A Systematic Literature Review on Image Preprocessing and Feature Extraction Techniques in Precision Agriculture

Sharmila, G.; Rajamohan, Kavitha

doi:10.1007/978-981-16-9416-5_24

Cited by 4 publications

(3 citation statements)

References 58 publications

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“…Among these areas, computer vision is perhaps the most interesting for scientists. Important image processing techniques include camera types, color spaces, color indices, and image segmentation related to agriculture applications and precision agriculture [12][13].…”

Section: Literature Reviewmentioning

confidence: 99%

An Image-Based Rice Weighing Estimation Approach on Clock Type Weighing Scale Using Deep Learning and Geometric Transformations

Tran

Kim

Nguyễn

2023

Adv. technol. innov.

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AI impacts surrounding human life, such as the economy, health, education, and agricultural production; however, the crop prices in the harvest season are still on manual calculation, which causes doubts about accuracy. In this study, an image-based approach is proposed to help farmers calculate rice prices more accurately. YOLOv5 is used to detect and extract the scales in the images taken from the harvesting of rice crops. Then, various image processing techniques, such as brightness balance, background removal, etc., are compiled to determine the needle position and number on the extracted scale. Lastly, geometric transformations are proposed to calculate the weight. A real dataset of 709 images is used for the experiment. The proposed method achieves good results in terms of mAP@0.5 at 0.995, mAP@[0.5:0.95] at 0.830 for scale detection, and MAE at 3.7 for weight calculation.

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Section: Literature Reviewmentioning

confidence: 99%

An Image-Based Rice Weighing Estimation Approach on Clock Type Weighing Scale Using Deep Learning and Geometric Transformations

Tran

Kim

Nguyễn

2023

Adv. technol. innov.

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“…At the preliminary stage, the images are preprocessed in two distinct ways: Bilateral Filtering noise elimination and contrast enhancement using CLAHE. The bilateral filtering makes use of nonlinear image denoising [25,34,35]. The linear principles of spatial Gaussian convolution that considers weighted factors for smoothing images.…”

Section: Fig 1 Proposed Odcnnf-stc Algorithm For Soil Nutrient Classi...mentioning

confidence: 99%

Optimal Deep Convolutional Neural Network based Fusion Model for Soil Nutrient Analysis

Sharmila.G

2023

IJRITCC

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The vast majority of people in India, agriculture is their main line of work, and it has a large economic impact.. Soil is important for supplying vital nutrients to crops for better yield. Determining soil nutrients is certainly essential for selecting appropriate crops and monitoring growth. Common methods used by agriculturalists are inadequate to satisfy increasing demands and have to obstruct cultivating soil. For a better crop yield, agriculturalists must have an awareness regarding the soil nutrients for a specific crop. There comes a need for using Deep learning methods in soil analysis that would help farmers in the domain. This study introduces an Optimal Deep Convolutional Neural Network Fusion Model for Soil nutrient Type Classification (ODCNNF-STC) technique. The presented ODCNNF-STC technique examines the input soil images to classify them into different nutrients present in the soil. In this approach, the noise present in the soil images are initially filtered using a bilateral filter (BF) followed by contrast enhancement. The preprocessed soil images are fed to the model formed by the fusion of DenseNet201 and InceptionResNetV2 models extracting the soil images that can successfully differentiate soil nutrients. Finally, classification of soil nutrients were performed by three classifiers namely extreme learning machine (ELM), RMSProp optimizer-based 1DCNN, and RMSProp optimizer-based Stacked Auto Encoder (SAE). The experimental validation of ODCNNF-STC method is examined on real-time dataset of soil images with a maximum accuracy of 99.39% over recent methods.

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“…In our study, we explored image preprocessing techniques like histogram equalization and adaptive histogram equalization, widely recognized in precision agriculture [25]. Notably, we observed their efficacy in controlled environments, such as watershed preprocessing and HSV in olive identification [12], and histogram equalization for plant disease detection [26].…”

Section: Introductionmentioning

confidence: 99%

Comparative Evaluation of Color Correction as Image Preprocessing for Olive Identification under Natural Light Using Cell Phones

Mojaravscki,

Graziano Magalhães

2024

AgriEngineering

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Integrating deep learning for crop monitoring presents opportunities and challenges, particularly in object detection under varying environmental conditions. This study investigates the efficacy of image preprocessing methods for olive identification using mobile cameras under natural light. The research is grounded in the broader context of enhancing object detection accuracy in variable lighting, which is crucial for practical applications in precision agriculture. The study primarily employs the YOLOv7 object detection model and compares various color correction techniques, including histogram equalization (HE), adaptive histogram equalization (AHE), and color correction using the ColorChecker. Additionally, the research examines the role of data augmentation methods, such as image and bounding box rotation, in conjunction with these preprocessing techniques. The findings reveal that while all preprocessing methods improve detection performance compared to non-processed images, AHE is particularly effective in dealing with natural lighting variability. The study also demonstrates that image rotation augmentation consistently enhances model accuracy across different preprocessing methods. These results contribute significantly to agricultural technology, highlighting the importance of tailored image preprocessing in object detection models. The conclusions drawn from this research offer valuable insights for optimizing deep learning applications in agriculture, particularly in scenarios with inconsistent environmental conditions.

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A Systematic Literature Review on Image Preprocessing and Feature Extraction Techniques in Precision Agriculture

Cited by 4 publications

References 58 publications

An Image-Based Rice Weighing Estimation Approach on Clock Type Weighing Scale Using Deep Learning and Geometric Transformations

An Image-Based Rice Weighing Estimation Approach on Clock Type Weighing Scale Using Deep Learning and Geometric Transformations

Optimal Deep Convolutional Neural Network based Fusion Model for Soil Nutrient Analysis

Comparative Evaluation of Color Correction as Image Preprocessing for Olive Identification under Natural Light Using Cell Phones

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