TY - JOUR
T1 - Grow-IoT (smart analytics app for comprehensive plant health analysis and remote farm monitoring using smart sensors)
AU - Nigam, Rohan
AU - Rao, Meghana
AU - Dias, Nihal Rian
AU - Hariharan, Arjun
AU - Choraria, Amit
AU - Tendolkar, Atharv
AU - Manohara Pai, M. M.
N1 - Publisher Copyright:
© 2022 Institute of Physics Publishing. All rights reserved.
PY - 2022/1/11
Y1 - 2022/1/11
N2 - Agriculture is the primary source of livelihood for a large section of the society in India, and the ever-increasing demand for high quality and high quantity yield calls for highly efficient and effective farming methods. Grow-IoT is a smart analytics app for comprehensive plant health analysis and remote farm monitoring platform to ensure that the farmer is aware of all the critical factors affecting the farm status. The cameras installed on the field facilitate capturing images of the plants to determine plant health based on phenotypic characteristics. Visual feedback is provided by the computer vision algorithm using image segmentation to classify plant health into three distinct categories. The sensors installed on the field relay crucial information to the Cloud for real-time optimized farm status management. All the data relayed can then be viewed using the user-friendly Grow-IoT app to remotely monitor integral aspects of the farm and take the required actions in case of critical conditions. Thus, the mobile platform combined with computer vision for plant health analysis and smart sensor modules gives the farmer a technical perspective. The simplistic design of the application makes sure that the user has the least cognitive load while using it. Overall, the smart module is a significant technical step to facilitate efficient produce across all seasons in a year.
AB - Agriculture is the primary source of livelihood for a large section of the society in India, and the ever-increasing demand for high quality and high quantity yield calls for highly efficient and effective farming methods. Grow-IoT is a smart analytics app for comprehensive plant health analysis and remote farm monitoring platform to ensure that the farmer is aware of all the critical factors affecting the farm status. The cameras installed on the field facilitate capturing images of the plants to determine plant health based on phenotypic characteristics. Visual feedback is provided by the computer vision algorithm using image segmentation to classify plant health into three distinct categories. The sensors installed on the field relay crucial information to the Cloud for real-time optimized farm status management. All the data relayed can then be viewed using the user-friendly Grow-IoT app to remotely monitor integral aspects of the farm and take the required actions in case of critical conditions. Thus, the mobile platform combined with computer vision for plant health analysis and smart sensor modules gives the farmer a technical perspective. The simplistic design of the application makes sure that the user has the least cognitive load while using it. Overall, the smart module is a significant technical step to facilitate efficient produce across all seasons in a year.
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U2 - 10.1088/1742-6596/2161/1/012059
DO - 10.1088/1742-6596/2161/1/012059
M3 - Conference article
AN - SCOPUS:85124705222
SN - 1742-6588
VL - 2161
JO - Journal of Physics: Conference Series
JF - Journal of Physics: Conference Series
IS - 1
M1 - 012059
T2 - 1st International Conference on Artificial Intelligence, Computational Electronics and Communication System, AICECS 2021
Y2 - 28 October 2021 through 30 October 2021
ER -