Remote Sensing Applications: Society and Environment 31 (2023) 100984
Available online 19 May 2023
2352-9385/© 2023 Elsevier B.V. All rights reserved.
Contents lists available atScienceDirect
Remote Sensing Applications: Society and Environment
journal homepage:www.elsevier.com/locate/rsase
A multi-spectral image-based high-level classification based on a modified SVM with enhanced PCA and hybrid metaheuristic algorithm
Ramalingam Sugumar, D. Suganya
*PG & Research, Department of Computer Science, CHRISTHURAJ College, Panjappur, Trichy, India
A R T I C L E I N F O Keywords:
Satellite images Crop classification Multi-spectral images UAV platform
Precision agriculture and implementation
A B S T R A C T
In the context of Precision Agriculture (PA), an extremely fine crop classification has recently grown in importance. Satellite photos are often used to identify land use. Furthermore, this classi- fication poses new difficulties since in addition to utilising the multi-temporal features of the multi-spectral images; it also calls for pixel-based examination and a greater number of trainings.
Multiple machine learning methods are applied to multi-spectral and multi-temporal satellite data in this paper to create crop categorization models. The UAV platform is used for monitoring the crop field. The multispectral photo is caught as the intake figure which is taken out of the dataset. In precision agriculture, the pre-processing is performed for removing the noise content present in the input image. Feature section is done through PCA and the kernel modified SVM is used for classification. The implementation is performed using PYTHON platform. Paddy and Wheat are selected for the similarity. The accomplishment of the considered methodology is par- alleled with and without the optimization, and also compared with the existing methods like Naïve bayes (NB), K- Nearest Neighbour (KNN), K- means and Random Forest (RF).
1. Introduction
The PA is the new technique for enhancing the yields of the crop and supporting agriculture choices with cutting-edge sensor and analysis tools. An innovative idea called PA has been widely adopted in order to expand emission, shorten labour hours, and ensuring effective improved irrigation control. It makes extensive use of data and data on improving agricultural resource utilisation while in- creasing crop production and quality (Muangprathub et al., 2019;Filippi et al., 2019). The goal of PA is to increase resource produc- tivity in agricultural fields through new advancements and optimised field-level management. In order to increase productivity, qual- ity, and yield, farmers can use PA, a newly developed technique that can provide optimised inputs like water and fertiliser. It necessi- tates the collection of sizable amounts of high-resolution geographical data on the health or condition of crops during the growing season (Bodkhe et al., 2020;Jung et al., 2021). Irrespective of the source of data, PA's primary objective is to help farmers manage their farms. Although there is lot of different forms of this support, the capabilities are often reduced as a result. Monitoring crop sta- tus through observations and measurements of factors like soil quality, plant health, the effectiveness of fertilisers and pesticides, irri- gation, and crop yield is essential to modern agricultural production. For crop producers, controlling all of these factors is a signifi- cant challenge (Mekonnen et al., 2019;Tseng et al., 2019;Tseng et al., 2019,2019;Ayaz et al., 2019).
* Corresponding author.
E-mail address:[email protected](D. Suganya).
https://doi.org/10.1016/j.rsase.2023.100984
Received 27 December 2022; Received in revised form 12 April 2023; Accepted 30 April 2023
In order to manage crop yield and make wise use of farming resources, it is crucial to quickly improve the accuracy of agriculture productivity monitoring and health evaluation. For the many cycles of crop growth, reliable biophysical indication maps must be pro- duced, remote sensing (RS) systems like hyperspectral imaging can be used to address these challenges (Higgins et al., 2019;War et al., 2020;Zahid et al., 2020). The use of RS in various agricultural applications is a rapidly growing technology. Imaging spectroscopy in broad sustained narrow bands is very helpful for understanding the biophysical and metabolic features of agricultural crops (Loures et al., 2020;Singh et al., 2020). Observing alterations in many physical phenomena is also beneficial, that is more easily seen with multispectral RS. For extensive crop inventories and yield forecasts, advanced RS techniques have been applied.
Applications of RS are utilized in agricultural research that focuses on how soil or plant matter interacts with the surface of the Earth with electromagnetic radiation. In PA (Yang, 2020;Toscano et al., 2019;Radoglou-Grammatikis et al., 2020), RS is frequently used in conjunction with GIS and/or GPS systems. In contrast to using conventional field methods, this makes it possible for farmers and other agricultural producers to optimize cost savings while minimising input expenditures.
The foremost contribution of the paper is as follows.
➢ The UAV principles are used for monitoring the crop field. The dataset contains the multispectral picture, which is used as the input image.
➢ In precision agriculture, the preprocessing is performed for removing the noise content present in the input image.
➢ Feature section is done through PCA and the kernel modified SVM is used for classification. In our work, the paddy and wheat crops are used for comparison.
