A novel SMLR-PSO model to estimate the chlorophyll content in the crops using hyperspectral satellite images

Nandibewoor, Archana; Hegadi, Ravindra S.

doi:10.1007/s10586-018-2243-7

Cited by 7 publications

(4 citation statements)

References 10 publications

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“…The images which were obtained were given as input to a segmentation algorithm (Nandibewoor and Hegadi, 2019). Segmentation of an image is clustering similar property pixels into one cluster (Sufola et al, 2016a;Sufola et al, 2019;Ramya et al, 2015).…”

Section: Image Segmentationmentioning

confidence: 99%

“…The segmentation was done so that the image was represented in a more meaningful way so that it became easier to analyze (Sufola et al, 2018). Segmentation was done to separate the wanted part of the image from the background (Nandibewoor and Hegadi, 2019). Segmentation helps to distinguish the region of interest from the background of the image.…”

Section: Image Segmentationmentioning

confidence: 99%

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Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

2021

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Instinctive detection of infections by carefully inspecting the signs on the plant leaves is an easier and economic way to diagnose different plant leaf diseases. This defines a way in which symptoms of diseased plants are detected utilizing the concept of feature learning (Sulistyo et al., 2020). The physical method of detecting and analyzing diseases takes a lot of time and has chances of making many errors (Sulistyo et al., 2020). So a method has been developed to identify the symptoms by just acquiring the chili plant leaf image. The methodology used involves image database, extracting the region of interest, training and testing images, symptoms/features extraction of the plant image using moments, building of the symptom vector feature dataset, and finding the correlation and similarity between different symptoms of the plant (Sulistyo et al., 2020). This will detect different diseases of the plant.

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Section: Image Segmentationmentioning

confidence: 99%

Section: Image Segmentationmentioning

confidence: 99%

Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

2021

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“…In [13], a hybrid model was established for estimating the chlorophyll content from the crops utilizing HIS segmentation with active learning, which contains two important stages. First, it can utilize a sparse multinomial logistic regression (SMLR) method for learning the class posterior probability distribution with quadratic programming or joint probability distributions.…”

Section: Introductionmentioning

confidence: 99%

Squirrel Search Optimization with Deep Transfer Learning-Enabled Crop Classification Model on Hyperspectral Remote Sensing Imagery

et al. 2022

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With recent advances in remote sensing image acquisition and the increasing availability of fine spectral and spatial information, hyperspectral remote sensing images (HSI) have received considerable attention in several application areas such as agriculture, environment, forestry, and mineral mapping, etc. HSIs have become an essential method for distinguishing crop classes and accomplishing growth information monitoring for precision agriculture, depending upon the fine spectral response to the crop attributes. The recent advances in computer vision (CV) and deep learning (DL) models allow for the effective identification and classification of different crop types on HSIs. This article introduces a novel squirrel search optimization with a deep transfer learning-enabled crop classification (SSODTL-CC) model on HSIs. The proposed SSODTL-CC model intends to identify the crop type in HSIs properly. To accomplish this, the proposed SSODTL-CC model initially derives a MobileNet with an Adam optimizer for the feature extraction process. In addition, an SSO algorithm with a bidirectional long-short term memory (BiLSTM) model is employed for crop type classification. To demonstrate the better performance of the SSODTL-CC model, a wide-ranging experimental analysis is performed on two benchmark datasets, namely dataset-1 (WHU-Hi-LongKou) and dataset-2 (WHU-Hi-HanChuan). The comparative analysis pointed out the better outcomes of the SSODTL-CC model over other models with a maximum of 99.23% and 97.15% on test datasets 1 and 2, respectively.

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“…Similarly, for the application of fertilizer support for the corn plants a satellite image based approach is presented (18) . In (19) , a chlorophyll estimation approach with sat. Image is presented where the SMLR-PSO model extracts different features from spectral images to estimate the yield.…”

Section: Introductionmentioning

confidence: 99%

Real time satellite image based CCF approximation model for efficient sugarcane growth and yield estimation using artificial neural networks

Saranya¹,

Thenmozhi²

2020

IJST

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Objective: To improve the performance of an efficient satellite image based CCF (Color, Climate, Flow) approximation model is presented in this article. Method: We attempted for plant growth estimation and yield estimation using artificial neural networks. The model receives the satellite images and preprocesses to improve the quality of the image. From the quality improved image, the method extracts the color values. Further, the features like climate and flow features from the data set of the region have been extracted. Using these features, for different time window, the method generates number of neurons and initializes them with the set of features. The set of images from the data set are used to extract several features and used to train the network. The classification is performed according to the same set of features obtained from input satellite image and the other features of the region at current window. The method estimates CCF plant growth and yield support measures as result. Based on these values, the yield estimation is performed. Result: The proposed method improves the performance of plant growth up to 97.25 and the yield estimation performance is increased up to 985 which is higher than previous approaches. Provide comparative estimation. Novelty: The proposed CCF model consider color, climate and features in plant growth estimation which differ from other approaches by considering maximum features obtained from satellite images as well as features collected from ground truth in different time stamp.

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A novel SMLR-PSO model to estimate the chlorophyll content in the crops using hyperspectral satellite images

Cited by 7 publications

References 10 publications

Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

Symptom-Based Identification of G-4 Chili Leaf Diseases Based on Rotation Invariant

Squirrel Search Optimization with Deep Transfer Learning-Enabled Crop Classification Model on Hyperspectral Remote Sensing Imagery

Real time satellite image based CCF approximation model for efficient sugarcane growth and yield estimation using artificial neural networks

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