Authors: Kratee Pareek , Kumkum S A, Navyashree D S , Neha C, R Kasturi Rangan
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Shifting cultivation and its practice are said to be pernicious and eco-hostile from the standpoint of dependence of tribal people on forest-clad hill slopes. In this farming, the soil bone diseases are also reduced significantly. This is an example of subsistence, extensive, and arable farming. In the rainforest, it is one of the traditional forms of agriculture. The Amazonian Indians mostly do this farming in South America. They use the land for 2 to 3 years before moving to another area. There is a huge part of land which is used for Shifting cultivation for several years. Because of its spread, growing loss of potential green cover and related imbalance in eco-habitat, the Forest Policy, 1952 and the National Commission on Agriculture, 1976 suggested that shifting cultivation be banned, providing the tribal practitioner alternative systems of livelihood support. In this paper we are going to look at different methods and algorithms used to build AI models to predict Land used for Shifting Cultivation.
The history of shifting cultivation can be traced back to about 8000 B C in the Neolithic period which witnessed the remarkable and revolutionary change in man’s mode of production of food from hunters and gatherers to food producers. Shifting cultivation since its inception is identified with rotation of fields rather than rotation of crops, absence of draught animals and manuring, use of human labor only, employment of dibble sticks or hoe, and short period of occupancy alternating with long fallow periods to assist the regeneration of vegetation, culminating in secondary forests. Many social scientists have described shifting cultivation as a way of life of the societies practicing it.
It is found that the shifting cultivation fields and their surrounding forests provide two alternative sources of subsistence to the dependent population. In case the crops are not good or fail, the forest resource aid the farmer by augmenting their food supplies in addition to the provision of house building material, fuel wood and timber.
Shifting Cultivation is a process to utilize land to grow crops, then abandon it after taking the plant products. This process takes place for several years by tribal communities. Because of its spread, there is a loss of potential green cover, there is an imbalance in eco-habitat.
Thus, on the consideration of the Forest Policy-1952 and the National Commission on Agriculture-1976, suggested that the Shifting Cultivation be banned, providing Tribal partitioner alternative systems of livelihood. Immediate implementation isn’t that easy because this leads to many issues like Crop diversity and food availability, Challenges to the ecosystem and many more.
To solve these issues Indian Council of Agricultural Research (ICAR) needed the updated and accurate data related to land used for shifting cultivation to conduct research on it. As we know, Managing transformations in shifting cultivation areas and bringing shifting cultivators into the mainstream of economic development was a complex process. This issue was addressed to NITI Ayog, they suggested some actions to be taken in immediate, medium and long-term time frame. Bridging Data Gaps was one of them, for this data was advised to collect using Remote Sensing and Village Survey.
Remote sensing is a useful source to collect data from satellites and to classify land using Artificial Intelligence. Satellite images are pre-processed using image analysis tools like ERDAS to understand the Land Use Land Cover under specific areas. This data is further used to design
ML algorithms to classify land used for shifting cultivation.
A. General Procedure
In this section we will describe workflow of the research papers covered in this survey, using flowchart:
Dataset for the experiment was collected from the satellite, thus there were few images consisting of clouds or its shadow. Due to this the images were not clear, there was noise in the data. Statistical data was used to get the information of land used under agriculture, deforestation and area used to grow crops like rice. These data were useful to create scenarios and to know the feature of land with respect to shifting cultivation. Some parameters include human interference, like population density, the higher the density the more frequent land use change. Creating an ML model considering these parameters was a bit challenging.
Different land surfaces were used for mapping shifting cultivation but this method was found to be time consuming and biased. Thus through all these observations it was necessary to go towards more scientific parameters like NDVI ratio.
The Government of India is working on a project to study areas under shifting cultivation. Through this project we get an opportunity to contribute to their efforts. While working on this project an individual gets an exposure to open source technologies, used to collect satellite images. Much research has been conducted on this topic. Still it was observed that the remote sensing mapping is unable to provide correct data, as it doesn't reach the root cause of shifting cultivation. Government is expecting organizations to look for all of these issues. Through this project a person gets knowledge of various ML algorithms for image classification, we can also see few scientists have integrated ML algorithms to get the result. ISRO organization has also worked on this. Thus they have built a few websites through which a person can get related data for the project. Bhuvan is used for data collection and Vedas for data analysis. Through these sources a student can carry forward the project.
II. LITERATURE REVIEW
Below is the table providing an overview of all the Research papers considered in this survey.
Images are collected from various satellites and the image collected had huge time gaps so that the changes in land were easily observed. In a few papers we can see that the scientist had considered statistical data of area under LULC classes. Using Bhuvan website data can be collected having classified maps and maps showing NDVI ratio of different areas.
After going through all this research, we came to know about multiple parameters to classify land under shifting cultivation. To continue this project we have to focus on geographical parameters as well as on human intervention like population density and policies made by the government related to shifting cultivation. It is difficult to come up with an algorithm which can be applied to every area to classify shifting cultivation land, but using the upcoming technologies we can get some common parameters like NDVI ratio. Even after so many practices still in a study it was found that the remote sensing way of mapping can’t verify the facts at the grass-root to ascertain the factual realities.
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Copyright © 2022 Kratee Pareek , Kumkum S A, Navyashree D S , Neha C, R Kasturi Rangan. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.