Advantages and disadvantages in agriculture

Generally, in machine learning it is not easy to analyse the unstructured data. For this type of data analysis, applying deep learning methods will be more useful where we can use different types of data formats to make algorithm work. To find the relation between different domains which are interdisciplinary we can use deep learning algorithm. Generally, workers get tired or irresponsible or neglect the small things, but in deep learning models that is not the case. The algorithm will perform thousands of cycles of work without any error that too in short period of time. Also, the quality of work will not be affected until and unless data input by the user have some problem. In traditional learning approach, identification of features needs to be accurate, whereas in deep learning models have ability to create new features by themselves. Generally, problem-​solving in machine learning is done by dividing big tasks in small tasks and combining the results of all the small tasks for the final output, whereas in deep learning tasks are solved on end-​to-​end basis. Deep learning requires large amount of data or information and it is expensive to use a deep learning model. One of the major disadvantages is that we are not able to find how the analysis is done inside the model. Generally, we call it black box, but sometimes knowing the analysis algorithm is important because interpretability is necessary in some domains. Nowadays as machine learning is growing in all the domains in a dramatic way, main fear is that machine learning may take all the work of humans and may drive humans into unemployment or slavery.