Precision Food Crop Mapping Using Deep Neural Networks and Improved Dipper Throat Optimization Techniques

Title

Precision Food Crop Mapping Using Deep Neural Networks and Improved Dipper Throat Optimization Techniques

Creator

Antony, Anil; Ganesh Kumar, R.

Description

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.

Source

International Journal of Engineering Trends and Technology;Volume;74;Issue;4;pp.87-100

Date

01-01-2026

Publisher

Seventh Sense Research Group

Subject

Feature Extractor; Food Crop Classification; Levy Flight; Remote Sensing; Sobel Filter

Coverage

Antony A., Department of Computer Science & 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 & Engineering, CHRIST (Deemed to be University), School of Engineering and Technology, Kengeri Campus, Bangalore, India

Rights

All Open Access; Bronze Open Access

Relation

ISSN: 23490918;

Format

online

Language

English

Type

Article

Identifier

https://doi.org/10.14445/22315381/IJETT-V74I4P107

https://www.scopus.com/pages/publications/105039496365?origin=resultslist