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Enhancing AI based evaluation for smart cultivation and crop testing using agro-datasets

Shibin David1, *, R.S. Anand2, Martin Sagayam3

Corresponding Author:

Shibin David


1 Department of Computer Science and Engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
Email: [email protected]
2 Department of mechanical engineering, Karunya Institute of Technology and Sciences, Coimbatore, India
 Email: [email protected]
3 Department of Electronics and Communication Engineering, Coimbatore, India
Email: [email protected]
*Corresponding Author: Shibin David, Email: [email protected]


In India, Agriculture is considered as the backbone of the country where 70 percent of the economy relies upon it. It requires the involvement of many natural resources including land, water, and energy. An astonishing factor is that 60 percent of the water diverted or pumped for irrigation is wasted via runoff into waterways or evapotranspiration. Although chemical fertilizers improve the growth of plants and increase the yields of fruits and vegetables in a relatively short period of time, there are certain shortcomings of using chemical fertilizers as an opponent to the use of organic fertilizers obtained from natural wealth. The persistent use of chemical fertilizers causes the pollution of ground water sources, or leaching. Since the chemicals present in the chemical fertilizers spoil the soil scraps, the inference of this will be a high impact on the soil with reduced drainage and in the air circulation. The synthesizers that are used for farming will adversely affect the nature and the pH of the soil. Nowadays water scarcity is considered to be major concern in the cultivation process. Alongside, yet another major problem faced in cultivation is the usage of lots of fertilizers which makes the land infertile.. In this paper, the nature of the work to sense various factors such as atmospheric temperature, soil moisture, rain, and pH value through a GSM module. Using the aforementioned factors, the farmers and landlords can able to predict how much water is required for the land and how much nutrients required for land. The technological advancement in artificial intelligence paves a way to detect the sensed values and predict whether a crop could be planted on the soil present in a region.


Arduino UNO, Soil moisture sensor, pH sensor, Temperature sensor, Rain drop sensor, GSM module, Agriculture land

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Cite This Paper:

Shibin David, R.S. Anand, Martin Sagayam (2020). Enhancing AI based evaluation for smart cultivation and crop testing using agro-datasets. Journal of Artificial Intelligence and Systems, 2, 149–167.


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