Agriculture Land Evaluation Model (ALEM): A decision support approach for sustainable development

Agriculture has become the most predominant occupation of India, and the GDP value depends on the outcome of farming practices. The failure of agriculture will affect production in other sectors, leading to a decrease in the country’s GDP. Nearly 70% of the population relies on agriculture for livelihood and employability. Arable land is getting diminished due to the tremendous increase in population. Also, farmers lack the technical knowledge to overcome the challenges like variable climatic conditions, unhealthy soil due to repeated cultivation in the same land, floods and other unexpected natural disasters. The success of agricultural development begins with identifying the suitability of land for cultivation, and it is essential for sustainable development. The suitability of the arable land can be evaluated using several parameters such as soil, water, etc. Thus, the Agriculture Land Evaluation model (ALEM) is proposed in this paper to assist farmers in identifying the suitability of their agricultural land for crop cultivation by considering several factors. Various methods, such as Shannons Entropy method, PROMETHEE and the soft set algorithm, have been applied for the development of the mathematical model. Shannons entropy method is used for calculating the priorities of the evaluation factors considered for decision-making. The weighted PROMETHEE method is applied for generating the preference values of the agriculture dataset, and the bijective soft set algorithm is used for the generation of classification rules. The developed model is validated using various farm datasets and opinions from experienced professionals in the relevant field. Furthermore, the model performed well with more than 90% across different metrics when compared to other machine learning algorithms like Na"ive Bayes, KNN, decision Tree, Random Forest and SVM. Thus, ALEM proved to provide better results for the given problem and will provide greater insights for researchers in the field.