This paper proposes a novel dynamic, distributive, and self-organizing entropy based clustering scheme (EBCS) that benefits from the local information of sensor nodes measured in terms of entropy and use that as criteria for cluster head election and cluster formation. It divides the WSN into two-levels of hierarchy and three-levels of energy heterogeneity of sensor nodes. The simulation results reveal that the proposed approach outperforms existing baseline algorithms in terms of energy consumption, stability period, and the network lifetime.

Artificial Intelligence (AI) is generating new horizons in one of the biggest challenges in the world’s society—poverty. Our goal is to investigate utilities of AI in poverty prediction via finding answers to the following research questions: (1) How many papers on utilities of AI in poverty prediction were published up until March, 2022? (2) Which approach to poverty was applied when AI was used for poverty prediction? (3) Which AI methods were applied for predicting poverty? (4) What data were used for poverty prediction via AI? (5) What are the advantages and disadvantages of the created AI models for poverty prediction? In order to answer these questions, we selected twenty-two papers using appropriate keywords and the exclusion criteria and analyzed their content. The selection process identified that, since 2016, publications on AI applications in poverty prediction began. Results of our research illustrate that, during this relatively short period, the application of AI in predicting poverty experienced a significant progress. Overall, fifty-seven AI methods were applied during the analyzed span, among which the most popular one was random forest. It was revealed that with the adoption of AI tools, the process of poverty prediction has become, from one side, quicker and more accurate and, from another side, more advanced due to the creation and possibility of using different datasets. The originality of this work is that this is the first sophisticated survey of AI applications in poverty prediction.

Maize ( Zea mays ) is a critical crop for global food security and economic stability. However, it is highly vulnerable to various diseases such as northern leaf blight, common rust, and maize lethal necrosis, which can lead to significant crop losses if not detected early. Traditional CNN-based models, while effective in extracting spatial features, often fail to capture subtle multi-scale variations necessary for distinguishing between disease symptoms. These models also suffer from high computational complexity when deeper layers are introduced to handle fine-grained details. Transformer-based models, on the other hand, provide long-range dependencies but come with significant computational overhead, limiting their use in real-time agricultural applications. To overcome these challenges, we propose MSCPNet, a novel architecture that combines a truncated MobileNetV2 backbone with a Multi-Scale Convolutional PoolFormer block. The truncated backbone ensures that only essential layers for general feature extraction are retained, enhancing the model’s adaptability across domains. The Multi-Scale Convolutional PoolFormer block captures both local and global dependencies through parallel convolutional branches of varying kernel sizes, while the PoolFormer module efficiently handles feature aggregation without the heavy computational cost associated with traditional attention mechanisms. This design allows the model to balance computational efficiency and high accuracy, making it highly suitable for real-time maize disease detection. Extensive evaluations on the maize leaf disease classification task yielded outstanding results, with the proposed MSCPNet achieving an accuracy of 97.44%, precision of 96.76%, recall of 97.37%, F1-score of 97.04%, and an MCC of 0.9653, with a model size of 998,084 parameters and 315,258,752 FLOPs. Furthermore, the model was evaluated on the PlantVillage dataset for tomato leaf disease classification, where it achieved an accuracy of 99.32%, precision of 99.32%, recall of 99.33%, F1-score of 99.32%, and an MCC of 0.9925. These results demonstrate the effectiveness and efficiency of MSCPNet in disease classification across different domains.

Agriculture plays the vigorous role in economy as well as it is contemplated to be the mainstay of economic system in emerging countries. Furthermore, it influences the society in huge ways including ancillary livelihoods, anyhow occurring of plant diseases impacts many farmers. Henceforth, erroneous leaf disease identification inclines to menacing effects on plants thereby curtailing product quality, productivity or quantity. To overcome these issues, we have designed Multi-agent DRL and EfficientNet assisted 3D plant leaf disease identification and severity estimation (MULTINET) framework. The proposed work comprehends of following four consecutive processes. At first, we have performed image pre-treatment for data cleansing using Adaptable LoW Pass Weiner (AWW) filter and then EMbellished Manta-Ray Optimization Algorithm (EMMARO) based data augmentation for enhancing image quality and balance the classes. After that, we have adopted Block Divider Model (BDM) for transfiguring 2D to 3D image which sustenance to navigate the information from multiple perceptive and bargains superior viewpoints from miscellaneous angles. Following that, we have executed dual effective segmentation on 3D image by utilizing Enhanced Deep Q-Network (EnDeep) that embrace of Haar-U-Net (HUNT) Algorithm for appropriate feature extraction and Convolution Feature Attention (CFA) Mechanism for dimensionality reduction. Here, the Multiple Deep Reinforcement Learning (DRL) agents are employed for segmentation and ensuring its accuracy through segmentation reliability checker which accommodates rewards entrenched on its actions. Based on those segmented regions, we have accomplished species detection and disease classification by Lightweight EfficientNet and Di-attention aided Bilinear-VGG (EBBI) Algorithm. At last, the severity enumeration is performed by contemplating lesions counts and its density. The proposed MULTINET framework is conducted in MATLAB R2020a simulation tool and the performance of proposed work is evaluated in terms of accuracy, precision, sensitivity, F1-score and ROC curve where the proposed work outperforms existing works.