Wireless sensor network based advisory system for Apple Scab prevention

Bhargava, Kriti; Kashyap, Arti; Gonsalves, Timothy A.

doi:10.1109/ncc.2014.6811263

Cited by 18 publications

(5 citation statements)

References 4 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Apple Disease Prediction Bhargava et al [59] designed a wireless sensor network for the building of a decision support system for apple crop disease prediction in the hilly region. In their study, the sensors were deployed in the field for the collection of data.…”

Section: 2mentioning

confidence: 99%

IoT-Smart Agriculture: Comparative Study on Farming Applications and Disease Prediction of Apple Crop using Machine Learning

Islam

Jamwal²,

Mir³

et al. 2022

Iraqi Journal of Science

View full text Add to dashboard Cite

Recently, the Internet of Things has emerged as an encouraging technology that is scaling up new heights towards the modernization of real word physical objects into smarter devices in several domains. Internet of Things (IoT) based solutions in agriculture drives farming into a smart way through the proliferation of smart devices to enhanced production with minimal human involvement. This paper presents a comprehensive study of the role of IoT in prominent applications of farming, wireless communication protocols, and the role of sensors in precision farming. In this research article, the existing frameworks in IoT-based agriculture systems with relevant technologies are presented. Furthermore, the comparative analysis of the apple disease prediction system concerning different types of disease found in the apple crop are discussed. In addition, this paper presents the contributions made by numerous researchers over the past few years in the apple disease prediction system. The aim of this research is to support the development of smart agriculture applications that would be helpful in precision farming for the optimization of resources with the help of IoT in agriculture and early disease prediction in apple crops.

show abstract

Section: 2mentioning

confidence: 99%

IoT-Smart Agriculture: Comparative Study on Farming Applications and Disease Prediction of Apple Crop using Machine Learning

Islam

Jamwal²,

Mir³

et al. 2022

Iraqi Journal of Science

View full text Add to dashboard Cite

show abstract

“…The feasibility of deploying WSNs at low cost has made these systems highly desirable for military, agriculture, sports, medicine, and industry. The potential applications of WSNs as cost-effective processes to improve agricultural resource management have been reported: irrigation management systems [3], farming systems monitoring [4][5][6], pest and disease control [7], controlled use of fertilizers [8], cattle movement monitoring [9], groundwater quality monitoring [10], greenhouse gases monitoring [11], asset tracking [12], and remote control and diagnosis [13,14]. The most promising applications for WSN in precision agriculture are irrigation management, farming monitoring, disease control, and fertilizer accuracy [15].…”

Section: Introductionmentioning

confidence: 99%

Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize

Singh

Heeren

et al. 2022

Sensors

View full text Add to dashboard Cite

Agriculture is considered a hotspot for wireless sensor network (WSN) facilities as they could potentially contribute towards improving on-farm management and food crop yields. This study proposes six designs of unmanned aerial system (UAS)-enabled data ferries with the intent of communicating with stationary sensor node stations in maize. Based on selection criteria and constraints, a proposed UAS data ferrying design was shortlisted from which a field experiment was conducted for two growing seasons to investigate the adoptability of the selected design along with an established WSN system. A data ferry platform comprised of a transceiver radio, a mini-laptop, and a battery was constructed and mounted on the UAS. Real-time monitoring of soil and temperature parameters was enabled through the node stations with data retrieved by the UAS data ferrying. The design was validated by establishing communication at different heights (31 m, 61 m, and 122 m) and lateral distances (0 m, 38 m, and 76 m) from the node stations. The communication success rate (CSR) was higher at a height of 31 m and within a lateral distance of 38 m from the node station. Lower communication was accredited to potential interference from the maize canopy and water losses from the maize canopy.

show abstract

“…This specification may reduce the window of opportunity of the farmer to quickly intervene. Bhargava et al [9] developed a simulation of a WSN to predict the onset of Apple Scab. Although the simulation provided a suitable architecture for deployment of WSN, no experimental tests were performed and the power efficiency and the duration of operation of the motes were not evaluated.…”

Section: Introductionmentioning

confidence: 99%

Low-Cost IoT Remote Sensor Mesh for Large-Scale Orchard Monitorization

Varandas

Faria

Gaspar

et al. 2020

JSAN

View full text Add to dashboard Cite

Population growth and climate change lead agricultural cultures to face environmental degradation and rising of resistant diseases and pests. These conditions result in reduced product quality and increasing risk of harmful toxicity to human health. Thus, the prediction of the occurrence of diseases and pests and the consequent avoidance of the erroneous use of phytosanitary products will contribute to improving food quality and safety and environmental land protection. This study presents the design and construction of a low-cost IoT sensor mesh that enables the remote measurement of parameters of large-scale orchards. The developed remote monitoring system transmits all monitored data to a central node via LoRaWAN technology. To make the system nodes fully autonomous, the individual nodes were designed to be solar-powered and to require low energy consumption. To improve the user experience, a web interface and a mobile application were developed, which allow the monitored information to be viewed in real-time. Several experimental tests were performed in an olive orchard under different environmental conditions. The results indicate an adequate precision and reliability of the system and show that the system is fully adequate to be placed in remote orchards located at a considerable distance from networks, being able to provide real-time parameters monitoring of both tree and the surrounding environment.

show abstract

Wireless sensor network based advisory system for Apple Scab prevention

Cited by 18 publications

References 4 publications

IoT-Smart Agriculture: Comparative Study on Farming Applications and Disease Prediction of Apple Crop using Machine Learning

IoT-Smart Agriculture: Comparative Study on Farming Applications and Disease Prediction of Apple Crop using Machine Learning

Unmanned Aerial System-Based Data Ferrying over a Sensor Node Station Network in Maize

Low-Cost IoT Remote Sensor Mesh for Large-Scale Orchard Monitorization

Contact Info

Product

Resources

About