Exploration of research on Internet of Things enabled smart agriculture

Kumar, T. Bharath; Prashar, Deepak

doi:10.1016/j.matpr.2021.05.652

Cited by 3 publications

(1 citation statement)

References 19 publications

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“…Furthermore, the IoT consists of a various set of networks with which these objects can communicate. T. Bharath Kumar and D. Prashar [19] discuss about the Smart Agriculture which uses technology to increase the efficiency and productivity of agricultural production. This technology helps farmers to monitor and manage their crops, livestock, and other resources with the help of sensors, automation, and communication tools.…”

Section: Internet Of Things and Sensorsmentioning

confidence: 99%

Role of Machine Learning and Internet of Thing in Agriculture- A Survey

Jha

Yadav

Gupta

2022

2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)

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Agriculture is the backbone of the economy and a source of jobs for developing nations like India. For human life, food is a necessity that can be fulfilled only through farming. Agricultural activities are usually classified into three broad categories: post-harvest, harvest, and pre-harvest. Farmers are doing their best to produce grain, but there is grain loss at the time of various post-harvest activities like cutting, storage, etc. Various studies have already been conducted in the fields of preharvest, harvest, and food quality. But in the field of postharvest, there is lots of scope for work. For food sustainability reduction in post-harvest activities is necessary. This paper presents a broad survey of the post-harvest activities, Internet of Things (IoT) and machine learning (ML) applications in the agricultural domain to mitigate the post-harvest issue. Applying machine learning (ML) and the internet of things (IoT) in agriculture allows more efficient and accurate monitoring of grain so food loss problem can be solved in effective manner.

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Section: Internet Of Things and Sensorsmentioning

confidence: 99%

Role of Machine Learning and Internet of Thing in Agriculture- A Survey

Jha

Yadav

Gupta

2022

2022 IEEE International Conference on Current Development in Engineering and Technology (CCET)

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Cloud Computing for Smart Farming: Applications, Challenges, and Solutions

Rajasekaran,

Samad,

Ganesan

2024

Signals and Communication Technology

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Environmental Simulation Model for Rapid Prediction of Tea Seedling Growth

Mao

Wang

et al. 2022

Agronomy

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Accurate and effective monitoring of environmental parameters in tea seedling greenhouses is an important basis for regulating the seedling environment, which is crucial for improving the seedling growth quality. This study proposes a tea seedling growth simulation (TSGS) model based on deep learning. The Internet of Things system was used to measure environmental change during the whole seedling process. The correlation between the environmental parameters and the biomass growth of tea seedlings in various varieties was analyzed. A CNN-LSTM network was proposed to build the TSGS model of light, temperature, water, gas, mineral nutrition, and growth biomass. The results showed that: (1) the average correlation coefficients of air temperature, soil temperature, and soil moisture with the biomass growth of tea seedlings were 0.78, 0.84, and −0.63, respectively, which were three important parameters for establishing the TSGS model. (2) For evaluating the TSGS model of a single variety, the accuracy of ZM’s TSGS based on the CNN-LSTM network was the highest (Rp2 = 0.98, RMSEP = 0.14). (3) For evaluating the TSGS model of multiple varieties, the accuracy of TSGS based on the CNN-LSTM network was the highest (Rp2 = 0.96, RMSEP = 0.17). This study provided effective technical parameters for intelligent control of tea-cutting growth and a new method for rapid breeding.

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Exploration of research on Internet of Things enabled smart agriculture

Cited by 3 publications

References 19 publications

Role of Machine Learning and Internet of Thing in Agriculture- A Survey

Role of Machine Learning and Internet of Thing in Agriculture- A Survey

Cloud Computing for Smart Farming: Applications, Challenges, and Solutions

Environmental Simulation Model for Rapid Prediction of Tea Seedling Growth

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