Recommendation System using Neutrosophic Logic in Agriculture
Keywords:
crop recommendation system; uncertainty; soil texture; neutrosophic logic; fuzzy; indeterminacyAbstract
For billions of people worldwide, agriculture provides food and a means of subsistence, making it an essential component of the global economy. Agriculture, one of the main industries, contributes to both economic stability and food security. To fulfill the expanding nutritional needs and maintain long-term resilience, however, innovation and sustainable techniques in agriculture are urgently needed as a result of the COVID-19 pandemic and continued climate change. Computational Intelligence is becoming more and more applicable in several automobile, industrial, and commercial sectors worldwide. Its ability to provide efficient and accurate functionalities attracts top companies to invest in A.I. because scientists and researchers believe that it will have a significant implication in the strife towards improving human life. Cultivating crops unsuitable to environmental conditions, such as soil and weather, is one of the main reasons behind the continuing decline in agricultural advances. One way to solve this problem is to apply the use of a recommendation system to predict favorable crops. Here, we are proposing a recommendation system based on neutrosophic logic. Neutrosophic logic is a promising tool for smart agriculture that can cope with the complexity and dynamism of agricultural systems. By incorporating neutrosophic logic into smart agriculture via IoT, it is possible to achieve more accurate, reliable, and robust solutions that can improve the quality and quantity of agricultural outputs while reducing the environmental and social impacts. The proposed model efficiently predicts the crop yield outperforming existing models like KNN, fuzzy logic, and neutrosophic logic.
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