Rice crop monitoring system — A lot based machine vision approach

Tanmayee, P.

doi:10.1109/icnets2.2017.8067890

Cited by 9 publications

(6 citation statements)

References 3 publications

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“…IoT transforms the agricultural industry and enables farmers to overcome different challenges. Innovative applications can address these issues and therefore increase the quality, quantity, sustainability and cost-effectiveness of crop production [41], [42], [43]. IoT provides more benefits to the farming industry by improving the health of animals through better food and environment, addressing the labour shortage issue as well cost savings through automation, increase in milk production, and increase in some animals during the breeding period through detection of estrus cycle and additional revenue streams from waste.…”

Section: Introductionmentioning

confidence: 99%

Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review

Madushanki¹,

Halgamuge²,

Wirasagoda³

et al. 2019

IJACSA

159

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It is essential to increase the productivity of agricultural and farming processes to improve yields and costeffectiveness with new technology such as the Internet of Things (IoT). In particular, IoT can make agricultural and farming industry processes more efficient by reducing human intervention through automation. In this study, the aim to analyze recently developed IoT applications in the agriculture and farming industries to provide an overview of sensor data collections, technologies, and sub-verticals such as water management and crop management. In this review, data is extracted from 60 peer-reviewed scientific publications (2016-2018) with a focus on IoT sub-verticals and sensor data collection for measurements to make accurate decisions. Our results from the reported studies show water management is the highest subvertical (28.08%) followed by crop management (14.60%) then smart farming (10.11%). From the data collection, livestock management and irrigation management resulted in the same percentage (5.61%). In regard to sensor data collection, the highest result was for the measurement of environmental temperature (24.87%) and environmental humidity (19.79%). There are also some other sensor data regarding soil moisture (15.73%) and soil pH (7.61%). Research indicates that of the technologies used in IoT application development, Wi-Fi is the most frequently used (30.27%) followed by mobile technology (21.10%). As per our review of the research, we can conclude that the agricultural sector (76.1%) is researched considerably more than compared to the farming sector (23.8%). This study should be used as a reference for members of the agricultural industry to improve and develop the use of IoT to enhance agricultural production efficiencies. This study also provides recommendations for future research to include IoT systems' scalability, heterogeneity aspects, IoT system architecture, data analysis methods, size or scale of the observed land or agricultural domain, IoT security and threat solutions/protocols, operational technology, data storage, cloud platform, and power supplies.12 | P a g e www.ijacsa.thesai.orgThis review provides an overall picture of currently developed IoT applications in agriculture and farming between 2016 and 2018.

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Section: Introductionmentioning

confidence: 99%

Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review

Madushanki¹,

Halgamuge²,

Wirasagoda³

et al. 2019

IJACSA

159

View full text Add to dashboard Cite

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“…RGB camera shows better performance compared to NIR-based detection [150]. An integrated technology consisting of machine vision and the Internet of Things can be deployed at smart indoor farms to get an early alert about the crop infection and take immediate remedies [151]. Unmanned aerial vehicles (UAV), also known as drones, have been already introduced for smart agricultural activities.…”

Section: Detection Of Plant Diseasesmentioning

confidence: 99%

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Hati

Singh

2021

AgriEngineering

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Conventional farming necessitates a large number of resources and infrastructure such as land, irrigation, manpower to manage farms, etc. Modern initiatives are required to automate conventional farms. Smart indoor farms offer the potential to remedy the shortfalls of conventional farms by providing a controlled, intelligent, and smart environment. This paper presents a three-dimensional perspective consisting of soilless farming, energy harvesting, and smart technologies, which could be considered as the three important characteristics of smart indoor farms. A six-layer smart indoor farms architecture has also been proposed, which explains how data are collected using various sensors and devices and then transmitted onto the cloud infrastructure for further analysis and control through various layers. Artificial lighting, smart nutrition management, and artificial climate control, to name a few, are some of the important requirements for smart indoor farms while considering control and service management factors. The major bottleneck in installing such systems is both the economical and the technical constraints. However, with the evolution of technology (and when they become widely available in the near future), a more favourable farming scenario may emerge. Furthermore, smart indoor farms could be viewed as a potential answer for meeting the demands of a sustainable agricultural revolution as we move closer to Agriculture 4.0. Finally, in order to adapt smart indoor farms and their study scope, our work has presented various research areas to potential researchers.

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“…Doenças em plantas geralmente manifestam seus primeiros sintomas observáveis através das folhas, e muitos estudos focam na análise das folhas para a identificação dessas patologias [Barbedo et al 2018, Sawarkar andKawathekar 2018]. Pelo uso de técnicas avançadas de Aprendizagem de Máquina e Visão Computacionalé possível não só a identificação dessas doenças, mas também a análise do grau do dano causado pelas mesmas [Tanmayee 2017, Solanke et al 2018. Uma das etapas fundamentais para a identificação e análise do dano causado por patologias em plantasé a segmentação das imagens das folhas, porém, muitas das propostas existentes na literatura realizam essa tarefa manualmente [Lee et al 2015, Barbedo et al 2018, o que requer um alto grau de esforço humano.…”

Section: Introductionunclassified

Agrupamento de Imagens Baseado em uma Abordagem Híbrida entre a Otimização por Busca em Grupo e K-Means para a Segmentação Automática de Doenças em Plantas

Pacífico¹

2020

Anais Do Encontro Nacional De Inteligência Artificial E Computacional (ENIAC 2020)

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Neste trabalho, um algoritmo de agrupamento particional híbrido entre o K-Means e a Otimização por Busca em Grupo é usado para a tarefa de segmentação automática de doenças em imagens de folhas, como parte de um sistema automático de classificação de doenças em plantas: o GSO-KM. Uma abordagem não supervisionada de agrupamento de imagens é adotada no intuito de lidar com o problema. O GSO-KM é comparado a cinco algoritmos da literatura de agrupamento de dados através do uso de doze imagens, que apresentam diferentes graus de doença, e pelo uso de quatro métricas de avaliação. A análise experimental revelou que o GSO-KM é capaz de realizar, de forma satisfatória, a tarefa de segmentação das doenças nas imagens avaliadas.

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Rice crop monitoring system — A lot based machine vision approach

Cited by 9 publications

References 3 publications

Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review

Adoption of the Internet of Things (IoT) in Agriculture and Smart Farming towards Urban Greening: A Review

Smart Indoor Farms: Leveraging Technological Advancements to Power a Sustainable Agricultural Revolution

Agrupamento de Imagens Baseado em uma Abordagem Híbrida entre a Otimização por Busca em Grupo e K-Means para a Segmentação Automática de Doenças em Plantas

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