A Cloud Computing Framework for Space Farming Data Analysis DOI Creative Commons
Adrian Genevie Janairo, Ronnie Concepcion, Marielet Guillermo

и другие.

AgriEngineering, Год журнала: 2025, Номер 7(5), С. 149 - 149

Опубликована: Май 8, 2025

This study presents a system framework by which cloud resources are utilized to analyze crop germination status in 2U CubeSat. research aims address the onboard computing constraints nanosatellite missions boost space agricultural practices. Through Espressif Simple Protocol for Network-on-Wireless (ESP-NOW) technology, communication between ESP-32 modules were established. The corresponding sensor readings and image data securely streamed through Amazon Web Service Internet of Things (AWS IoT) an ESP-NOW receiver Roboflow. Real-time plant growth predictor monitoring was implemented web application provisioned at end. On other hand, sprouts on bed determined custom-trained Roboflow computer vision model. feasibility remote computational analysis CubeSat, given its minute form factor, successfully demonstrated proposed framework. detection model resulted mean average precision (mAP), precision, recall 99.5%, 99.9%, 100.0%, respectively. temperature, humidity, heat index, LED Fogger states, shown real time dashboard. With this use case, immediate actions can be performed accordingly when abnormalities occur. scalability nature allows adaptation various crops support sustainable activities extreme environments such as farming.

Язык: Английский

IoT and AI-driven solutions for human-wildlife conflict: advancing sustainable agriculture and biodiversity conservation DOI Creative Commons
Niloofar Abed, M. Ramu,

Akbar Deldari

и другие.

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 100829 - 100829

Опубликована: Фев. 1, 2025

Язык: Английский

Процитировано

1
Design and Implementation of ESP32-Based Edge Computing for Object Detection DOI Creative Commons
Yeong‐Hwa Chang,

Fuyan Wu,

Hung-Wei Lin

и другие.

Sensors, Год журнала: 2025, Номер 25(6), С. 1656 - 1656

Опубликована: Март 7, 2025

This paper explores the application of ESP32 microcontroller in edge computing, focusing on design and implementation an server system to evaluate performance improvements achieved by integrating cloud computing. Responding growing need reduce burdens latency, this research develops server, detailing hardware architecture, software environment, communication protocols, framework. A complementary framework is also designed support processing. deep learning model for object recognition selected, trained, deployed server. Performance evaluation metrics, classification time, MQTT (Message Queuing Telemetry Transport) transmission data from various brokers are used assess performance, with particular attention impact image size adjustments. Experimental results demonstrate that significantly reduces bandwidth usage effectively alleviating load study discusses system’s strengths limitations, interprets experimental findings, suggests potential future applications. By AI IoT, demonstrates benefits localized processing enhancing efficiency reducing dependency.

Язык: Английский

Процитировано

0
DEEP LEARNING FRAMEWORK FOR FRUIT COUNTING AND YIELD MAPPING IN TART CHERRY USING YOLOv8 and YOLO11 DOI Creative Commons
Anderson Luiz dos Santos Safre, Alfonso F. Torres‐Rua, Brent Black

и другие.

Smart Agricultural Technology, Год журнала: 2025, Номер unknown, С. 100948 - 100948

Опубликована: Апрель 1, 2025

Язык: Английский

Процитировано

0
Tomato Leaf Detection, Segmentation, and Extraction in Real-Time Environment for Accurate Disease Detection DOI Creative Commons
Shahab Ul Islam, Giampaolo Ferraioli, Vito Pascazio

и другие.

AgriEngineering, Год журнала: 2025, Номер 7(4), С. 120 - 120

Опубликована: Апрель 11, 2025

Agricultural production is a critical sector that directly impacts the economy and social life of any society. The identification plant disease in real-time environment significant challenge for agriculture production. For accurate detection, precise detection leaves meaningful challenging task developing smart agricultural systems. Most researchers train test models on synthetic images. So, when using model scenario, it does not give satisfactory result because trained images fed with image plant, then its accuracy affected. In this research work, we have integrated two models, Segment Anything Model (SAM) YOLOv8, to detect tomato leaf mask leaf, extract environment. To improve performance environment, need accurately. We developed system will specific leaf. modified YOLOv8 used, masking extraction from used. Then, an provided deep neural network disease.

Язык: Английский

Процитировано

0
A Cloud Computing Framework for Space Farming Data Analysis DOI Creative Commons
Adrian Genevie Janairo, Ronnie Concepcion, Marielet Guillermo

и другие.

AgriEngineering, Год журнала: 2025, Номер 7(5), С. 149 - 149

Опубликована: Май 8, 2025

This study presents a system framework by which cloud resources are utilized to analyze crop germination status in 2U CubeSat. research aims address the onboard computing constraints nanosatellite missions boost space agricultural practices. Through Espressif Simple Protocol for Network-on-Wireless (ESP-NOW) technology, communication between ESP-32 modules were established. The corresponding sensor readings and image data securely streamed through Amazon Web Service Internet of Things (AWS IoT) an ESP-NOW receiver Roboflow. Real-time plant growth predictor monitoring was implemented web application provisioned at end. On other hand, sprouts on bed determined custom-trained Roboflow computer vision model. feasibility remote computational analysis CubeSat, given its minute form factor, successfully demonstrated proposed framework. detection model resulted mean average precision (mAP), precision, recall 99.5%, 99.9%, 100.0%, respectively. temperature, humidity, heat index, LED Fogger states, shown real time dashboard. With this use case, immediate actions can be performed accordingly when abnormalities occur. scalability nature allows adaptation various crops support sustainable activities extreme environments such as farming.

Язык: Английский

Процитировано

0