Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Antony, Anil; Ganesh Kumar, R. Title A name given to the resource Precision Food Crop Mapping Using Deep Neural Networks and Improved Dipper Throat Optimization Techniques Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived International Journal of Engineering Trends and Technology;Volume;74;Issue;4;pp.87-100 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.14445/22315381/IJETT-V74I4P107" target="_blank" rel="noreferrer noopener">https://doi.org/10.14445/22315381/IJETT-V74I4P107</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105039496365?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105039496365?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Antony A., Department of Computer Science &amp; Engineering, CHRIST (Deemed to be University), School of Engineering and Technology, Kengeri Campus, Bangalore, India, Department of Computer Science and Engineering, Sahrdaya College of Engineering and Technology, Kerala, Thrissur, India; Ganesh Kumar R., Department of Computer Science &amp; Engineering, CHRIST (Deemed to be University), School of Engineering and Technology, Kengeri Campus, Bangalore, India Description An account of the resource In recent times, the use of Remote Sensing (RS) data obtained from Unmanned Aerial Vehicles (UAVs) has gained significant popularity in crop classification tasks, including crop mapping, yield prediction, and soil classification. The classification of food crops utilizing RS Imageries (RSI) is a major application of RS tools in crop growing. Meeting the conditions for investigating these data requires more difficult approaches, and Artificial Intelligence (AI) technologies offer the mandatory support. Because of the variation and division of crop planting, archetypal classification methods have fewer classification outcomes. This manuscript focuses on the design and execution of a Leveraging Enhanced Dipper Throat Optimization Algorithm with Dipper-Inspired Precision Classification for Remote-sensed Optimized (DIP-CROP) Processing methodology. The drive of the DIP-CROP algorithm is to classify distinct types of crops that exist in remote sensing. At first, the DIP-CROP model applies image processing using the Sobel Filter (SF) to eliminate the noise. Next, the presented DIP-CROP technique takes place SqueezeNet model is employed for the feature extractor. To classify the food crop types, the DIP-CROP approach utilizes a Multi-Head Attention-based Bi-directional Long Short Term Memory (MHA-BiLSTM) algorithm. For hyperparameter tuning of the MHA-BiLSTM classifier, the Enhanced Dipper Throat Optimization Algorithm (EDTOA) will be applied in this work. The optimization process utilizes Levy flight distribution, which is known for its faster convergence due to efficient exploration of the search space. Levy flights can be used to take larger steps in exploration, which prevents getting stuck in local minima and accelerates convergence. The performance of the DIP CROP method is examined experimentally using a benchmark database. Experimental results affirmed the superior solution of the DIP-CROP algorithm over existing methods. 2026 Seventh Sense Research Group. Subject The topic of the resource Feature Extractor; Food Crop Classification; Levy Flight; Remote Sensing; Sobel Filter Publisher An entity responsible for making the resource available Seventh Sense Research Group Relation A related resource ISSN: 23490918; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Bronze Open Access Format The file format, physical medium, or dimensions of the resource online