Sensors, Год журнала: 2024, Номер 24(20), С. 6608 - 6608
Опубликована: Окт. 14, 2024
An image is a visual representation that can be used to obtain information. A camera on moving vector (e.g., rover, drone, quad, etc.) may acquire images along controlled trajectory. The maximum information captured during fixed acquisition time when consecutive do not overlap and have no space (or gap) between them. said
Язык: Английский
Cleaner Energy Systems, Год журнала: 2024, Номер 9, С. 100132 - 100132
Опубликована: Авг. 11, 2024
Modern agricultural practices encounter challenges related to operational efficiency and environmental effects. This prompts a demand for innovative solutions foster sustainability in farming while emphasizing the limitations of conventional methods. To address these modern agriculture systems, this research proposes comprehensive framework smart farming. The proposed comprises three technology integrations: 1) an efficient integration renewable energy resources (RERs) with solar panels battery storage systems (BESS), 2) IoT-based monitoring precision irrigation, 3) android application-controlled robotic system targeted chemical application. investigates case study on Sharjah, United Arab Emirates (UAE) explore analyze optimal scenarios multiple resources. Results demonstrate successful cross-prototype through Blynk IoT platform providing users unified interface. Furthermore, results provide analysis investigation into interactions between RERs grid across various combinations. findings indicate potential revolutionize thus offer sustainable, efficient, technologically advanced approach. It also represents contribution complete solution presenting tangible promising future sustainable practices.
Язык: Английский
Процитировано
23
Advances in environmental engineering and green technologies book series, Год журнала: 2025, Номер unknown, С. 431 - 468
Опубликована: Янв. 3, 2025
This chapter emphasizes the integration of IoT and computer vision technology improving precision farming also highlights crucial role that real-time data processing plays in farm robots. According to research studies, enhances efficiency operations. The spraying can be even more accurate by up 20% operating costs reduced 12%. In addition discussing topics like accuracy cybersecurity, this still addressed benefits for crop monitoring autonomous form instantaneous feedback. further explains some future areas under AI, climate-smart behaviors, emergent technology. Some takeaway points are there is so much potential greatly increase agricultural output sustainability through these advancements. Apart from that, it includes requirements continuous innovation adaptations technologies ensure they meet today's agriculture needs.
Язык: Английский
Процитировано
4
Remote Sensing, Год журнала: 2024, Номер 16(6), С. 1003 - 1003
Опубликована: Март 12, 2024
Yield calculation is an important link in modern precision agriculture that effective means to improve breeding efficiency and adjust planting marketing plans. With the continuous progress of artificial intelligence sensing technology, yield-calculation schemes based on image-processing technology have many advantages such as high accuracy, low cost, non-destructive calculation, they been favored by a large number researchers. This article reviews research crop-yield remote images visible light images, describes technical characteristics applicable objects different schemes, focuses detailed explanations data acquisition, independent variable screening, algorithm selection, optimization. Common issues are also discussed summarized. Finally, solutions proposed for main problems arisen so far, future directions predicted, with aim achieving more wider popularization image technology.
Язык: Английский
Процитировано
9
European Journal of Agronomy, Год журнала: 2024, Номер 164, С. 127477 - 127477
Опубликована: Дек. 17, 2024
Язык: Английский
Процитировано
7
Agriculture, Год журнала: 2024, Номер 14(9), С. 1596 - 1596
Опубликована: Сен. 13, 2024
Plant height is a crucial indicator of crop growth. Rapid measurement facilitates the implementation and management planting strategies, ensuring optimal production quality yield. This paper presents low-cost method for rapid multiple lettuce heights, developed using an improved YOLOv8n-seg model stacking characteristics planes in depth images. First, we designed lightweight instance segmentation based on by enhancing architecture reconstructing channel dimension distribution. was trained small-sample dataset augmented through random transformations. Secondly, proposed to detect segment horizontal plane. leverages plane, as identified image histogram from overhead perspective, allowing identification parallel camera’s imaging Subsequently, evaluated distance between each plane centers contours select cultivation substrate reference bottom height. Finally, plants determined calculating difference top plant. The experimental results demonstrated that achieved 25.56% increase processing speed, along with 2.4% enhancement mean average precision compared original model. accuracy plant algorithm reached 94.339% hydroponics 91.22% pot scenarios, absolute errors 7.39 mm 9.23 mm, similar sensor’s direction error. With images downsampled factor 1/8, highest speed recorded 6.99 frames per second (fps), enabling system process 174 targets second. confirmed exhibits promising accuracy, efficiency, robustness.
Язык: Английский
Процитировано
6
INMATEH Agricultural Engineering, Год журнала: 2024, Номер unknown, С. 96 - 105
Опубликована: Март 31, 2024
This research is dedicated to enhancing the accuracy and processing speed of grape disease recognition. As a result, real-time detection model named MSCI-YOLOv8s, based on an improved YOLOv8s framework proposed. The primary innovation this lies in replacing backbone network original with more efficient MobileNetV3. alteration not only strengthens ability capture features various manifestations leaf images but also improves its generalization capabilities stability. Additionally, incorporates SPPFCSPC pyramid pooling structure, which maintains stability receptive field while significantly speed. integration CBAM attention mechanism further accentuates identify key features, substantially increasing detection. Moreover, employs Inner-SIoU as loss function, optimizing precision bounding box regression accelerating convergence, thereby efficiency. Rigorous testing has shown that MSCI-YOLOv8s achieves impressive average (mAP) 97.7%, inference time just 37.2 milliseconds memory footprint 39.3 MB. These advancements render highly extremely practical for detection, meeting actual demands orchard identification demonstrating significant potential application.
Язык: Английский
Процитировано
4
AgriEngineering, Год журнала: 2024, Номер 6(3), С. 2494 - 2512
Опубликована: Авг. 1, 2024
In open-field agricultural environments, the inherent unpredictable situations pose significant challenges for effective human–robot interaction. This study aims to enhance natural communication between humans and robots in such challenging conditions by converting detection of a range dynamic human movements into specific robot actions. Various machine learning models were evaluated classify these movements, with Long Short-Term Memory (LSTM) demonstrating highest performance. Furthermore, Robot Operating System (ROS) software (Melodic Version) capabilities employed interpret certain actions be performed unmanned ground vehicle (UGV). The novel interaction framework exploiting vision-based activity recognition was successfully tested through three scenarios taking place an orchard, including (a) UGV following authorized participant; (b) GPS-based navigation specified site orchard; (c) combined harvesting scenario participants aid transporting crates from harvest designated sites. main challenge precise hand gesture “come” alongside navigating intricate environments complexities background surroundings obstacle avoidance. Overall, this lays foundation future advancements collaboration agriculture, offering insights how integrating can communication, trust, safety.
Язык: Английский
Процитировано
3
Computers and Electronics in Agriculture, Год журнала: 2024, Номер 225, С. 109345 - 109345
Опубликована: Авг. 17, 2024
Язык: Английский
Процитировано
3
IEEE Access, Год журнала: 2024, Номер 12, С. 123595 - 123608
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
3
Computers and Electronics in Agriculture, Год журнала: 2025, Номер 231, С. 110032 - 110032
Опубликована: Янв. 30, 2025
Язык: Английский
Процитировано
0