## 2 Related Work  
Smart agriculture has undergone some remarkable developments in recent years, driven by the integration of machine learning (ML) and deep learning (DL). A summary of some of the latest work is presented in Table 1. These technologies are being used to address critical challenges such as disease detection, crop classification, and fruit recognition, with a strong emphasis on improving accuracy and operational efficiency. The authors of [14] conducted a comparative study of seven ML techniques. The main objective was to detect diseases in economically important crops such as apples, grapes, maize, bell peppers, and tomatoes. Their work showed the superiority of Random Forest (RF), with an accuracy of 91.80%. A similar type of the work was carried out in [15] but with soybean. The authors performed soybean leaf disease classification with a combination of SWIN transformers and CNNs, achieving impressive accuracies ranging from 76% to 98% in multiple datasets, including PlantVillage and Embrapa. These studies demonstrate how well transformer-based models and ensemble approaches handle the complexities of agricultural data. In smart agriculture, fruit and crop classification has also gained the attention of researchers and farmers. The authors of [16] investigated the impact of transfer learning on fruit detection. They used the Fruits-360 dataset and achieved accuracies ranging from 65% to 99%. The ML models used in their research work were VGG16, ResNet50, and EfficientNet-B0. The authors of [17; 18] used CNNs to classify fruit images. Their classification accuracies were 98.17% and 97.15%, respectively. Their research work shows the significance of machine learning methods for fruit classification. The detection of crop leaf disease has also received significant attention, with studies such as [19; 20] suggesting the use a hybrid formed by merging Artificial Bee Colony (ABC) and Gray Wolf Optimization (GWO). The authors enhanced the feature selection process and improved classification accuracies. Their implementations achieved the classification accuracies of 98.53% and 95.69%, respectively. Their works highlight the potential of these hybrid algorithms in improving agricultural disease detection. Similarly, the research work carried out by [PERSON] et al. [21], merged Random Forest Optimization (RFO) with SVM and achieved an accuracy of 97.70% for leaf disease classification. Weed classification was performed by [PERSON] and [PERSON] [22] using a Region-Based CNN (RCNN), achieving a classification accuracy of 99.40%. This study emphasizes the importance of object detection methods in precision agriculture, especially when it comes to differentiating weeds from crops. Feature selection methods have become a key part of improving the performance of ML models. These methods help to increase model performance by reducing data dimensionality and removing unnecessary features. The research work carried out by the authors of [23] introduced a hybrid approach to intrusion detection by merging the Crow Search Algorithm and the Flower Pollination Algorithm. Their work resulted in the achievement of accuracies of 99.14% and 97.98% with AdaBoost. Another work carried out in [24] developed a hybrid of the Grasshopper Optimization Algorithm (GOA) and the Genetic Algorithm (GA) for the detection of cloud intrusion. The authors achieved classification accuracies of 98%, 99%, and 92% using different datasets. These studies show the role of hybrid optimization methods in the selection of features for security applications. At the same time, nature-inspired feature selection algorithms have gained popularity in diverse fields. An interesting work presented in [25] classified emotions. The authors utilized Gray Wolf Optimization (GWO) for EEG signal-based emotion classification and achieved accuracies of 89.63% and 84.54% with XGBoost. Leukemia classification was carried out in [26]. The authors employed entropy-controlled firefly feature selection and achieved classification accuracy above 96% with a multi-class SVM.