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Adaptive Boundary Learning and ETENSN-aware Mobile Tomato Crop Disease Monitoring for Precision Farming

Author(s) Lalitha Reddy Badam
Country United States
Abstract Tomato cultivation faces major yield losses due to fungal, bacterial, and insect diseases. However, existing methods lacked adaptive feature similarity boundary learning, reducing classification accuracy. Hence, this research work presents an adaptive similarity boundary learning and ETENSN-aware mobile tomato crop disease monitoring system for precision farming. Initially, input leaf images are pre-processed based on WF, and data are balanced. Then, the complex backgrounds are removed using the GMM approach. After the removal of the background, the RGB image is converted to HSV. Meanwhile, from the background-removed outcome, the vein structures are extracted using HGSSMM. From color converted image and the extracted vein structure, the features are extracted. Next, the feature similarity boundary is extracted from the extracted features using the ABDNN approach. After that, the extracted features, the pre-processed image, and the extracted similarity boundary are given as input to the ETENSN for disease classification. At last, for the obtained diseases, the nutrient is recommended using FTMIS to further preserve the farming. Experimental evaluation shows that the model attains 99.6235% accuracy, which is superior to existing methods.
Keywords Agriculture technology, Crop disease detection, Image processing, Mobile application, Tomato Leaf, Approximate Bhattacharyya Distanced Nearest Neighbor (ABDNN), and Efficient Transferred Elastic Net and Softplus Network (ETENSN).
Field Engineering
Published In Volume 7, Issue 3, March 2026
Published On 2026-03-05
DOI https://doi.org/10.70528/IJLRP.v7.i3.2004
Short DOI https://doi.org/hbrj2d
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