The economy of our country depends on crop cultivation. In India, agriculture serves as the backbone of a growing financial system, so maintaining this financial development is crucial. It makes a significant contribution to the global economic and agricultural prosperity of all nations. Unpredictable weather conditions and soil parameters are frequently the cause of low crop productivity. The main objective is to suggest an ML (machine learning) based agriculture system that can assist farmers regarding crops that can be harvested with specific values of soil and environmental parameters. Through several benchmark tests, LightGBM demonstrates improved performance in terms of prediction accuracy, model stability, and computing efficiency. This paper also evaluates the elements necessary to guarantee the optimal functioning of the crop recommendation system.

The use of effective IOT devices and decision learning for the prediction of crop growth in the agriculture field is encouraging ways to boost economic growth in the farming sector. Increasing operating costs and degradation of the atmosphere are the key issues in the area of agriculture. A predictive model with advanced data analysis is needed to process massive amounts of data collected through multivariate sensors deployed in the agriculture field. In classification predictive modeling achieving high accuracy is extremely challenging due to the high-imbalance characteristics of training data. There is a need to improve the classification performance of imbalanced data, which happens when there are insufficient instances of the data that represent either of the class labels. That also affects the robustness of the predictive model and significantly causes the loss of essential crop growth information and crucial details

This paper proposes an Optimized Cloud Intrusion Detection Framework (OCIDF) that uses multi-classifier security algorithms to improve detection performance of cloud systems in terms accuracy, adaptability, and efficiency. OCIDF uniquely combines decision-tree-based learners, ensemble boosted models, and lightweight deep neural networks into an adaptive and optimized layer, where the weights of classifiers are balanced dynamically against accuracy, latency and resource consumption. The framework is also designed to dynamically select features and have a feedback learning loop to adapt to new attack pattern, to guarantee strong resistant to zero-day threats. Experimental analysis indicates that OCIDF drastically outsmarts the current solutions, attaining 96%, 94%, 93%, and 93.5% accuracies, precision, recall, and F1-scores, respectively, and has a high throughput of 3200 instances per second