## 1 Introduction
One approach [12] is to model the spatial dependency to improve classification accuracy by scanning the image features outputted by the CNN model region-by-region from different directions (left to right, top to bottom, etc.) and input them to RNN to capture the sequential relationship. Another approach [13] works in a different direction by splitting the image into different patches and feeding them into the CNN model; the output features are then concatenated and input to RNN for better classification. In [14], researchers combined a CNN and RNN parallelly to extract image features and then used the perceptron attention mechanism to weigh the features extracted from the two models. In another simpler approach [15], the authors simply concatenated the output features from a CNN and fed them directly to the RNN as sequential information to achieve better classification results. Deep learning models reported in [9] and [16] can identify and categorize fruit images by combining CNN, RNN, and Long Short-Term Memory (LSTM) models to create a multi-model fruit image identification system. The CNN is used to develop discriminative features from the images, while the RNN handles sequential labeling tasks. The LSTM is employed to encode learning at each classification interval, enhancing the model's understanding of the data. When it comes to disease detection using leaf images, Ref. [17] proposes an approach that leverages pre-trained CNN networks like Xception, VGG16, and InceptionV3 to extract deep features from various fully connected layers. Additionally,an LSTM is employed to capture relationships among these image features. Transfer learning is used to facilitate feature extraction from pre-trained models. The deep features extracted from both the CNN and RNN layers are concatenated and inputted into a fully connected layer. However, most of the current studies in this direction face one of the three main gaps below:  
* Much of the research primarily studies improving accuracy without adequately considering the limitations of IoT devices or edge computing [18]. This oversight highlights the need to address the efficiency of models, including factors like time complexity and computational cost. Bridging this gap is crucial to ensure that disease detection models can effectively operate within resource-limited environments. This is particularly important for enabling real-time monitoring and intervention in agriculture, where timely detection and response are vital. Therefore, it is essential to take a balanced approach that aims for both accuracy and efficient use of computational resources for practical applications;
* Another significant aspect is that many of the existing studies utilize an LSTM as the primary RNN model for temporal dependency detection. However, LSTMs are computationally intensive, especially when dealing with high-dimensional datasets, such as those used in image classification. Ref. [19] demonstrates that LSTM models struggle to retain information when the input time steps T > 100, limiting their effectiveness with high-resolution images. Hence, there is a pressing need to explore alternative RNN models, such as the recently developed LTC model, to assess the impact of combining CNN and RNN architectures;
* Finally, while most studies focus on improving model architecture and testing on images captured in perfect conditions, it is necessary to validate lightweight models on real-life datasets that reflect the conditions in which images are captured in real-world scenarios [20]. These datasets should include images taken from different angles, under various lighting conditions, or with low-resolution cameras. Such validation is crucial to ensure that the models are feasible and perform well when applied in real-life applications. Addressing these research gaps, this paper proposes a hybrid lightweight model combining a CNN and an RNN to detect tomato plant diseases from leaf images, focusing on reducing computational costs while maintaining high accuracy.