Machine Learning Based Approach for Crop Growth Monitoring in Hydroponics Cultivation
Keywords:
Hydroponics System, Machine Learning, Internet of ThingsAbstract
As the population of world's increases by the day, there is currently a challenge with how to fulfil everyone's food requirements. The development of crop growth required more time and water in traditional farming techniques. In agricultural farming, irrigation wastes a lot of water, and the supply of water for farming is dependent on rainfall. In our study, we employed hydroponic farming methods, which allow a crop to be grown in water rather than soil. We provide the essential nutrients required for plant through water so plant absorbed required nutrients from water. Today’s requirement is to save the water as the day by day the climate change and unpredictable rain, so in future we have to face the problems of water shortage. Artificial intelligence and machine learning techniques is used to monitor the crop growth in hydroponic environment. Our system automatically controlled itself by retrieving sensor values and taking the necessary actions. In hydroponics system, we can reuse water so that 70% of water we can save than the traditional farming. In our research, we find the accuracy of crop growth using machine learning techniques. In our research found that Support Vector Regression and Lasso has best result on (Coefficient of DeterminationR2=0.93), Support Vector Regression (SVR) have good results on (Mean Absolute Error =12.65 and Root Mean Square Error = 21.31) and Lasso Regression (LR) have good result on (Mean Square Error=4.51).
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