標(biāo)題: Titlebook: IoT and AI in Agriculture; Self- sufficiency in Tofael Ahamed Book 2023 The Editor(s) (if applicable) and The Author(s), under exclusive li [打印本頁(yè)] 作者: 風(fēng)俗習(xí)慣 時(shí)間: 2025-3-21 16:11
書目名稱IoT and AI in Agriculture影響因子(影響力)
書目名稱IoT and AI in Agriculture影響因子(影響力)學(xué)科排名
書目名稱IoT and AI in Agriculture網(wǎng)絡(luò)公開(kāi)度
書目名稱IoT and AI in Agriculture網(wǎng)絡(luò)公開(kāi)度學(xué)科排名
書目名稱IoT and AI in Agriculture被引頻次
書目名稱IoT and AI in Agriculture被引頻次學(xué)科排名
書目名稱IoT and AI in Agriculture年度引用
書目名稱IoT and AI in Agriculture年度引用學(xué)科排名
書目名稱IoT and AI in Agriculture讀者反饋
書目名稱IoT and AI in Agriculture讀者反饋學(xué)科排名
作者: Astigmatism 時(shí)間: 2025-3-21 21:20
urceneinsatz und der Bearbeitungsdauer. Weiterhin steigen die individuellen An- sprüche der Auftraggeber, so da? ehemals routinem??ig erledigte Gesch?ftsprozesse nunmehr der Projektarbeit bedingen und ehemals "einfache" Projekte komplexer wer- den. Darüber hinaus tragen neue und im Zeitablauf sich ?作者: 出血 時(shí)間: 2025-3-22 02:52
Munirah Hayati Hamidon,Mohammad Hussain Seyar,P. D. Kahandage,Victor Massaki Nakaguchi,Arkar Minn,Aiurceneinsatz und der Bearbeitungsdauer. Weiterhin steigen die individuellen An- sprüche der Auftraggeber, so da? ehemals routinem??ig erledigte Gesch?ftsprozesse nunmehr der Projektarbeit bedingen und ehemals "einfache" Projekte komplexer wer- den. Darüber hinaus tragen neue und im Zeitablauf sich ?作者: gorgeous 時(shí)間: 2025-3-22 06:53 作者: 固定某物 時(shí)間: 2025-3-22 10:42 作者: 積極詞匯 時(shí)間: 2025-3-22 14:10 作者: 抗體 時(shí)間: 2025-3-22 20:38 作者: 硬化 時(shí)間: 2025-3-22 22:17 作者: Fulsome 時(shí)間: 2025-3-23 01:24 作者: Ascendancy 時(shí)間: 2025-3-23 08:53 作者: 泥土謙卑 時(shí)間: 2025-3-23 18:56
Arkar Minn,Tofael Ahamedtscheiders in Verbindung gebracht werden kann. Abschlie?end stellt dieses Kapitel dar, welche irrationalen Verhaltensmuster sich durch die Wahrscheinlichkeitsgewichtefunktion erkl?ren lassen. Hierzu geh?rt beispielsweise die Tendenz, zu viele kleine Versicherungen abzuschlie?en. Ebenso l?sst sich di作者: 圖畫文字 時(shí)間: 2025-3-24 01:18
Linhuan Zhang,Tofael Ahamed,Yan Zhang,Pengbo Gao,Tomohiro Takigawat wird. Ebenso k?nnen die Erkenntnisse genutzt werden, um das eigene Verhalten zu lenken oder im Sinne eines Hedonic Framing die Wahrnehmung so zu beeinflussen, dass die eigene Zufriedenheit gesteigert wird..In diesem Kapitel werden für diese Anwendungsfelder jeweils Beispiele pr?sentiert, wie aus d作者: EPT 時(shí)間: 2025-3-24 04:02 作者: Pander 時(shí)間: 2025-3-24 08:02 作者: 抱怨 時(shí)間: 2025-3-24 12:45 作者: 評(píng)論者 時(shí)間: 2025-3-24 15:05
Long Range Wide Area Network (LoRaWAN) for Oil Palm Soil Monitoring,he template for LoRaWAN network is laid out in four parts; sensor node, gateway, network server, and application server. LoRaWAN is perfect for outlying regions without cellular network coverage or for establishing private networks covering long distances with minimum power consumption and maintenan作者: 傷心 時(shí)間: 2025-3-24 22:30
Artificial Intelligence in Agriculture: Commitment to Establish Society 5.0: An Analytical Conceptsge and its consequences over crops is demanding innovative solutions to keep on increasing yield while mitigating the adverse effects on the ecosystem. The aim of this chapter is to provide an analytical concept mapping and framework about AI-based learning systems, in a quasi-philosophical way to e作者: 小淡水魚(yú) 時(shí)間: 2025-3-24 23:09
Potentials of Deep Learning Frameworks for Tree Trunk Detection in Orchard to Enable Autonomous Nav (7–8?PM) conditions in August and September (summertime) in Japan. Thermal imagery datasets were augmented to train, validate, and test using the faster R-CNN, YOLO-v3, and CenterNet deep learning model to detect a tree trunk. A total of 12,876 images were used to train the model, 9270 images were 作者: 與野獸博斗者 時(shí)間: 2025-3-25 04:27
