Growth Monitoring and Planting Decision Supporting for Pear During the Whole Growth Stage Based on Pie-Landscape System

Guo, Lei; Liu, Yanghua; Hao, Hanjie; Han, Jiaqi; Liao, Tongkui

doi:10.1109/agro-geoinformatics.2018.8476123

Cited by 2 publications

(2 citation statements)

References 6 publications

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“…Several publications did not target orchard management per se but rather the management tasks in the other areas of the value chain, e.g., bayberry breeding [58]. Examples of models that could be used within an orchard MIS include a method for yield estimation of apple trees [90], a carbon balance model for use in guiding apple tree thinning [13], a model for apple and pear growth [55,91], apple and orange yield estimations [14,56,87], an agent-based decision support system for mango flower initiation [92] and a tool for the forward estimation of mango harvest timing [93]. The most comprehensive management system was that of Jianwei et al [94], who applied research on plant growth modelling within a framework to enable management decisions on, e.g., pruning, irrigation, fertilisation, yield prediction and cultivation.…”

Section: Plant Developmentmentioning

confidence: 99%

Management Information Systems for Tree Fruit—1: A Review

Dhonju,

Walsh,

Bhattarai

2024

Horticulturae

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A farm management information system (MIS) entails record keeping based on a database management system, typically using a client-server architecture, i.e., an information system, IS, coupled with a variety of tools/methods/models for the support of operational management. The current review adopts a multivocal approach to consider academic and commercial developments in MISs for orchard management, based primarily on the refereed literature but extending to grey literature and interviews of Australian mango orchard managers. Drivers for orchard MIS development include increasing the orchard size and management complexity, including regulatory requirements around labour, chemical spray use and fertilisation. The enablers include improvements in within-orchard communications, distributed (web) delivery systems using desktop and mobile devices, and sensor systems and predictive models, e.g., for pest management. Most orchard MIS-related publications target the commodities of apple, grape, mango and olive in the context of management of plant health (pest and disease), plant development, irrigation and labour management. Harvest forecast and MIS modules are only now beginning to emerge, in contrast to a long history of use in grain production. The commercial systems trend towards an incorporation of financial information, an integration of data from multiple sources and a provision of dashboards that are tailored to the user. Requirements for industry adoption of a MIS are discussed in terms of technical and design features, with a focus on usability and scalability.

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Section: Plant Developmentmentioning

confidence: 99%

Management Information Systems for Tree Fruit—1: A Review

Dhonju,

Walsh,

Bhattarai

2024

Horticulturae

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“…The labor costs can be efficiently reduced by automatically capturing the architectural parameters of the plant, so more and more attention has been paid to the design of the plant monitoring system (Somov et al, 2018 ; Grimblatt et al, 2021 ; Rayhana et al, 2021 ). Early, lots of systems are designed to monitor the various environmental parameters on plant growth, such as humidity, temperature, solar illuminance, etc., e.g., James and Maheshwar ( 2016 ) used multiple sensors to measure the soil data of plants and transmitted these data to the mobile phone by Raspberry Pi; Okayasu et al ( 2017 ) developed a self-powered wireless monitoring device that is equipped with some environmental sensors; Guo et al ( 2018 ) added big-data services to analyze the environmental data on plant growth. These environmental parameters indirectly indicate the process of plant growth, and they cannot record the visual scenes on plant growth, resulting in the unavailability of the physical structure parameters on plants.…”

Section: Related Workmentioning

confidence: 99%

Green Visual Sensor of Plant: An Energy-Efficient Compressive Video Sensing in the Internet of Things

Yang

Sun

2022

Front. Plant Sci.

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Internet of Things (IoT) realizes the real-time video monitoring of plant propagation or growth in the wild. However, the monitoring time is seriously limited by the battery capacity of the visual sensor, which poses a challenge to the long-working plant monitoring. Video coding is the most consuming component in a visual sensor, it is important to design an energy-efficient video codec in order to extend the time of monitoring plants. This article presents an energy-efficient Compressive Video Sensing (CVS) system to make the visual sensor green. We fuse a context-based allocation into CVS to improve the reconstruction quality with fewer computations. Especially, considering the practicality of CVS, we extract the contexts of video frames from compressive measurements but not from original pixels. Adapting to these contexts, more measurements are allocated to capture the complex structures but fewer to the simple structures. This adaptive allocation enables the low-complexity recovery algorithm to produce high-quality reconstructed video sequences. Experimental results show that by deploying the proposed context-based CVS system on the visual sensor, the rate-distortion performance is significantly improved when comparing it with some state-of-the-art methods, and the computational complexity is also reduced, resulting in a low energy consumption.

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Growth Monitoring and Planting Decision Supporting for Pear During the Whole Growth Stage Based on Pie-Landscape System

Cited by 2 publications

References 6 publications

Management Information Systems for Tree Fruit—1: A Review

Management Information Systems for Tree Fruit—1: A Review

Green Visual Sensor of Plant: An Energy-Efficient Compressive Video Sensing in the Internet of Things

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