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Devonthink pro 사용법 mac#
1 application on the Mac for document and information management. Strong encryption and app-wide security (including Face ID) keeps your private data private.ĭEVONthink is the no. Synchronize your data directly on the local network, via iCloud, Dropbox, or any WebDAV server with DEVONthink for Mac. Store and organize your important documents on the go.
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Devonthink pro 사용법 Pc#
3 : Windows PC 용 DEVONthink To Go 사용 방법 - Windows 7/8 / 8.1 / 10 It could also reduce costs, risk of crop damage and excess herbicide residue, as well as potentially reduce environmental impact.사용 DEVONthink To Go 귀하의 Windows 컴퓨터에서 실제로 매우 쉽습니다하지만 당신 이이 과정을 처음 접한다면, 당신은 분명히 아래 나열된 단계에주의를 기울일 필요가있을 것입니다. It can significantly reduce the quantity of agrochemicals required, especially compared with traditional broadcast sprayers that usually treat the entire field, resulting in unnecessary application to areas that do not require treatment. This smart technology integrates a state of the art (AI-based) weed detection system, a novel fast and precision spraying system, and a weed mapping system. Finally, an RTK GPS was connected to the smart sprayer and an algorithm was developed to automatically generate weed maps and visualize the collected data (after every application). The less powerful GPU (NVIDIA Jetson TX2) achieved an overall precision and recall of 90% and 89% respectively on the first scenario with artificial plants, and 59% and 44% respectively on the second scenario with real plants. The more powerful GPU (NVIDIA GTX 1070 Ti) achieved an overall precision of 71% and recall of 78% (for plant detection and target spraying accuracy) on the most challenging scenario with real plants, and 91% accuracy and recall for the first scenario with artificial plants. Two different embedded graphics processing unit (GPU) were evaluated as the smart sprayer processing unit (for image processing and target detection). The second and most challenging scenario contained real plants portulaca weeds as targets, and sedge weeds and pepper plants as non-targets.
The first scenario contained artificial weeds (targets) and artificial plants (non-targets). Two different experimental scenarios were designed to simulate a vegetable field and to evaluate the smart sprayer’s performance. vegetable crops) and precisely spray on the desired target/location.
To reduce these negative impacts, a smart sprayer was designed and developed utilizing machine vision and artificial intelligence to distinguish target weeds from non-target objects (e.g. Most conventional sprayers apply agrochemicals uniformly, despite the fact that distribution of weeds is typically patchy, resulting in wastage of valuable compounds, increased costs, crop damage risk, pest resistance to chemicals, environmental pollution and contamination of products.
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