Deep-Learning Framework for Optimal Selection of Soil Sampling Sites

• URL: http://arxiv.org/abs/2309.00974v1
• Date: Sat, 2 Sep 2023 16:19:21 GMT
• Title: Deep-Learning Framework for Optimal Selection of Soil Sampling Sites
• Authors: Tan-Hanh Pham, Praneel Acharya, Sravanthi Bachina, Kristopher Osterloh, Kim-Doang Nguyen
• Abstract summary: This work leverages the recent advancements of deep learning in image processing to find optimal locations that present the important characteristics of a field. Our framework is constructed with an encoder-decoder architecture with the self-attention mechanism as the backbone. The model has achieved impressive results on the testing dataset, with a mean accuracy of 99.52%, a mean Intersection over Union (IoU) of 57.35%, and a mean Dice Coefficient of 71.47%.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: This work leverages the recent advancements of deep learning in image processing to find optimal locations that present the important characteristics of a field. The data for training are collected at different fields in local farms with five features: aspect, flow accumulation, slope, NDVI (normalized difference vegetation index), and yield. The soil sampling dataset is challenging because the ground truth is highly imbalanced binary images. Therefore, we approached the problem with two methods, the first approach involves utilizing a state-of-the-art model with the convolutional neural network (CNN) backbone, while the second is to innovate a deep-learning design grounded in the concepts of transformer and self-attention. Our framework is constructed with an encoder-decoder architecture with the self-attention mechanism as the backbone. In the encoder, the self-attention mechanism is the key feature extractor, which produces feature maps. In the decoder, we introduce atrous convolution networks to concatenate, fuse the extracted features, and then export the optimal locations for soil sampling. Currently, the model has achieved impressive results on the testing dataset, with a mean accuracy of 99.52%, a mean Intersection over Union (IoU) of 57.35%, and a mean Dice Coefficient of 71.47%, while the performance metrics of the state-of-the-art CNN-based model are 66.08%, 3.85%, and 1.98%, respectively. This indicates that our proposed model outperforms the CNN-based method on the soil-sampling dataset. To the best of our knowledge, our work is the first to provide a soil-sampling dataset with multiple attributes and leverage deep learning techniques to enable the automatic selection of soil-sampling sites. This work lays a foundation for novel applications of data science and machine-learning technologies to solve other emerging agricultural problems.

Related papers

• Global-Local Progressive Integration Network for Blind Image Quality Assessment [6.095342999639137]
  Vision transformers (ViTs) excel in computer vision for modeling long-term dependencies, yet face two key challenges for image quality assessment (IQA) We propose a Global-Local progressive INTegration network for IQA, called GlintIQA, to address these issues through three key components.
  arXiv  Detail & Related papers  (2024-08-07T16:34:32Z)
• Rock Classification Based on Residual Networks [4.256045122451066]
  We propose two approaches using residual neural networks to tackle the problem of rock classification. By modifying kernel sizes, normalization methods and composition based on ResNet34, we achieve an accuracy of 70.1% on the test dataset. Using a similar backbone like BoTNet that incorporates multihead self attention, we additionally use internal residual connections in our model. This boosts the model's performance, achieving an accuracy of 73.7% on the test dataset.
  arXiv  Detail & Related papers  (2024-02-19T04:45:15Z)
• Domain Adaptive Synapse Detection with Weak Point Annotations [63.97144211520869]
  We present AdaSyn, a framework for domain adaptive synapse detection with weak point annotations. In the WASPSYN challenge at I SBI 2023, our method ranks the 1st place.
  arXiv  Detail & Related papers  (2023-08-31T05:05:53Z)
• Predicting Crop Yield With Machine Learning: An Extensive Analysis Of Input Modalities And Models On a Field and sub-field Level [24.995959334158986]
  We use high-resolution crop yield maps as ground truth data to train crop and machine learning model methods at the sub-field level. We use Sentinel-2 satellite imagery as the primary modality for input data with other complementary modalities, including weather, soil, and DEM data. The proposed method uses input modalities available with global coverage, making the framework globally scalable.
  arXiv  Detail & Related papers  (2023-08-17T12:40:38Z)
• One-Shot Learning for Periocular Recognition: Exploring the Effect of Domain Adaptation and Data Bias on Deep Representations [59.17685450892182]
  We investigate the behavior of deep representations in widely used CNN models under extreme data scarcity for One-Shot periocular recognition. We improved state-of-the-art results that made use of networks trained with biometric datasets with millions of images. Traditional algorithms like SIFT can outperform CNNs in situations with limited data.
  arXiv  Detail & Related papers  (2023-07-11T09:10:16Z)
• DeepDC: Deep Distance Correlation as a Perceptual Image Quality Evaluator [53.57431705309919]
  ImageNet pre-trained deep neural networks (DNNs) show notable transferability for building effective image quality assessment (IQA) models. We develop a novel full-reference IQA (FR-IQA) model based exclusively on pre-trained DNN features. We conduct comprehensive experiments to demonstrate the superiority of the proposed quality model on five standard IQA datasets.
  arXiv  Detail & Related papers  (2022-11-09T14:57:27Z)
• A Novel Hand Gesture Detection and Recognition system based on ensemble-based Convolutional Neural Network [3.5665681694253903]
  Detection of hand portion has become a challenging task in computer vision and pattern recognition communities. Deep learning algorithm like convolutional neural network (CNN) architecture has become a very popular choice for classification tasks. In this paper, an ensemble of CNN-based approaches is presented to overcome some problems like high variance during prediction, overfitting problem and also prediction errors.
  arXiv  Detail & Related papers  (2022-02-25T06:46:58Z)
• Optimization-Based Separations for Neural Networks [57.875347246373956]
  We show that gradient descent can efficiently learn ball indicator functions using a depth 2 neural network with two layers of sigmoidal activations. This is the first optimization-based separation result where the approximation benefits of the stronger architecture provably manifest in practice.
  arXiv  Detail & Related papers  (2021-12-04T18:07:47Z)
• Two-dimensional Deep Regression for Early Yield Prediction of Winter Wheat [0.0]
  We tackle the yield prediction problem using a Convolutional Neural Network (CNN) trained on data that combines radar satellite imagery and on-ground information. We utilize radar data acquired from the Sentinel-1 satellites, while the on-ground data correspond to a set of six features. We show that our proposed methodology yields better results than five compared methods.
  arXiv  Detail & Related papers  (2021-11-15T20:40:15Z)
• Pre-Trained Models for Heterogeneous Information Networks [57.78194356302626]
  We propose a self-supervised pre-training and fine-tuning framework, PF-HIN, to capture the features of a heterogeneous information network. PF-HIN consistently and significantly outperforms state-of-the-art alternatives on each of these tasks, on four datasets.
  arXiv  Detail & Related papers  (2020-07-07T03:36:28Z)
• Diversity inducing Information Bottleneck in Model Ensembles [73.80615604822435]
  In this paper, we target the problem of generating effective ensembles of neural networks by encouraging diversity in prediction. We explicitly optimize a diversity inducing adversarial loss for learning latent variables and thereby obtain diversity in the output predictions necessary for modeling multi-modal data. Compared to the most competitive baselines, we show significant improvements in classification accuracy, under a shift in the data distribution.
  arXiv  Detail & Related papers  (2020-03-10T03:10:41Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.