Advanced Crop Recommendation: AI Approaches for Precision Agriculture

Agriculture​‍​‌‍​‍‌​‍​‌‍​‍‌ serves as an instance where data-driven technologies are employed to tackle issues that arise from soil degradation, climate change, and incorrect crop selection, etc. Through accurate crop prediction, farmers can select the best crop that suits the soil and thus, more yield will be obtained and the farm will not lose its vitality.This research paper focuses on developing a crop prediction model using Machine Learning and Deep Learning algorithms such as Support Vector Machine (SVM), Naïve Bayes, and Bidirectional Long Short-Term Memory (BiLSTM). By analyzing the soil along with other factors (Nitrogen (N), Phosphorus (P), Potassium (K), pH, Moisture, Temperature, Humidity), the system determines the crop that can yield the maximum output. SVM and Naïve Bayes are selected as baseline machine learning models to compare because they are very efficient and capable of handling multi-class classification, whereas BiLSTM is used to uncover the deeper temporal patterns in soil and environmental data.BiLSTM outperformed other models on almost all the datasets used and is regarded as being superior to traditional machine learning methods because it handles the sequential data in both directions.The findings indicate that integrating soil analytics with advanced modeling significantly increases the accuracy of the models, makes better crop planning possible, and helps in the conservation of natural resources in agriculture.Finally, such a framework, therefore, provides a dependable, scalable, and smart manner of catering to the farmers' needs and leading them to the right decisions in terms of crop selection.