Abstract : Maintaining nutrition, increasing yield, and minimizing an overabundance all depend on prompt identification and accurate classification of pests and diseases in tropical fruit crops. However, current deep learning approaches frequently have significant computing expenses and little flexibility to actual farming operations. In order to accomplish excellent classification precision with considerably fewer processing necessities, this study proposes ESA-ResNet34-Lite+, a novel lightweight, attention-enhanced deep learning framework that can be used in mobile and Internet of Things-based farming systems. Optimizing extracting features and concentrating on disease-specific behaviours while lowering the complexity of the model is achieved by integrating separable convolutions by depth and Efficient Spatial Attention (ESA) modules within a modified ResNet34 foundation. Furthermore, greater levels utilize Convolutional Block Attention Modules (CBAM) to improve multi-scale visualization of features and accuracy in classification. To train and validate the approach, a carefully selected dataset of roughly 3,500 tagged photos of tropical fruit crops including important guava diseases like Phytophthora, Scab, and Styler was utilized. Investigations show that ESA-ResNet34-Lite+ outperforms standard models like VGG16, MobileNet, and ResNet34 by 2–4% in classification accuracy, with an overall accuracy of 95.7%, precision, recall, and F1-scores of 95.6%, 95.8%, and 95.7%, respectively. The model also achieves a 70% reduction in FLOPs and an 85% reduction in specifications when compared to traditional architectures, highlighting its effectiveness and appropriateness for real-time field implementation. These results demonstrate that ESA-ResNet34-Lite+ offers an adaptable and economical intelligent agricultural approach by striking a good balance between accuracy and computational cost. Through the demonstration of exceptional detection performance on a difficult tropical crop dataset, this study creates a useful and trustworthy foundation for precision gardening, facilitating prompt disease control and encouraging ethical farming practices..
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Abstract : Effectively processing range queries for big, distributed data sets remains a perennial problem throughout today’s data systems, when queries are multifeatured in particular. Traditional indexes, such as B-Trees, KD-Trees, and R-Trees, often don’t work well in distributed or high-dimensional environments due to scalability and integrative limitations. This research paper proposes AVL Tree based Multi-Feature Query Engine framework for optimal multi-feature range queries. Maintaining logarithmic time per query when coupled with collection size, AVL trees represent a highly efficient indexing model, most prominently when resultant sets are modest in size. It’s deployed in Python and describes building local AVL trees on partitioned data and mapping it to a distributed environment utilizing MapReduce’s Hadoop implementation. It suggests a highly efficient filtering of big collections by performing range criteria in a staged mode across features, which severely reduces execution time when compared to linear scans. It describes the usability of balancing trees within distributed querying systems and spans scalability between in-memory indexes and big data systems..
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