MACHINE LEARNING FOR PREDICTION OF CROP YIELD

According to the current surveys and research, it is observed that there is a proliferate increase in suicide rate of farmers over the years. The reasons behind this includes weather conditions, debt, family issues. Most of the times farmers are not aware of the crop which suits their soil quality, soil nutrients and soil composition. There are hardware devices which can check the soil quality. But is there any software to do this? If yes, is it economical ? Is it accurate? The answer to all these questions is ,the currently booming technology MACHINE LEARNING . This provides a feasible solution to farmers.

This work proposes to help farmers to check the soil quality depending on the analysis done based on data mining approach. Thus the system focuses on checking the soil quality to predict the crop suitable for cultivation according to their soil type .As the rate of farmers suicides are increasing, we want to help farmers to understand the importance of prior crop prediction, to flourish their basic knowledge about soil quality, understanding location-wise weather constraints, nutrients present in the soil in order to achieve high crop yield through our technology solution. Most of the existing systems are hardware based which makes them expensive and are difficult to maintain. Also they lack to give accurate results .Some systems suggest crop sequence depending on yield rate and market price. The system which we are proposing tries to overcome these drawbacks and predicts crops by analyzing structured data. The idea being “Prediction of soil quality using machine learning” certainly focuses on agricultural aspects. Being a totally software solution, it does not allow maintenance factor to be considered much. Also the accuracy level would be high as compared to hardware based solutions, because components like soil composition, soil type, pH value, weather conditions all come into picture during the prediction process.

Predicting nutrients:

Firstly, a data set containing parameters like soil type, climatic conditions(maximum temperature, minimum temperature, average rainfall), pH value of soil, previously grown crop should be taken. Also we can consider the amount of sunlight the soil or the land faces. Based on these parameters, we can predict the nutrients present in the soil using machine learning algorithms( Build a model, train the model and test the model). Then based on the type of nutrients present in the soil, we can predict the crop to be grown.

Recommending fertilizers:

Now once we know the type of crop, we can recommend the suitable fertilizers that can be used for high yield of a crop. To do all of these steps, we require adequate data . And as we increase the number of parameters in a data set, accuracy increases.