Sistema de recomendação de culturas usando Machine Learning
DOI:
https://doi.org/10.5935/jetia.v10i48.1186Resumo
A agricultura é o principal campo de trabalho na Índia. Os agricultores enfrentam muitos problemas ao avaliar o rendimento de suas colheitas. A produção de culturas desempenha um papel importante em nossa economia indiana. Esse sistema proposto ajuda os agricultores a escolher as culturas adequadas com base na precipitação, umidade, tipo de solo, pH do solo e temperatura. Isso ajudará os agricultores a reduzir as perdas que enfrentam e a melhorar o rendimento. A aprendizagem automática desempenha um papel importante na área de cultivo de culturas. Este trabalho propõe um sistema de recomendação de safra usando técnicas de aprendizado de máquina, como k-vizinho mais próximo (KNN), rede neural artificial (ANN), floresta aleatória (RF) e máquina de vetor de suporte (SVM). Os modelos são simulados de forma abrangente em um conjunto de dados indianos. O modelo preditivo SVM teve uma precisão de 97,85% e um tempo de treinamento de 218,691 ms. O modelo preditivo K-NN apresentou uma precisão de 97,95% e um tempo de treinamento de 218,691 ms, e o RF apresentou uma precisão de 99,22% e um tempo de treinamento de 138,021 ms. Esse modelo é benéfico para os agricultores porque permite que eles saibam o tipo de cultura antes de cultivar o campo agrícola e, portanto, os incentiva a tomar decisões adequadas.
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