Real-Time Pear Fruit Detection and Counting Using YOLOv4 Models and Deep SORT,e unique ID method was found to be more reliable, with an F1. of 87.85%. This was because YOLOv4 had a very low false negative in detecting pear fruits. The ROI line is more reliable because of its more restrictive nature, but due to flickering in detection it was not able to count some pears despit作者: collagenase 時(shí)間: 2025-3-25 08:32
Pear Recognition System in an Orchard from 3D Stereo Camera Datasets Using Deep Learning Algorithms) conditions at JST, Tokyo Time, August 2021 (summertime) to prepare training, validation, and test datasets at a ratio of 6:3:1. All the images were taken by a 3D stereo camera which included PERFORMANCE, QUALITY, and ULTRA models. We used the PERFORMANCE model to capture images to make the dataset作者: Paraplegia 時(shí)間: 2025-3-25 13:20 作者: SOBER 時(shí)間: 2025-3-25 19:51
Low-Cost Automatic Machinery Development to Increase Timeliness and Efficiency of Operation for Smaare significant noted: Level 0 refers to no automation, Level 1 is assistance in automation, Level 2 outlines partial automation, Level 3 is conditional automation, Level 4 is high automation, and Level 5 is full automation considering the sensing system lateral and longitudinal control of machinery作者: 慎重 時(shí)間: 2025-3-25 20:36 作者: Fluctuate 時(shí)間: 2025-3-26 00:39
Autonomous Robots in Orchard Management: Present Status and Future Trends,d grippers with few degrees of freedom robotic arms in combination with developed neural networks and classification methods are innovations for achieving the commercialization of orchard automation and low-cost roboticization of medium-scale orchard crops.作者: 下邊深陷 時(shí)間: 2025-3-26 06:39 作者: 一窩小鳥(niǎo) 時(shí)間: 2025-3-26 09:27 作者: 莊嚴(yán) 時(shí)間: 2025-3-26 15:12 作者: Memorial 時(shí)間: 2025-3-26 20:20 作者: DIS 時(shí)間: 2025-3-26 21:33 作者: 策略 時(shí)間: 2025-3-27 01:32 作者: exhilaration 時(shí)間: 2025-3-27 08:50 作者: Cocker 時(shí)間: 2025-3-27 12:44
Tofael AhamedCovers key technologies and techniques such as IoT, AI, and ML in the development of smart agriculture.Provides information for different types of hardware, platforms, and machine learning techniques 作者: CRP743 時(shí)間: 2025-3-27 15:50 作者: 正常 時(shí)間: 2025-3-27 21:22
https://doi.org/10.1007/978-981-19-8113-5Deep learning; Digital Agriculture; Smart Agriculture; Data Driven System; Smart farming; Machine learnin作者: GUISE 時(shí)間: 2025-3-27 22:19 作者: 蚊子 時(shí)間: 2025-3-28 03:38 作者: Anterior 時(shí)間: 2025-3-28 07:52 作者: urethritis 時(shí)間: 2025-3-28 13:31 作者: FADE 時(shí)間: 2025-3-28 17:00 作者: 加入 時(shí)間: 2025-3-28 18:55 作者: limber 時(shí)間: 2025-3-29 00:14 作者: Indicative 時(shí)間: 2025-3-29 04:47
Addie Ira Borja Parico,Tofael Ahamedngsqualit?t beitragen. Hierbei wurde jedoch noch zu wenig dargestellt, wie mit den im deskriptiven Teil II dieses Buches vorgestellten Biases aus Sicht des Entscheiders am besten umgegangen werden kann. In diesem Kapitel wird exemplarisch auf einige entsprechende Debiasing-Instrumente eingegangen, d作者: 邊緣 時(shí)間: 2025-3-29 10:53 作者: 棲息地 時(shí)間: 2025-3-29 13:56
Victor Massaki Nakaguchi,Tofael Ahameduch von Motiven beeinflusst, die im Hinblick auf das Erreichen einer hohen Entscheidungsqualit?t kritisch zu sehen sind, weil sie den Menschen unbewusst auf einen Pfad lenken, auf den dieser m?glicherweise gar nicht gehen m?chte. Dieses Kapitel geht auf drei ?gef?hrliche“ Grundbedürfnisse des Mensch作者: indemnify 時(shí)間: 2025-3-29 17:33 作者: 發(fā)起 時(shí)間: 2025-3-29 23:42 作者: 單獨(dú) 時(shí)間: 2025-3-30 02:59
Linhuan Zhang,Tofael Ahamed,Yan Zhang,Pengbo Gao,Tomohiro Takigawangsqualit?t beitragen. Hierbei wurde jedoch noch zu wenig dargestellt, wie mit den im deskriptiven Teil II dieses Buches vorgestellten Biases aus Sicht des Entscheiders am besten umgegangen werden kann. In diesem Kapitel wird exemplarisch auf einige entsprechende Debiasing-Instrumente eingegangen, d作者: Angiogenesis 時(shí)間: 2025-3-30 06:34 作者: 教唆 時(shí)間: 2025-3-30 11:34 作者: arabesque 時(shí)間: 2025-3-30 13:37 作者: Charlatan 時(shí)間: 2025-3-30 19:42
