Complexity: Hybrid LSTM-KNN Approach for Analyzing the Severity of Strawberry Leaf Scorch in Science

Strawberry Leaf Scorch disease is a big danger to growing strawberries all over the world. It makes crops less and poorer in quality. This study introduces a new method using two combined deep learning models. One is called Long Short-Term Memory (LSTM) and the other K-Nearest Neighbors (KNN). They accurately sort badness levels of diseases into groups. A big set of 11,000 clear pictures showing different disease stages was carefully gathered and readied. The mix of LSTM-KNN models was very good, getting 96.8% correct in sorting disease seriousness levels. These show the model's strong results at varying levels of seriousness. The mix-up table helps to understand better where the model's accuracy is and how it might be improved. The results show the model works well in finding out how bad a disease is. This gives useful information for managing sicknesses on farms. The model's great accuracy and careful testing show it could be a dependable tool for quick action in reducing Strawberry Leaf Scorch disease. This study not only helps improve ways of classifying diseases but also shows how machine learning could be used in farming for accuracy. This mixed model works well to improve crop health, keep farming sustainable and strengthen global food safety.