Plant Health Detection and Monitoring

Lee, Won Suk

doi:10.1007/978-1-4939-2836-1_11

Cited by 5 publications

(2 citation statements)

References 39 publications

(36 reference statements)

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“…HSI being used in exploring plants traits such as water content, nutrient status, disease infections and insect damages, has a high cost compared to other sensors such as RGB cameras, point spectrophotometers etc (Lee, 2015). A common approach is to use HSI in an exploratory first phase to identify relevant application-specific wavelengths, and then further on use, this information to design and apply cheaper multispectral systems (Mahlein et al, 2013;Mutka & Bart, 2015).…”

Section: Technical Challengesmentioning

confidence: 99%

Close range hyperspectral imaging of plants: A review

Mishra

Asaari

Herrero-Langreo³

et al. 2017

Biosystems Engineering

234

120

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Section: Technical Challengesmentioning

confidence: 99%

Close range hyperspectral imaging of plants: A review

Mishra

Asaari

Herrero-Langreo³

et al. 2017

Biosystems Engineering

234

120

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“…Moreover, it is suggested that more research is required for designing effective disease detection techniques for performing farming and differentiating the disease from other stresses. 12 IoT-assisted plant health monitoring is devised using certain imperative aspects that involve humidity, temperature, light, and carbon dioxide level, influencing plant growth productivity. Thus, continuous monitoring of environmental aspects provides information to the user.…”

mentioning

confidence: 99%

Deep computation model to the estimation of sulphur dioxide for plant health monitoring in IoT

Karnati¹,

Rao

Prakash

et al. 2021

Int J Intell Syst

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The Internet of Things (IoT) is an emerging domain in recent days as they provided a huge number of applications in day-to-day lives. In contrast to the agricultural sector, the automatic techniques for recognizing plant disease have different benefits and pose several issues. In addition, inappropriate diagnoses are ineffectual in treating the disease and may affect the crop yield. This paper presents a novel technique for plant health monitoring by estimating sulphur dioxide. Here, the simulation of IoT was performed for improved functioning. After that, the cluster head selection and routing are performed using the proposed invasive water cycle (IWC) algorithm, which is devised by integrating the water cycle algorithm (WCA) and invasive weed optimization (IWO) algorithm. Here, the fitness function is newly modeled using certain factors involving Energy, intra and intercluster distance, and delay. After the cluster head selection and routing, the sulphur dioxide content from the soil is estimated. For sulphur dioxide estimation, the soil data is considered the input data, and then the data transformation is performed to transform the data. After that, the feature selection is performed by Mahalanobis distance, and then sulphur dioxide from the soil is estimated using Deep Q-Network, where training is performed using the proposed IWC algorithm. The proposed IWC-based Deep Q-Network offered improved performance with the highest accuracy of 0.941, and the smallest root mean square error (RMSE) of 0.242. In addition, the minimal Energy and highest Throughput are computed by the proposed IWC-based Deep Q-Network.

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