Deep-Learning-Based Multispectral Image Reconstruction from Single Natural Color RGB Image—Enhancing UAV-Based Phenotyping

Zhao, Jiangsan; Kumar, Ajay; Banoth, Balaji Naik; Marathi, Balram; Rajalakshmi, P.; Rewald, Boris; Ninomiya, S.; Guo, Wei

doi:10.3390/rs14051272

Cited by 16 publications

(5 citation statements)

References 69 publications

(103 reference statements)

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“…To the best of the authors' knowledge, this paper presents a novel and comprehensive approach that significantly differs from existing works in predicting crop yields. While previous studies have explored various methodologies, such as statistical modeling [7], and deep learning techniques [13,14], none have integrated multi-temporal images and machine learning algorithms in the manner proposed here.…”

Section: Our Main Contributionmentioning

confidence: 99%

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Pukrongta,

Taparugssanagorn,

Sangpradit

2024

Preprint

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This paper presents the PEnsemble 4 model, a sophisticated machine learning framework that integrates IoT-based environmental data to accurately forecast maize yield. With the projected significant growth in global maize demand over the next decade, the inherent risks posed by the crop’s dependence on weather conditions necessitate improved prediction capabilities. The PEnsemble 4 model, developed with high accuracy, incorporates comprehensive datasets encompassing soil attributes, nutrient composition, weather conditions, and UAV-captured vegetation imagery. By employing a combination of Huber and M estimates, the PEnsemble 4 model effectively analyzes temporal patterns in vegetation indices, specifically CIre and NDRE, which serve as reliable indicators of canopy density and plant height. In addition, this research significantly contributes to precision agriculture by offering an efficient and sustainable alternative to conventional farming practices through precise yield predictions. Notably, the PEnsemble 4 model enables earlier estimation, advancing the timeline for yield prediction from the conventional day 100 in the R6 stage to day 79 in the R2 stage. This improvement enhances decision-making processes in farming operations. The remarkable accuracy rate of 91% underscores the importance of adopting a multifaceted data approach that harnesses IoT-derived environmental insights. Additionally, the PEnsemble 4 model extends its benefits beyond yield prediction, facilitating the detection of water and crop stress, as well as disease monitoring in broader agricultural contexts. Ultimately, the PEnsemble 4 model establishes a new standard in maize yield prediction, revolutionizing crop management and protection through the synergistic utilization of IoT and machine learning technologies.

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Section: Our Main Contributionmentioning

confidence: 99%

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Pukrongta,

Taparugssanagorn,

Sangpradit

2024

Preprint

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“…However, they also revealed that the method provides accurate outputs only until appearance of senescence. Paper [10] proposes a deep-learning-based method to generate a multi-spectral (MSI) data from pure RGB images. They first reproduced RGB images from multi-spectral ones through a rendering model, which was built with a benchmark hyperspectral image dataset.…”

Section: Related Workmentioning

confidence: 99%

Estimation of Vegetation Indices With Random Kernel Forests

Devyatkin¹

2023

IEEE Access

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Vegetation indexes help perform precision farming because they provide useful information regarding moisture, nutrient content, and crop health. Primary sources of those indexes are satellites and unmanned aerial vehicles equipped with expensive multispectral sensors. Reducing the price of obtaining such information would increase the availability of precision farming. Several studies have proposed deep neural network methods to estimate the indexes from RGB color images. However, these studies report relatively large errors for mature plants, when highly non-linear relationships between image RGB bands and vegetation indexes arise. One could apply multilayer random forest-based models (Deep Forests) to solve this problem, but they have limited discriminative power and ability on catching non-linear relationships between image features. The cornerstone of the Deep Forests is that at each layer they enrich original features with embeddings containing empiric class probabilities from previous layers, although these probabilities deliver limited information. In this paper, we propose methods, which combine ideas of Deep Forests, Random Forests of multivariate trees, and global pruning of Random Forests to tackle these problems. We applied oblique (linear) and kernel (non-linear) trees as basic classifiers of the Deep Forest to improve discriminative power. We also utilized a method to refine Random Forests with a global loss optimization. This method helps to generate more expressive embeddings at each layer of the Deep Forest, which significantly improves results of the data analysis. In the experiments, we compared those methods with AlexNet and ResNet-based neural networks on several image classification datasets as well as on the NDVI prediction task. The experiments on image classification show that the proposed Deep Forest-based methods provide competitive results on datasets with small and medium size of feature-set. The results of the NDVI prediction task indicate that these methods are robust to senescence of plants and outperform neural network-based solutions.

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“…Multispectral and hyper-spectral aerial photographs are important in crop management because of their potential to demonstrate crop development. References [25,26] propose a new energy-balanced algorithm, and use the landsat8 images to estimate the potato crop yield.…”

Section: Models (Version)mentioning

confidence: 99%

Smart Techniques for LULC Micro Class Classification Using Landsat8營magery

Jamil¹,

Rehman²,

SaleemUllah³

et al. 2023

Computers, Materials &Amp; Continua

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Wheat species play important role in the price of products and wheat production estimation. There are several mathematical models used for the estimation of the wheat crop but these models are implemented without considering the wheat species which is an important independent variable. The task of wheat species identification is challenging both for human experts as well as for computer vision-based solutions. With the use of satellite remote sensing, it is possible to identify and monitor wheat species on a large scale at any stage of the crop life cycle. In this work, nine popular wheat species are identified by using Landsat8 operational land imager (OLI) and thermal infrared sensor (TIRS) data. Two thousand samples of each wheat crop species are acquired every fifteen days with a temporal resolution of ten multispectral bands (band two to band eleven). This study employs random forest (RF), artificial neural network, support vector machine, Naïve Bayes, and logistic regression for nine types of wheat classification. In addition, deep neural networks are also developed. Experimental results indicate that RF shows the best performance of 91% accuracy while DNN obtains a 90.2% accuracy. Results suggest that remotely sensed data can be used in wheat type estimation and to improve the performance of the mathematical models.

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Deep-Learning-Based Multispectral Image Reconstruction from Single Natural Color RGB Image—Enhancing UAV-Based Phenotyping

Cited by 16 publications

References 69 publications

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Enhancing Crop Yield Predictions with PEnsemble 4: IoT and ML-Driven for Precision Agriculture

Estimation of Vegetation Indices With Random Kernel Forests

Smart Techniques for LULC Micro Class Classification Using Landsat8營magery

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