Prediction of Crops Based on a Machine Learning Algorithm

Agriculture is crucial to any country's economy. Farmers around the world face a constant challenge in trying to keep up with the rising demand for food crops in the face of fluctuating climates and an alarming rise in population. One of the most widely grown cereals, wheat supplies a significant portion of the world's main food supply. This heat-sensitive crop is being severely harmed by the unusual rise in environmental temperature and decrease in the amount of rainfall. Scientists from all around the world have been looking at what are called Climate Sustainable Agriculture Practices in an effort to boost wheat crop yields while reducing the impact on the environment. Predicting crop yields before harvest can assist scientists and farmers evaluate risks and implement preventative actions to maintain a consistent agricultural harvest. There are two main types of models used to predict harvest yields: crop growth models and data-driven models. The time, money, and accuracy costs associated with using crop growth models to predict crop yields stem from the fact that these methods are sensitive to environmental variables. So, the farmer can't do anything in the nick of time to boost his crop's production. With the advent of machine learning algorithms, data-driven models have become even more effective at a fraction of the cost of traditional empirical models. Machine learning and Machine learning have come a long way, but they haven't been completely used for precise crop output forecasting. With this study, the authors want to provide a reliable method for estimating future wheat harvests in one of India's Punjab provinces. For precise and timely wheat crop yield prediction, an KNN with DT hybrid Machine learning model is proposed. To further improve the model's performance, the researchers used a genetic algorithm to tune the KNN-two DT's most crucial hyper parameters: the size of its window and the number of neurons in its hidden layer. This study has also examined the impact of environmental factors on agricultural yield in order to isolate the most important environmental parameters for future regulation and monitoring in order to more accurately predict crop yield. The proposed model for predicting wheat crop yield was tested with a battery of trials. The effectiveness of the suggested model was validated through a comparative comparison with state-of-the-art approaches for yield prediction. Farmers, policymakers, and planners can all benefit greatly from the proposed study by improving their ability to make informed decisions and take corrective and preventative action to boost agricultural yields.