“…The IoT based greenhouse [19][20][21] with adoption of machine learning also had been presented in previous studies. [22][23][24][25][26]. This IoT platform provide dashboard to visualize the data collected from sensor node monitor by user.…”

Section: Related Workmentioning

confidence: 99%

IoT Based Greenhouse Condition Monitoring System for Chili Plant Growth

Muhammad Syamim Mohammad Pandi,

Roslan Seman,

Aini Hafizah Mohd Saod

et al. 2024

ARASET

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Over the last few years, most of greenhouses have been managed traditionally based on manpower to monitor and maintain the plants, such as watering, fertilizing, and using their skill in detecting plant diseases. Thus, this study focuses on developing a greenhouse monitoring system by using IoT technology. It aims to assist the local farmers in monitoring their greenhouse, instead of applying the conventional methods that are tedious and required more labour works. For data collection, parameters for chilli plants are measured using temperature and humidity sensors, soil moisture sensor, and light dependent resistor (LDR) sensor. The data are sent to microcontroller and transferred to the Ubidots cloud for the monitoring purpose. Watering pump and exhaust fan can be activated to control the plant parameters so that the plant remains in its normal condition. The sensor data are processed through classification using Support Vector Machine (SVM) to analyse the plant condition, whether the proposed method is suitable or not for the chilli plant to grow. Overall, a mini greenhouse prototype for chilli plant has been successfully constructed with a system interface on Ubidots dashboard to monitor the condition of the greenhouse plant. Results show that the developed system can be an alternative in monitoring the greenhouse condition with accuracy of 94.5%.

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“…These approaches leverage cluster-based strategies to efficiently identify affected crop areas [2]. Additionally, self-supervised learning-based intelligent systems have been developed for precision agriculture, enabling automated inspection and monitoring of crop growth [9]. Moreover, research has identified the applications, necessities, and encounters associated with UAVs in smart agriculture, emphasizing the need for advanced technologies to address agricultural demands [10].…”

Section: History Of Researchmentioning

confidence: 99%

Integrating Bio-Inspired Approaches with UAV-FSN Systems for Clustered Target Area Detection in Agriculture

2024

tjes

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Target area detection in agriculture plays a crucial role in optimizing crop management and resource allocation. Traditional methods often lack the precision and efficiency required for modern farming practices. Integrating bio-inspired approaches with UAV-FSN systems offers a promising solution to this challenge. By harnessing principles from nature, such as swarm intelligence and reinforcement learning, it becomes possible to optimize the deployment and coordination of UAVs within FSN for clustered target area detection in agriculture. This research explores the integration of bio-inspired approaches with UAV-FSN systems for clustered target area detection in agriculture. The increasing demand for precision agriculture necessitates efficient methods for identifying target areas affected by various factors. In this study, we introduce a novel algorithm, the QL-ABC Algorithm, which combines the Artificial Bee Colony (ABC) Algorithm with Q-Learning to optimize the deployment and movement of unmanned aerial vehicles (UAVs) within flying sensor networks (FSN) for target area detection. The performance outcomes of the proposed QL-ABC Algorithm on comparison with the existing algorithms are analysed using extensive simulation analysis with the appropriate simulation metrics: packet delivery ratio, mean end-to-end delay, and energy consumption. Results validate the usefulness of the proposed QL-ABC algorithm in accurately detecting clustered target areas in agricultural landscapes, thus contributing to advancements in precision farming practices and sustainable agriculture. Keywords: Flying Sensor Networks, Artificial Bee Colony Algorithm, Clustered Target Area Detection, Q-Learning, Unmanned Aerial Vehicles.

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A Self-Supervised Learning-Based Intelligent Greenhouse Orchid Growth Inspection System for Precision Agriculture

Cited by 12 publications

References 36 publications

Multi-Scenario Variable-State Robust Fusion Algorithm for Ranging Analysis Framework

Multi-Scenario Variable-State Robust Fusion Algorithm for Ranging Analysis Framework

IoT Based Greenhouse Condition Monitoring System for Chili Plant Growth

Integrating Bio-Inspired Approaches with UAV-FSN Systems for Clustered Target Area Detection in Agriculture

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