➢ The comparison is performed with and without optimization, and also compared with the existing techniques like NB, KNN, K- means, RF. The implementation is performed using PYTHON platform.
The rest of the paper is organized as shown below. Related work is provided in Section II. Sections II, describe the data used in the paper and explain the proposed methodology. Section IV describes the results and discussions. Section V concludes by outlining con- clusions and future work.
2. Literature review
“Some of the recent research works related to Churn Prediction were reviewed in this section”
In 2019, Hamada and Kanat et al. (Hamada et al., 2019) have introduced the Traditional multispectral systems that are carried in aerial or satellite systems, have poor temporal resolution, and can only measure static targets. In this study, we present a unique mul- tispectral system that employs a camera array with multiple filters to constantly and dynamically collect 12-bit images with 960540 resolutions in 35 bands.
In 2019, Singhal and Bansod et al. (Singhal et al., 2019) have presented destruction and laborious way of estimating bio-physical parameter. To investigate the application of high-resolution UAV imagery in estimating leaf chlorophyll concentration for crop health monitoring, and (2) to assess and compare the effectiveness of different machine learning regression algorithms in estimating leaf chlorophyll concentration.
In 2021, Tumelienė and Visockienė et al. (Tumelienė et al., 2021) have proposed to investigate the impact of seasonality on image segmentation for the identification of abandoned land regions. In this study, multi-spectral Sentinel-2 photos from three separate time periods (April, July, and September) were employed, as well as three supervised image segmentation algorithms (Spectral Angle Map- ping (SAM), Maximum Likelihood (ML), and Minimum distance (MD)) with the same parameters.
In 2021, Fawakherji and Potena et al. (Fawakherji et al., 2021) have proposed an alternate solution to conventional data augmen- tation methods and apply it to the fundamental problem of crop/weed segmentation in precision farming. Beginning with real photos, we produce semi-artificial samples by substituting the most relevant object classes (i.e., crop and weeds) with synthesised ones.
In 2019, Zhang and Su et al. (Zhang et al., 2019) have proposed the AquaCrop model at field scales using advanced Bayesian infer- ence algorithms and UAV multi-spectral photos. Aerial photos with high spatial and temporal resolutions are used to generate Canopy Cover (CC) values using machine learning-based classification. The CC is then incorporated into the AquaCrop model, and Markov Chain Monte Carlo is used to infer unknown parameters (MCMC).
In 2019, Xie and Zhang et al. (Xie et al., 2019) have used Deep CNN to categorise high spatial resolution photos gathered over smallholder agricultural landscapes, and CNN will be compared to the standard random forest classifier. End-to-end CNN training may effectively extract spatial information useful for discriminating spectrally identical classes, which is common in smallholder agri- culture sceneries, particularly the four-band multispectral image.
In 2020, Nguyen and Hoang et al. (Nguyen et al., 2020) have proposed to create a system capable of assessing chemical parame- ters of agricultural soils using satellite photos and machine learning algorithms. Sentinel-2 is employed to acquire images for this pur- pose, and Linear Regression, SVR, Bayesian Ridge, Lasso, PLSR, and Random Forest algorithms are implemented.
In 2019, Bendini and Fonseca et al. (do Nascimento Bendini et al., 2019) have used the application of U-Net-based deep learning over an area of Punjab, India; (b) detection of important agricultural land use types, including wheat, berseem, mustard, other vegeta- tion, water, and buildup; and (c) validation and comparative analysis using an RF classifier. This location was chosen for its agricul- turally intense zone, where vegetative areas have been separated into tiny holdings.
In 2020, Gudmann and Csikós et al. (Gudmann et al., 2020;Mazzia et al., 2020) have proposed the impact of landscape metrics that have not yet been employed in image classification in order to give additional information for land cover mapping in order to in- crease the thematic accuracy of satellite image-based land cover mapping. To that goal, Landsat 8 Operational Land Imager (OLI) and Sentinel-2 Multispectral Instrument (MSI) multispectral satellite photos from three different seasons in 2017 were analysed.
In 2013, Löw and Michel et al. (Löw et al., 2013) have proposed the effect of feature space size on SVM performance is investi- gated in the context of field-based crop classification using multidate optical satellite data. The effect of feature selection on classifica- tion accuracy and uncertainty is specifically assessed.
Image Segmentation Using Multiple Species utilising K-means clustering. The used machine learning algorithm may operate auto- matically. The size and accuracy of the spectral data model determine the correctness of the results of classifying weeds and crops.
Segmentation, classification, and selection of the most useful remote sensing data for agriculture. Field segmentation works best with the channels R-band (SWIR I + SWIR II), G-band (Narrow NIR - Green), and B-band (SWIR I - Green).
