Heterogeneous Features and Deep Learning Networks Fusion-Based Pest Detection, Prevention and Controlling System Using Iot and Pest Sound Analytics in a Wide Agriculture System DOI
Md. Akkas Ali, Anupam Kumar Sharma, Rajesh Kumar Dhanaraj

и другие.

Опубликована: Янв. 1, 2023

Every country's main pillar is its agricultural industry, which produces almost fifty percent of the world's economic growth. It impossible to overstate importance accurate farming in evaluating crop status and choosing efficient solutions for various pest problems. The traditional approach detection unstable forecasts with poor precision. However, these surveillance methods typically display intrusiveness, demand a lot time money, are subject different preconceptions. pests produce sounds, can be captured little investment or effort using IoT networks. automatic identification categorization sounds made possible by deep learning algorithms, improving assessment species distribution ranges, monitoring nature. IoT-driven computerized components used this research's unique system use incorporated machine techniques on collection audio recordings insect sounds. Butterworth filter, Blackman Flattop window, Ultraspherical Filter, Rife-Vincent Window, Cosine-Tapered FFT, DFT, STFT, PNCC, RASTA-PLPCC, LSFCC, sound detectors, PID sensors were couple used. HFDLNet was utilized planned study training, testing, validation, 7,200 from 72 types examined identify their special features statistical properties. recommended model achieves 99.87% accuracy rate, sensitivity 99.96%, specificity 99.88%, recall an F1 score 99.93%, precision 99.98%. This research shows substantial improvements over earlier academic studies, such as Inception-ResNet-v2, FRCNN ResNet-50, Fatser-PestNet, MD-YOLO, YOLOv5m, MAM-IncNet, Xception. proposed has networks analysis create build prevention control strategy also constructed solar-powered generator that provide electricity devices within situated across expansive fields.

Язык: Английский

A Survey on Blockchian direct delivery of agricultural products from farmer to consumer and non-profit organizations DOI Open Access

Waje Archana Tryambak,

Poonam N. Railkar

International Research Journal of Modernization in Engineering Technology and Science, Год журнала: 2024, Номер unknown

Опубликована: Апрель 23, 2024

The entire blockchain-based supply chain restoration for food and agriculture (agri-food).It utilizes smart contracts the fundamental elements of blockchain technology, both which are utilized on networks.This paper describes how technology works, it could be used, might affect state current SCM Registry systems, what legal experts do.Blockchain's widespread adoption is detrimental to government agencies businesses that thought reliable enough manage transactions.Therefore, guarantee distribution methods, confidence, traceability in Agri-Food chain, a robust system required.Under suggested arrangement, every transaction recorded blockchain, uploads information Interplanetary File Storage System (IPFS).Identifying transform logistics sector primary goal.The common problems these fields were taken into account, characteristics can address noted.We learned about potential drawbacks advantages applications through poll.This thesis enable many firms collaborate with companies developing solutions, given existing sector.

Язык: Английский

Процитировано

0
Automated Inspection System for Crop Disease Detection DOI
S. Sobitha Ahila,

C Kaushal

Опубликована: Апрель 12, 2024

This project aims to advance agricultural practices through the development of a sophisticated plant disease detection system, focusing on critical crops such as tomatoes, potatoes, and peppers. Utilizing InceptionV3 base model for image detection/classification, our approach integrates high-level feature extraction with enhancements inspired by VGG ResNet architectures improved accuracy in identifying diseases. The system employs novel architecture that freezes layers, incorporates convolutional layers processing, utilizes batch normalization training stability, introduces simulated ResNet-style skip connections overcome vanishing gradient problem. A dense prediction layer finalizes classification task, catering dynamic nature environments. Additionally, this explores integration blockchain technology secure web interface, ensuring data integrity transparency dissemination results. dual-faceted not only enhances efficiency identification but also establishes reliable platform exchange, setting new standard technological applications sustainable farming. predictive represents significant advancement domain, offering potential improve crop yield sustainability early precise detection. By enabling timely accurate interventions, contributes healthier reduced losses, aligning farming practices.

