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- W4226244987 abstract "Precision agriculture implies the technological fusion in agricultural management, and it ensures the highest crop yield at low cost by precise monitoring and control of parameters related to plant health status. The measurand from different sensors connected with wireless networks plays a vital role in efficient crop management with less manual intervention. The sensor fusion approach detects and understands parameters such as soil moisture, temperatures, salinity, climate conditions, and plant growth. The measured data is used for decision-making in the utilization of water, pesticides, and fertilizers. In rapidly growing precision farming, the early identification of disease aids to effectively prevent the plants from diseases and its negative impacts on crop yields. The conventional methods of disease identification are greatly dependent on the expert’s experience, also expensive and time-consuming. Hence, fast and precise methods are needed for the identification of plant diseases for efficient agricultural management. The extensive range of data from various sensors with the Internet of things provides a primary data source for acquiring disease information. To accurately identify the disease type, deep learning can be applied. Like humans, the deep learning approach can respond by training themselves from a vast amount of sensor data and associated image processing techniques. With the use of trained experience, it decides on its own for the accurate identification of plant diseases. This chapter explains the advancement in precision farming and the identification of plant diseases with a deep learning algorithm which further improves the food safety and efficiency of crop management." @default.
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- W4226244987 date "2022-04-04" @default.
- W4226244987 modified "2023-09-23" @default.
- W4226244987 title "Plant disease identification using IoT and deep learning algorithms" @default.
- W4226244987 doi "https://doi.org/10.1515/9783110734652-002" @default.
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