Machine learning-based Chlorophyll Assessment Using Multi-Spectral Unmanned Aircraft System. The article discussed the ap- plicability of multispectral sensors onboard unmanned aerial vehicles combined with a machine learning approach to evaluate the chlorophyll contents as a replacement for destruction and time-consuming methods of calculating biophysical parameters. The exper- imental findings demonstrated the value of using machine learning techniques as a reliable and improved method of retrieving chlorophyll.
Seasonality's Effect on Multi-Spectral Image Segmentation for Identifying Abandoned Land In order to identify areas of abandoned land, the research aims to analyse how seasonality affects image segmentation. This study demonstrated that the results of image seg- mentation were influenced by seasonality and the segmentation algorithm. The outcome revealed that the SAM algorithm's segmenta- tion of autumnal images was the most accurate.
Exactness farming uses spread-spectrum image fusion for crop/hemp segmentation. The strategy described in this paper uses using a cGAN to synthesise just the most important items for segmentation, it can create whole agricultural landscapes. The central idea be- hind the suggested method is to prepare techniques to take into account the forms of actual things. The second evaluation aims to show how various cutting-edge segmentation architectures can perform better when using cGAN augmented datasets.
Using UAV Multi-Spectral Images, the AquaCrop Model is used to calibrate the winter wheat crop. This research focuses on cali- brating the AquaCrop model using field-scale UAV multi-spectral images and cutting-edge Bayesian 11 inference algorithms. In specifically, the Canopy Cover (CC) price is calculated through machine learning-based categorization using aerial pictures with large spatial and temporal qualities as the starting point. While the development of the algorithm and a tiny field was used for its first con- firmation are the main focuses of this work, algorithm 335 validation using large fields will be more convincing.
Using High Spatial Resolution Satellite Images, a Deep Convolutional Neural Network (deep CNN) for agriculturist cultivation Mapping was developed. Deep CNN will be used in this project to categorise high spatial resolution photographs of smallholder agri- cultural landscapes, and it will be compared to the well-known random forest classifier. GaoFen-1 images that have been unsharp- ened and have a firmness of 2 m were used in this study to examine four smallholder agricultural areas in Heilongjiang, China. In or- der to interpret the reference land cover maps, in training and testing, systematic samples of 2 m GaoFen-1 pictures are taken.
Deep learning and satellite imagery for the monitoring of agricultural areas. The study's objective is to create an autonomous with intelligent system that can distinguish between crop areas and non-crop areas using symbolism datasets from low-Earth space probes are accessible. Within that paper, discussion about a unique spatiotemporal-spectral deep neural network-based multi-terrestrial high-dimensional diagnosis classification approach to position rice fields at the pixel level over the course of a year and for each tem- poral instance. Because of the excellent size of pixels of Landsat 8 data, this method was developed and tested on it.
According to phenology intelligence upon densely packed satellite picture chronological data, a detailed classification of farming ground within Brazilian cerrado. With help of this method, we were able to produce a phenometrics for a Random Forest (RF) cate- gory are derived from a rich Enhanced Vegetation Index (EVI) data cube with an 8-day decideness. From classes of crop rotation to land use, we used a supervised learning method with four levels. Over 90% accuracy was displayed in the majority of the classes. Low- accuracy crop classes included single-crop and non-commercial crops. Nevertheless, we demonstrated the great potential for detailed agricultural mapping that phenometrics obtained using a hierarchical prediction model and large Landsat-like picture grid.
Enhancement of property Cover Classification via Satellite Images with Landscape Metrics. That whole study will look at how landscape metrics that haven't been used for picture categorization might enhance the topical correctness of space rocket-based land screen function. In order to achieve this, three different seasons of 2017's multispectral satellite images from the Sentinel-2 Multispec- tral Instrument (MSI) and Landsat 8 Operational Land Imager (OLI) were examined. quartet mend -level scenery measures were gen- erated from the segmented images: mean patch size, overall border, mean form index, and fourier transform.
Improvement of a pa satellite-driven vegetation indices using UAV and Machine Learning This study presents a novel framework for improving satellite imagery that is based on deep learning and takes advantage of data correctly derived from high-resolution pic- tures taken using multispectral aerial sensors on UAV. Correlation analysis and ANOVA were used to demonstrate that four distinct throughout the creeper increasing time of year were used to collect precise spacecraft -driven Normalised Difference Vegetation Index (NDVI) maps, provide a more accurate description of crop status than raw datasets.
3. Proposed methodology
Alternatives to satellite and airborne that are more flexible and affordable include UAV platforms. A typical UAV platform consists of an integrated communication and navigation system with a number of sensors mounted on it. The majority of UAV platforms are fixed-wing platforms. Options for multirotor aircraft are available. The weight of the payload determines the flying time. Fixed-wing systems, in general, are able to fly for longer distances, necessitating lighter payloads. For instance, a fixed-wing UAV with a payload of high-definition cameras can fly for about 2 h on currently available battery power with a payload of fewer than 300 g. On the other hand, battery-powered multirotor UAVs with greater payload capacities have a shorter flight time—roughly 15–25 min. UAV plat- forms are frequently used in the agricultural sector, specifically to remotely check on the health of crops. Due to its low price and sim-
plicity of use, DJI/Phantom-2 is a better option among these platforms for intermediate agricultural land. This platform also has the benefit of supporting the mounting of multiple cameras, allowing for the monitoring of the crop across a range of electromagnetic bands. The practical implementation of the UAV based multi-spectral image classification is shown inFig. 1.
