Citation :

Anil Antony, Ganesh Kumar R, "Precision Food Crop Mapping Using Deep Neural Networks and Improved Dipper Throat Optimization Techniques," International Journal of Engineering Trends and Technology (IJETT), vol. 74, no. 4, pp. 87-100, 2026. Crossref, https://doi.org/10.14445/22315381/IJETT-V74I4P107

Abstract

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.

Keywords

Food Crop Classification, Remote Sensing, Levy Flight, Feature Extractor, Sobel Filter.

References

[7] L. Karthikeyan, Ila Chawla, and Ashok K. Mishra, “A Review of Remote Sensing Applications in Agriculture for Food Security: Crop Growth and Yield, Irrigation, and Crop Losses,” Journal of Hydrology, vol. 586, pp. 1-22, 2020.
[CrossRef] [Google Scholar] [Publisher Link]

[8] María F. Molina, and Secundino Marrero, “Climate Optimization in Greenhouses using the NARMA-L2 Model: An Advanced Integration of Environmental Variables,” Fusion: Practice and Applications, vol. 16, no. 1, pp. 253-263, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[9] Chuanliang Sun et al., “Using of Multi-Source and Multi-Temporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region,” Sensors, vol. 19, no. 10, pp. 1-23, 2019.
[CrossRef] [Google Scholar] [Publisher Link]

[10] Jasmin Praful Bharadiya, Nikolaos Tzenios Tzenios, and Manjunath Reddy, “Predicting Crop Yield using Deep Learning and Remote Sensing,” Journal of Engineering Research and Reports, vol. 24, no. 12, pp. 29-44, 2023.
[CrossRef] [Google Scholar] [Publisher Link] 

[11] Mengmeng Meng et al., “Crop Classification based on G-CNN using Multi-Scale Remote Sensing Images,” Remote Sensing Letters, vol. 15, no. 9, pp. 941-950, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[12] Ahmed S. Almasoud et al., “Remote Sensing Imagery Data Analysis using Marine Predators Algorithm with Deep Learning for Food Crop Classification,” Biomimetics, vol. 8, no. 7, pp. 1-17, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[13] Seungtaek Jeong et al., “Deep Learning-Enhanced Remote Sensing-Integrated Crop Modeling for Rice Yield Prediction,” Ecological Informatics, vol. 84, pp. 1-11, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[14] Qianhui Shen et al., “Statistical Texture Learning Method for Monitoring Abandoned Suburban Cropland based on High-Resolution Remote Sensing and Deep Learning,” IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 16, pp. 3060-3069, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[15] M. Pajany et al., “Optimal Fuzzy Deep Neural Networks based Plant Disease Detection and Classification on UAV-based Remote Sensed Data,” IEEE Access, vol. 12, pp. 162131-162144, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[16] Mamoon Ur Rasheed, and Syed Amer Mahmood, “A Framework Base on Deep Neural Network (DNN) for Land Use Land Cover (LULC) and Rice Crop Classification without using Survey Data,” Climate Dynamics, vol. 61, no. 11-12, pp. 5629-5652, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[17] Xue Wang et al., “Incorporating Multi-Temporal Remote Sensing and a Pixel-based Deep Learning Classification Algorithm to Map Multiple-Crop Cultivated Areas,” Applied Sciences, vol. 14, no. 9, pp. 1-27, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[18] Danial Sharifrazi et al., “Fusion of Convolution Neural Network, Support Vector Machine and Sobel Filter for Accurate Detection of COVID-19 Patients using X-ray Images,” Biomedical Signal Processing and Control, vol. 68, pp. 1-14, 2021.
[CrossRef] [Google Scholar] [Publisher Link]

[19] Hari Iyer, and Heejin Jeong, “PE-USGC: Posture Estimation-based Unsupervised Spatial Gaussian Clustering for Supervised Classification of Near-duplicate Human Motion,” IEEE Access, vol. 12, pp. 163093-163108, 2024.
[CrossRef] [Google Scholar] [Publisher Link]

[20] Yina Wang et al., “A Novel Prediction Method of Transfer-Assisted Action Oriented to Individual Differences for the Excretion Care Robot,” Sensors, vol. 23, no. 24, pp. 1-20, 2023.
[CrossRef] [Google Scholar] [Publisher Link]

[21] Ziqi Liu et al., “A Multi-Strategy Siberian Tiger Optimization Algorithm for Task Scheduling in Remote Sensing Data Batch Processing,” Biomimetics, vol. 9, no. 11, pp. 1-23, 2024.
        [CrossRef] [Google Scholar] [Publisher Link]