In this chapter, a novel method of apple plant disease detection based on computer vision and the ensemble machine learning methods is presented. Apple diseases like apple scab, fire blight and cedar rust are serious problems for agriculture that early detection can help reduce hits to the bottom line. This work presents a multistage approach combining state-of-the-art image preprocessing, segmentation and feature extraction to detect disease areas on apple plants from images. Preprocessing methods that smooth input resolution and equalize the appearance across its histogram, such as Gaussian smoothing or adaptive histogram equalization are applied first. K-means clustering has been used to group pixels with similar features, and Watershed algorithm is utilized for refining boundaries of disease-affected regions. From the segmented regions, informative features such as texture, color and shape are extracted which serve as input to a machine learning classifier that consists of an ensemble (SVM-KNN-RFC) set. An ensemble model uses voting techniques to leverage these classifiers’ individual strengths, which gives more accuracy, precision and recall. Experiments show that the ensemble obtains it 97.2% accuracy rating which clearly leading among other traditional methods and also deep learning models currently available Its robustness and scalability makes the system suitable for real-time deployment in agricultural scenarios, thereby providing a dependable early disease detection and crop management strategy. Future efforts will focus on extending the dataset, and using techniques such as hyperspectral imaging or stacked ensemble methods to enhance detection capabilities.