The need for Data mining for effective control of irrigation

The need for Data mining for effective control of irrigation

Irrigation is considered to be the most important agricultural activity worldwide. Water is essential for the development of plants. The use of ineffective ways of irrigation can hamper the crop yield and quality of crops. We need sophisticated sensory devices that can record data like temperature, climate, humidity, soil temperature, soil type (PH), minerals in the soil, etc. We also need a predictive computer system for weather forecasting, or that can store data of metrological parameters. To analyze these enormous data that are captured through modern agricultural equipment, we also need data mining to extract relevant data that can be utilized for effective control of irrigation of cultivable land.

It is to be noted that not all soils can be cultivable; some are impervious and nonporous. Hence the study of properties of soils is essential, and the use of sophisticated computers can be beneficial to make this thing happen. Also, the rainfall is unpredictable, and we need to make more accurate predictions using advanced and more robust up-gradation on the weather forecasting machines.

Techniques or algorithm that is used for the accomplishment of irrigational tasks are

• Naïve Bayes Algorithm:-An algorithm used for prediction of weather by using probability functions on a sophisticated computer to solve complicated calculations is called Naïve Bayes Algorithm.
• Decision trees:-It is a concept in a computer system that gives a flow route for making decisions. They are symbolic representations of decision-making statements.
• Genetic Algorithm:-Generally used for genetic mutations and computing the optimal global point.
• Support Vector Regression Method: -A method that doesn’t require the entire test data but considers the data that are likely to affect the output and ignore the rest using vector analysis is called support vector regression method. Here the separating hyperplane separates the support vector on a plane.
• DM algorithm:-The algorithm used in the data mining process is called the data mining algorithm. There are ten types of data mining algorithm namely C4.5, k-means, Support vector machines, Apriori, EM, Page Rank, AdaBoost, kNN, Naive Bayes, and CART
• Fuzzy logic:-Use of Artificial intelligence for reasoning purposes requires fuzzy logic. The results are not always accurate and are perceived as a guess.

How can we use these algorithms for the development of an irrigation system?

• Application of Naïve Bayes Algorithm:- We can use this algorithm in predicting the weather forecast. The probability functions can easily be computed in a sophisticated computer, which can calculate the probability close to reality.
• Application of Decision tree algorithm:-Decision tree algorithm can be used in an automated irrigational system that can optimize the requirements of the crop as per change in the temperature, dryness of the soil, etc.
• Genetic algorithms:-If the production of crops is slow as compared to the increasing speed of population; then we would not suffice the needs of food for people. Hence we must speed up the process of food production through genetic mutation of crops. Genetic modification can be simple to understand using a genetic algorithm.
• Unmanned aerial vehicles can be used for surveillance of cultivable lands.
• Artificial neural networks can be used for the prediction of crops to be sown.
• Data mining of scientific captured data can be useful for new innovative technology projects.
• Protection of crops can be done by the effective distribution of pesticides using DRONES.

Limitations of Data Mining in Irrigation

• Data mining for irrigation needs a sophisticated computer system and equipment that are too expensive for farmers to purchase. The cost and budget of the project need to be brought down.
• Too much dependency on machines can be risky and non-profitable as well.

To conclude, we can say that there are many benefits of using data mining for agriculture and irrigational projects. However, we need to cut down the price of the project so that it is not at all a burden for farmers to use these sophisticated machines that record data for scientific purposes.