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All Journal Logistic and Operation Management Research
Deshmukh, Bhagyashri M.
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Decision Sciences, Operations Research & Management

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Red-Green-Blue (RGB) Image Classification Using Deep Learning To Predict Sugarcane Crop Age Pawar, Swati. P.; Pawar, Prashant. M.; Deshmukh, Bhagyashri M.
Logistic and Operation Management Research (LOMR) Vol. 3 No. 2 (2024): Logistic and Operation Management Research (LOMR)
Publisher : Research Synergy Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/lomr.v3i2.2774

Abstract

Traditional sugarcane growth monitoring methods are time-consuming and error-prone. This study investigated the use of deep learning to automate and enhance the accuracy of sugarcane growth stage classification. The study develops deep learning-based system that leverages high-resolution drone imagery for precise sugarcane age classification, thereby enabling accurate identification of the growth stages. High-resolution drone images were captured at various stages of sugarcane growth and were stitched together to form a comprehensive dataset. Segmentation of isolated areas of interest for analysis. The ResNet-50 deep learning model, enhanced with an additional fully connected layer, was used to classify the growth stages. The model was trained on cropped image sections, and its performance was compared to other deep learning architectures, such as GoogLeNet and VGG, to evaluate its accuracy. The ResNet-50 model outperformed other architectures, achieving 91% accuracy in classifying growth stages, demonstrating its effectiveness in agricultural image analysis and its potential to advance precision agriculture. This study is the first to apply deep learning to sugarcane age classification using high-resolution drone imagery, and it sets a new benchmark for agricultural image analysis. The dataset containing drone images from specific sugarcane fields may limit the model’s generalizability to different regions and environmental conditions.
Advancing Sugarcane Farm Management Through NDVI-Based Color Mapping and Drone Imaging Pawar, Meenakshi. M.; Pawar, Mukund. M.; Pawar, Pranav M.; Deshmukh, Bhagyashri M.
Logistic and Operation Management Research (LOMR) Vol. 3 No. 2 (2024): Logistic and Operation Management Research (LOMR)
Publisher : Research Synergy Press

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.31098/lomr.v3i2.2824

Abstract

Precision agriculture has emerged as a key strategy for boosting crop productivity and optimizing resource use. This study leverages advanced imaging and machine learning to enhance the management of sugarcane farms. Using drones, high-resolution RGB images of sugarcane fields are captured and transformed into multispectral images through a Generative Adversarial Network (GAN), revealing critical spectral data for plant health assessment. The Normalized Difference Vegetation Index (NDVI) is derived from these multispectral images and serves as a vital measure of vegetation health. This NDVI data, combined with farmer-reported yield information, creates a comprehensive dataset linking NDVI values to actual crop yields. To predict sugarcane yield from NDVI values, we trained a feedforward neural network on this integrated dataset. The proposed method not only enhances prediction accuracy but also provides valuable insights into the connection between NDVI metrics and crop performance. The model was validated using individual field images, enabling precise yield predictions for different field sections. This study highlights the effectiveness of integrating drone imagery, machine learning, and remote sensing in precision agriculture. The combination of NDVI data with yield information provides a robust tool for optimizing sugarcane production, improving farm management decisions, and advancing agricultural sustainability.
Co-Authors Pawar, Meenakshi. M. Pawar, Mukund. M. Pawar, Pranav M. Pawar, Prashant. M. Pawar, Swati. P.