Machine Learning based Mobile Applications for Autonomous Fertilizer Suggestion

Raviraja, S.; Raghavender, K. V.; Sunagar, Prashant; Ragavapriya, R.K.; Kumar, Munish; Bharath, V G

doi:10.1109/icirca54612.2022.9985721

Cited by 5 publications

(1 citation statement)

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“…However, recent natural disasters have presented serious difficulties for farmers. [5].The choice of inappropriate crops profoundly impacts farmers, depleting available resources like seed cost and fertilizers [17]. It is essential to check the soil to determine the availability of nutrients in soil for plant growth before applying fertilizer.…”

Section: Introductionmentioning

confidence: 99%

The Smart Agriculture System Using IOT and ML

Siddhi Vijay Ghadge

2024

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Numerous countries have abundant resources, including land, rivers, groundwater, the environment, with agriculture serving as the primary source of income for many people in country. Nonetheless, resource shortage has grown in recent decades, particularly for groundwater and river water, so remote control smart irrigation system is developed to avoid access use and loss of water in the agriculture. A lack of information about the best use of available resources leads to increased resource consumption and worse crop yields. Choosing crops that are unsuited for the soil or climate reduces both quality and quantity of crop. As a result, system that recommends appropriate crops and fertilizers based on soil NPK values, soil colour, Season, PH, Rainfall, temperature etc has been developed. Fires are one of the most destructive global disasters, thus early detection is necessary to avoid the damages in agriculture. To reduce losses, an automated system capable of early fire detection through alarm systems and prompt extinguishing procedures through the roof structure pipeline in the farm is developed. Another use of that same pipeline is for spraying the pesticides and fertilizers. A potential resolution to address all these challenges involves the development and implementation of an intelligent agricultural system integrating Internet of Things (IOT) and Machine Learning technologies.

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Section: Introductionmentioning

confidence: 99%