<p>Plant diseases pose a serious threat to global food security and economic stability due to significant crop losses. Timely detection and management are essential to reduce these losses. Unlike manual methods, which are slow and error-prone, deep learning (DL) provides accurate, automated solutions that save both time and effort. This study presents BiDect, a Patch Context Modeling approach using cascaded Bidirectional Long Short-term memory (Bi-LSTM) for plant disease detection and classification. Images are first divided into smaller patches to capture local fea-tures that may be missed in global analysis. Individual Bi-LSTM networks process these patch features to capture spatial dependencies. The final Bi-LSTM layer combines these local insights to identify global patterns, enhancing classification accuracy. This cascaded approach focuses on processing smaller patches, enabling the model to capture intricate patterns effectively, which is particularly beneficial for large-scale datasets. We evaluated the method on diverse datasets, testing its performance in both real-world fields and controlled laboratory conditions. Comparison with state-of-the-art models, such as ResNet, DenseNet, InceptionV3, Xception, and VGG19, demonstrated that the cascaded BiLSTM network, combined with CNN features, outperforms these models, achieving superior classification accuracy for real-world applications.</p>