Titlebook: Agriculture Digitalization and Organic Production; Proceedings of the F Andrey Ronzhin,Mikalai Bakach,Alexander Kostyaev Conference proceed

Strategy

書目名稱Agriculture Digitalization and Organic Production影響因子(影響力)




書目名稱Agriculture Digitalization and Organic Production影響因子(影響力)學(xué)科排名




書目名稱Agriculture Digitalization and Organic Production網(wǎng)絡(luò)公開度




書目名稱Agriculture Digitalization and Organic Production網(wǎng)絡(luò)公開度學(xué)科排名




書目名稱Agriculture Digitalization and Organic Production被引頻次




書目名稱Agriculture Digitalization and Organic Production被引頻次學(xué)科排名




書目名稱Agriculture Digitalization and Organic Production年度引用




書目名稱Agriculture Digitalization and Organic Production年度引用學(xué)科排名




書目名稱Agriculture Digitalization and Organic Production讀者反饋




書目名稱Agriculture Digitalization and Organic Production讀者反饋學(xué)科排名

危機(jī)

Conference proceedings 2024 Minsk, Belarus during June 05–08, 2024. The topics covered in the book are ground robotic systems in crop production, unmanned aerial vehicles in crop production, aerospace monitoring tools in crop production, robotic animal husbandry, digitalization of technological processes of agricultural produ

terazosin

存心

Puntis Jifroodian Haghighi,Ching Y. Suenural practices as a demand of next generation for agricultural automation systems. This paper discusses about land preparation, sowing, interculture operations like weed removal, spraying, harvesting, post-harvesting using field robot, drones (UAV), AGVs for farm operations using sensor-based AI, DL

連鎖,連串

Pre-processing Issues in Arabic OCRtion among agribusiness organizations and stakeholders. The objective of this research is to pinpoint the smart farming technologies utilized in grain production systems in Brazil and to assess farmers’ perceptions regarding technical support for SF. A survey was carried out with farmers, resulting

Expediency

表主動

Maher Khemakhem,Abdelfettah Belghithment. The robotic pest detection for pink bollworm with its characterization is carried out in YOLO v8 software by trained model. A mean Average Precision (mAP) value is observed as 67.1%. The peripherals such as Intel Real-Sense camera, Data Storage System (DSS) mounted on Agri-Bot for field experi

expository

Design of a Bilingual Kannada–English OCRhe Khabarovsk Territory from Sentinel-2 images with a resolution of 10?m. A cluster analysis of NDVI time series in 2022 was carried out to assess the heterogeneity of soybean, oat, corn and buckwheat crops at the regional level. Two clusters were described for soybeans, one of which represented clo

Vldl379

acrimony