# Stacked Ensembles Powering Smart Farming for Imbalanced Sugarcane Disease Detection
## 1 Introduction
With the introduction of integrated pest management (IPM) and climate-resistant varieties alongside precision agriculture, SDGs 2 and 12 are achieved due to stable resource and crop consumption [19]. Moreover, those who implement IPM techniques are able to adapt to climate change and therefore achieve SDG 13 without placing pressure on the environment. These approaches to sugarcane farming enhance agricultural productivity while contributing to global sustainability goals in the long term [20]. Besides the above, various studies have discussed different types of diseases; Refs. [21; 22; 23] highlight the impact of red rot, smut, and yellow leaf diseases on sugarcane productivity and the economic losses associated with these diseases. A review article [23] provides an in-depth analysis of red rot disease and its significant impact on the sugarcane yield and quality. The authors also discuss the variability in the causal fungus and its effect on the treatment of this disease. These studies, among others, suggest and discuss the need for management strategies to accurately detect these diseases early on; however, traditional methods such as expert visual inspections are labor-intensive, time-consuming, prone to human error, and not scalable for large-scale applications [24; 25]. These limitations, coupled with increasing pressures on the field, have driven the industry to adopt precise, smart, agricultural, and sustainable practices to maximize production and mitigate losses [26; 27; 28]. Artificial intelligence (AI) has emerged as a powerful tool in various sectors [29; 30; 31; 32; 33], including agriculture [34; 35; 36], offering new and innovative solutions for disease detection and management [37; 38]. In particular, deep learning (DL) techniques [39], such as convolutional neural networks (CNNs) [40], have shown exceptional performance in image classification tasks by effectively extracting spatial and hierarchical features from image data. Although these capabilities make CNNs and other DL models a promising approach to detecting sugarcane diseases based on leaf imagery, they are computationally intensive and time-consuming and often require significant resources. This paper addresses these limitations by presenting a novel cascaded framework that integrates embedding techniques with conventional machine learning algorithms to classify sugarcane leaf diseases. Using InceptionV3, SqueezeNet, and DeepLoc, features such as the size, width, and length are extracted from leaf images. These embeddings summarize complex image information into meaningful representations, reducing the computational overhead. The imbalanced classes are then classified using a combination of pixel-based features and extracted attributes through artificial neural networks (ANNs), random forest (RF), support vector machines (SVMs), k-nearest neighbors (KNN), and a stacked ensemble model. The primary contributions of this study are as follows. * Feature embeddings using InceptionV3, SqueezeNet, and DeepLoc, derived from the physical attributes of leaves (e.g., size, height, and length), play a pivotal role in enhancing the model's interpretability and significantly improving the computational efficiency and accuracy. * The embedded techniques integrated in this study summarize complex image information into compact, meaningful representations and reduce the need for raw pixel processing in subsequent stages. * The embeddings are integrated into the training of various machine learning (ML) models, including ANNs, RF, SVMs, a stack ensemble model, and KNN. The cascade architecture takes advantage of the extracted embeddings and the conventional ML ensemble method, achieving a balance between speed and performance. * Compared to pure CNNs, recurrent neural networks (RNNs), long short-term memory networks (LSTMs), and gated recurrent units (GRUs), this cascaded approach demonstrates faster computation, as it avoids the overhead of end-to-end deep learning and surpasses its accuracy.