An IoT-Based Precision Irrigation System to Optimize Plant Water Requirements for Indoor and Outdoo semiarid regions, for meeting plant water requirements. Considering the increasing water shortage due to the effects of climate change, water management in agriculture is the key to securing water for water consumers, including agriculture, municipal, industrial, and daily utilizations. Thus, effic作者: BROTH 時(shí)間: 2025-3-30 21:57 作者: hypnogram 時(shí)間: 2025-3-31 04:03
Purification of Agricultural Polluted Water Using Solar Distillation and Hot Water Producing with Cpolluted. The difficulty is severe, especially in underdeveloped countries in which majority is facing for energy crisis as well. Therefore, when finding the solutions, it is very important to pay attention for both problems as water purification and management is always bound with energy. Even thou作者: GRACE 時(shí)間: 2025-3-31 06:52 作者: Texture 時(shí)間: 2025-3-31 10:57
Strategic Short Note: Application of Smart Machine Vision in Agriculture, Forestry, Fishery, and Anre, which consists of forestry, fishery, and agriculture, and animal husbandry, manual observation is the main method used by farmers to monitor field and animal conditions. However, the younger generation is reluctant to engage in farming due to the high labor requirements and low wages. To solve t作者: 全部逛商店 時(shí)間: 2025-3-31 14:06
Artificial Intelligence in Agriculture: Commitment to Establish Society 5.0: An Analytical Conceptsctors of human activities is becoming the background of innovation. Therefore, the solution for many complex problems specially regarding to agri-food industry dynamics and climate change. Society 5.0 is an approach of the future society considering a projection over global population and the aspect作者: Apoptosis 時(shí)間: 2025-3-31 20:40 作者: Benign 時(shí)間: 2025-3-31 22:34 作者: 內(nèi)部 時(shí)間: 2025-4-1 04:15
Pear Recognition System in an Orchard from 3D Stereo Camera Datasets Using Deep Learning Algorithmsomatic picking systems. Advancements in computer vision have brought the potential to train for different shapes and sizes of fruit using deep learning algorithms. In this research, a fruit recognition method for robotic systems was developed to identify pears in a complex orchard environment using 作者: 松馳 時(shí)間: 2025-4-1 08:44 作者: cultivated 時(shí)間: 2025-4-1 12:27
Strategic Short Note: Intelligent Sensing and Robotic Picking of Kiwifruit in Orchard,nstable field labor availability and increased labor cost. Intelligent sensing and nondestructive picking of fruit are the two main key technologies for robotic harvesting. Deep learning technologies has been employed to train and detect kiwifruit, which achieved good performance by improving YOLOv3作者: 品牌 時(shí)間: 2025-4-1 16:23 作者: 話 時(shí)間: 2025-4-1 20:54
Vision-Based Leader Vehicle Trajectory Tracking for Multiple Agricultural Vehicles,omatically tracks the leader vehicle. With such a system, a human driver can control two vehicles efficiently in agricultural operations. The tracking system was developed for the leader and the follower vehicle, and control of the follower was performed using a camera vision system. A stable and ac作者: aggressor 時(shí)間: 2025-4-2 02:41 作者: intellect 時(shí)間: 2025-4-2 02:54
Strategic Short Note: Comparing Soil Moisture Retrieval from Water Cloud Model and Neural Network Ual methods for determining soil moisture are difficult, time-consuming, and challenging in the rural estate areas. In this study, synthetic aperture radar (SAR), L-band images, and in situ observations were conducted at an oil palm plantation to employ water cloud model (WCM) inversion for retrievin作者: allude 時(shí)間: 2025-4-2 10:08
