Supervised learning-based seed germination ability prediction for precision farming

Computer-aided decision-making, predictive analytics, and recommendations are the buzz in this era. Concerning this, machine learning and artificial intelligence systems have found as vital and critical concepts. One of the critical domains of human life is farming, which indeed requires precision in decision making in diversified phases of farming. One among them is notifying the germination ability of seeds to cultivate with optimal results in both formats of quantity and quality of the crop. The role of computer-aided methods in farming has significantly increased in the past decade, which often termed as precision farming. The contribution of this manuscript endeavored to define a supervised learning model that includes the method of optimal feature selection and performing binary classification to determine the germination ability of seed samples, which precisely termed as “Supervised Learning-based Seed Germination Ability Prediction for Precision Farming.” The diversified features of the microscopic images of both qualified and unqualified seeds shall consider as input corpus to perform the supervised learning in the proposed model. The significance of the model has scaled by performing experiments on the microscopic images of the paddy seeds, which considerably evincing the accuracy of the proposed approach with minimal false alarming. The contribution serves as a decision support tool to identify the quality seeds towards germination.