Artificial intelligence or more specifically machine learning (ML) is a powerful tool that has capability to learn from data and make predictions. Since rural schools may appear quite distant from the world of technology and data, teaching machine learning can be made fun and easy using agricultural data from the region. As a science, Agriculture is closely tied to rural areas, and when the concepts of Machine Learning are brought into the mix and integrated with practical lessons in Farming, the result is not only the practical skills obtained by the participants/learners but also an understanding of how data can be used to revolutionalise the existing practices in the field.
1.Introduction to Machine Learning
Machine learning is a subset of Artificial intelligence which relates to allowing computers to learn through data than being coded. Patterns that include analysis of large datasets of information can be achieved through algorithms, and ability to make prediction. Thus, learning with ML integrated with local farming data helps students get the sense of applicability of the technology to agriculture, which is real and allows them experiencing the subject in picture.
2.Using Local Farming Data for Learning
Most agricultural related activities produce tremendous data basis with regards to crop production and quality and rates and frequencies of soil moisture content, weather conditions, irrigation and pest infestation. By obtaining and utilizing local farming data, learners in rural schools will be able to use and develop machine learning models that will enable them to forecast about their environment. Thus, teachers have an opportunity to explain to the students how data are collected, cleaned and used for model training while working with real data of local farms.
For instance, students may assemble quantitative information on yield of crops in several seasons. They can capture parameters such as rain fall, type and quality of soil, temperatures and even the use of fertilisers. On this raw data, it is then easier to reason as to how machine learning can be used in the finding of such ‘hidden’ patterns and correlations.
3.Teaching Supervised and Unsupervised Learning
Machine learning can be divided into two main types: Division of the learning methods into two broad categories, namely the supervised learning and unsupervised learning. Supervised learning relies on a set of data with known results which taken as reference to draw prediction. Unsupervised learning operate with unprotected data, in other words the model identifies the hidden outcome without being informed of the result.
Two of the topics involve percentages and therefore it is logical to use local farming data to teach the concepts in the rural schools.
Supervised Learning: Similar to what teachers do to students in predicting mark prices base on historical pricing data, teachers can assist students in estimating crop yields in the future based on farming history. For instance, to teach the model depending on the weather conditions the students may input historical rainfall and temperature data along with corresponding crop yields; thus, the model will be able to predict what the harvest might be like given certain weather conditions.
Unsupervised Learning: This can be taught by letting the students to engage in lets say, unsupervised learning where they are given the metrics of the soil quality, and they have to come up with which kind of soil yields best crop under what weather conditions.
Such aspects are mastered in these exercises while seeing how some of the built models can be used in agricultural practices.
4.Predictive Analytics for Agriculture
The application of machine learning in agriculture that is of biggest utility is forecasting. Students can use different datasets provided by local farmers to create models that estimate essential results including, for example the appropriate time to sow seeds, pest invasions or chances of a drought.
For instance students could study the temperature data of a certain area in the past years in relation to crop production so as to be in a position to propose the right time for planting seeds for planting. Students are therefore able to develop models combining the different factors with potential for farmers to make key decision based on data. It is a way of learning that goes beyond Year 2 training that only focuses on the learning and mastering of Machine learning but exposes the student to real-life problems and expects him/ her to come up with solutions using technology.
5. Optimizing Resource Use with ML Models
Others are the application of machine learning in improving the usage of other scarce resources such as water, fertilizers and pesticides in farming. I have incorporated real data on soil moisture levels, forecasts of daily temperature, and other related factors that enable students to create models of appropriate irrigation frequency. This is especially helpful in regions where there may be a ration of the tools in concern hence using them efficiently is very important.
Through this approach, the teachers can make the students develop machine learning models that shall suggest the quantity of water or fertilizer to be used given some factors that affect their yields to reduce wastage. Thus, the student also gets to understand environmental sustainability and several technical factors concerning the construction of the prediction models.
6.Hands-On Learning with Farming Technology
In order to make machine learning even more comprehensible, the teachers include the actual projects that involve the usage of the technologies such as sensors and mobile applications associated with simple farming equipment. To illustrate, the students can obtain actual data through sensors fixed on the farms around concerning the moisture content, temperature and humidity of the soil. This data also can be uploaded and exercised for ML models training in the learning environment.
For instance, students could design a project which would include soil moisture sensors with irrigation systems to turn on as soon as the moisture level in the soil is low. They could use statistical models which could predict future irrigation needs based on previous data trends. This makes the students to experience oneness of the use of machine learning in the improvement of farming
Conclusion
Applying machine learning assignments While using data set on local farming in rural schools gaps the digital divide between the schools and the modern technological innovations with the lives of the students and farmers in the rural areas. When young people are shown how what they learn in their classrooms can be relevant to present-day matters like the use of artificial intelligence and machine learning in farming, then the student will remain engrossed in the teacher’s lessons. Apart from getting technical skills in the use of technology and data analysis, the students will also learn the basics of using better technology in the improvement of their respective communities through the approach adopted by Agrivomit. Such an approach creates a better affiliation between education, technology, and local culture so that learners can embrace the future fir where machine learning and agriculture interplay.