Язык: Английский

Процитировано

0
How do cue utilization and value co-creation and future orientation affect the consumers’ choices of smart agricultural products? DOI Creative Commons
Zheng Yan, Dayu Cao

Humanities and Social Sciences Communications, Год журнала: 2024, Номер 11(1)

Опубликована: Окт. 21, 2024

Facing sustainability challenges and the demand for green high-quality food, smart agriculture has become a key solution, understanding consumer preferences its products is crucial sustainable development. By employing structural equation model using sample data of an online survey conducted in China, this study investigated consumers' intention to purchase agricultural products, thereby examining effects value co-creation, cue utilization, attitude, future orientation. According results, utilization positively affects co-creation attitude. In addition, attitude can promote intention. Moreover, orientation moderates effect on These findings have substantial practical implications formulating marketing strategies aimed at promoting consumption products. strengthening orientation, marketers specifically target consumers facilitate transition more patterns.

Язык: Английский

Процитировано

0
Latest Trends and Challenges in Digital Agriculture for Crop Production DOI
Fernando Fuentes-Peñailillo,

Karen Gutter,

Ricardo Vega

и другие.

Опубликована: Янв. 1, 2023

Digital agriculture is a modern approach to farming that leverages technology improve the efficiency and sustainability of agricultural production. With digital tools, farmers can make data-driven decisions optimize their crops' growth reduce waste. One key components use sensors other monitoring devices gather data about health development crops. This information then analyzed using advanced software algorithms provide insights into most efficient ways manage irrigation, fertilizer application, pest control, critical aspects agriculture. The result more precise, efficient, sustainable production process. Another important aspect drones unmanned aerial vehicles survey fields monitor crop growth. These tools with real-time progress crops, allowing them informed optimizing processes. In addition these technological advancements, uses big artificial intelligence (AI) help decisions. AI, analyze vast amounts identify patterns trends relevant crops operations. Despite benefits, there are also some challenges associated this sense, paper aims main Agriculture faced by in current practices.

Язык: Английский

Процитировано

0
Heterogeneous Features and Deep Learning Networks Fusion-Based Pest Detection, Prevention and Controlling System Using Iot and Pest Sound Analytics in a Wide Agriculture System DOI
Md. Akkas Ali, Anupam Kumar Sharma, Rajesh Kumar Dhanaraj

и другие.

Опубликована: Янв. 1, 2023

Every country's main pillar is its agricultural industry, which produces almost fifty percent of the world's economic growth. It impossible to overstate importance accurate farming in evaluating crop status and choosing efficient solutions for various pest problems. The traditional approach detection unstable forecasts with poor precision. However, these surveillance methods typically display intrusiveness, demand a lot time money, are subject different preconceptions. pests produce sounds, can be captured little investment or effort using IoT networks. automatic identification categorization sounds made possible by deep learning algorithms, improving assessment species distribution ranges, monitoring nature. IoT-driven computerized components used this research's unique system use incorporated machine techniques on collection audio recordings insect sounds. Butterworth filter, Blackman Flattop window, Ultraspherical Filter, Rife-Vincent Window, Cosine-Tapered FFT, DFT, STFT, PNCC, RASTA-PLPCC, LSFCC, sound detectors, PID sensors were couple used. HFDLNet was utilized planned study training, testing, validation, 7,200 from 72 types examined identify their special features statistical properties. recommended model achieves 99.87% accuracy rate, sensitivity 99.96%, specificity 99.88%, recall an F1 score 99.93%, precision 99.98%. This research shows substantial improvements over earlier academic studies, such as Inception-ResNet-v2, FRCNN ResNet-50, Fatser-PestNet, MD-YOLO, YOLOv5m, MAM-IncNet, Xception. proposed has networks analysis create build prevention control strategy also constructed solar-powered generator that provide electricity devices within situated across expansive fields.

Язык: Английский

Процитировано

0