Survey on Machine Learning and Deep Learning Techniques for Agriculture Land

Agriculture land is playing a vital role in developing the economy of Indian states and contributes ~ 15% of India’s gross domestic product (GDP). Moreover, agriculture is a major source of livelihood by engaging two-third (~ 66%) of the nation’s population in various activities such as food supply, the raw material to the industries, internal and external trade. Therefore, the continuous monitoring and mapping of agricultural land are crucial for the sustainable life and development of the country. Most of the agriculture monitoring solutions are based on field observations or conventional strategies which are time-consuming and costlier. However, remote sensing delivers a cost-effective solution of acquiring information regarding the healthy or unhealthy vegetation in agricultural land with the help of a diverse range of advanced geospatial techniques such as classification, change detection, and pan-sharpening. In the present paper, we have performed a systematic survey with respect to recent advancements made in the classification algorithm, especially for agricultural land. These emerging methods incorporated in classifiers are machine learning and deep learning to enhance and detect the various features of vegetation parameters. It is expected that such studies will provide effective guidance to the researchers in better understanding the features, limitations, and specific importance of emerging classifiers in the Agriculture domain.