基于NB-IoT和能量收集技術(shù)的葡萄霜霉病預(yù)警系統(tǒng)
Pessl植物生理生態(tài)監(jiān)測系統(tǒng)的全套監(jiān)測系統(tǒng)和在線平臺FieldClimate適用于所有氣候區(qū)，可用于各種行業(yè)和各種用途——從農(nóng)業(yè)到研究、水文、氣象、洪水警報等。iMetos植物生理生態(tài)監(jiān)測系統(tǒng)已經(jīng)成為一個全球品牌，使用持續(xù)時間更長，性能更好，是通用的天氣監(jiān)測設(shè)備，具有早期識別和警報功能（有SMS手機提醒功能）；可以用來計劃、控制和管理復(fù)雜的獨立氣象過程。該監(jiān)測系統(tǒng)專為不同氣候區(qū)域的多種任務(wù)而設(shè)計。其可以安裝多達600個傳感器，如土壤和空氣濕度、溫度、降雨、風(fēng)速、風(fēng)向、葉片 濕度、總體輻射等傳感器。
摘要：影響葡萄生產(chǎn)的一個主要問題是真菌病原體的侵?jǐn)_，其中葡萄瘧原蟲是最嚴(yán)重的一種，引起葡萄霜霉病。這可能會對葡萄園造成重大損害，從而導(dǎo)致經(jīng)濟損失。預(yù)測疾病爆發(fā)的方法依賴于氣象參數(shù)的監(jiān)測。隨著物聯(lián)網(wǎng) (IoT) 技術(shù)的最新發(fā)展，可以有效地大規(guī)模收集原位數(shù)據(jù)。在本文中，提出了一種基于窄帶物聯(lián)網(wǎng)（NB-IoT）和能量收集的葡萄霜霉病預(yù)警系統(tǒng)實施新模型。霜霉病預(yù)警系統(tǒng)的模型已經(jīng)從早期的基于溫度（后來基于濕度）的模型發(fā)展為最新的機制模型，包括降雨/葉片濕度和每小時監(jiān)測。我們添加了“有利的夜間條件”和“風(fēng)速”等參數(shù)作為孢子囊傳播的關(guān)鍵。該模型與商業(yè) iMetos® 警告系統(tǒng)和三個特定葡萄園位置的最新機械模型的比較表明警報之間的高度相關(guān)性。
Grapevine Downy Mildew Warning System Based on NB-IoT and Energy Harvesting Technology
One major problem that affecting grape production is that of infestations by fungal pathogens, among which Plasmopara viticola is one of the worst, causing grapevine downy mildew. This can cause substantial damage to a vineyard, which leads to economic losses. Methods of predicting disease outbreak rely on the monitoring of meteorological parameters. With the recent development of Internet of Things (IoT) technologies, in situ data can be efficiently collected on a large scale. In this paper, a new model with early warning system implementation for grapevine downy mildew based on Narrow Band IoT (NB-IoT) and energy harvesting is presented. Models of downy mildew warning systems have evolved from the early temperature-based (and later, humidity-based) models to the latest mechanistic models which include rainfall/leaf wetness and hourly monitoring. We added parameters such as ’favorable night condition’ and ’wind speed’ as critical for sporangia spreading. The comparison of the model with the commercial iMetos® warning system and the latest mechanistic model for three specific vineyard locations indicates a high correlation between alarms.
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