Development of High-Quality Crops using Optimized Machine Learning in Smart Agriculture Environment

It is impossible to separate a country's economic success from its agricultural sector. Factors including population growth, regular changes in weather patterns, and dwindling resources make it challenging to supply the food needs of the existing human population. Precision agriculture or "smart farming" is a relatively new method that has shown promise in addressing the critical problems with agricultural sustainability. To put it simply, this cutting-edge technology wouldn't be where it is now without Machine Learning (ML) serving as its driving force. That is to say, it gives the machine the ability to pick up new skills without being taught them explicitly. ML is a revolutionary new method for studying and fixing intractable issues in the agricultural sector. The need to increase the efficiency of a learning model is a real-world consequence of using a machine learning algorithm. A strategy for optimising the hyper-parameters of an ML classical is presented in this research. The procedure of designing the optimal perfect construction via the optimal configuration of hyper-parameters is known as hyper-parameter tuning. It is generally agreed that tweaking hyper-parameters is crucial to creating a robust ML model, and this is especially true for the Support Vector Machine (SVM). Paddy crop results can be analysed using a variety of tools, including accuracy, precision, recall, and the F-measure.