3.1. Spatial resolution of the satellite data used
Crop classification using satellite data typically requires high-resolution imagery to accurately identify and distinguish different types of crops. The spatial resolution needed for crop classification can vary depending on the size and distribution of the crops being studied.For example, studies that focus on identifying individual plants or small plots of land may require very high-resolution im- agery, while studies that focus on large-scale crop distribution may use coarser resolution imagery. Some high-resolution commercial satellites such as WorldView-4 can capture images with a spatial resolution of 31 cm, which may be sufficient for crop classification in some cases. However, other studies may use even higher resolution imagery or a combination of different resolution imagery to im- prove the accuracy of their crop classification models. Modified SVMs are commonly used for crop classification as they can handle high-dimensional data and can effectively separate different types of crops based on their spectral signatures. However, the choice of spatial resolution for satellite data will depend on various factors such as the specific research question, the size and distribution of the crops, and the availability and cost of the data.
3.2. Dataset description
A total of 31500 detached perception images, split into 45 setting groups, make up the planned NWPU-RESISC45 data set. Red, green, and blue (RGB) colour space images are 700 in number and each class has 256 256-pixel dimensions. The spatial resolution for the majority of scene classes spans from around 30 to 0.2 m per element, with the exception of the lower spatial resolution classes of the island, lake, mountain, and snowberg. This set of information was also eradicated from Google Earth (Google Inc.), a 3-D globe- mapping application that uses photos from satellite imaging, aeriform shot, and geographic information systems (GIS) to map the Earth, in the same manner as the majority of the existing data sets. The 31500 images include developing, transitional, and highly de- veloped economies and span more than 100 nations and regions worldwide. In comparison to all current scene classification data sets, the data set stands out for the three reasons listed below.
1) Large Scale: The most recent developments in computer vision and machine learning have undergone a revolutionary change as a result of deep learning's resurgence. The availability of expansive data sets, this allows deep networks to function at their best, is a key element in its success. Sadly, almost all of the data sets currently available are of lesser extent. For fix this issue, we provide a huge benchmark data set of 31500 form and 45 scene categories with 700 figures each. Our data set's overall image count is 15 times greater than that of the largest collection of UC Merced data. Our dataset, which contains the most scene types and images generally, is the largest scale we are aware of. The development and testing of various data-driven algorithms will be made possible by the creation of this new dataset, which will advance the new-fashioned.
2) Rich variety in images: Any mechanism for categorising scenes, whether human or automated, should be tolerant of image variations. However, the majority of the datasets currently in use don't have a lot of different image types. Contrarily, our photos were carefully chosen in a variety of weathers, seasons, lighting setups, imaging setups, and scales. As a result, our dataset has a wide range of rich variations for each scene category, including translation, viewpoint, object pose and visual appeal, spatial resolution, brightness, origin, and compression, among other things.
3) High levels of both class-to-class and class-to-class similarity: Many high-performing algorithms based on deep neural networks have gone mainstream in denominations correctness on most extant datasets owing to its simplicity rather, lack of variance and diversity. In light of this, our new dataset presents some difficulties due to its high levels of inside class difference and between- class resemblance. In order to achieve that, gathered photographs from various environments and included additional powdered scene classifications with substantial linguistic overlap, similar elliptical and rectilinear farms, business and industrial areas, basketball and tennis courts, etc.
Fig. 1.UAV based practical implementation diagram.
The preceding paragraph outlines the procedures for collecting and preparing satellite images before analysis. First, a series of satellite images from various locations over the course of a year are downloaded. The images with heavy cloud coverage are also taken out of the dataset because they are useless for evaluation. Then, the further pictures (between 24 and 37, depending on the re- gion) for each region are separately aggregated by taking the median value of each pixel along a temporal axis. The result is a set of photos with the yearly average characteristics of the index for every pixel, one for each selected area. The flow chart of the proposed technique is shown inFig. 2.
3.3. Pre-processing
The information is set up as geospatial data in the Cropland Data Layer (CDL) format. Utilising various farming ground truth and medium resolution satellite pictures, the CDL is a pixel, geo-coding, crop-precise land cover evidence overlay produced each year for the mainland. Due to the fact that the quality of CDL data varies depending on the crop and the state, only plants with high accuracy values are chosen as training and assessment data. Alternatively, the assessment would be conducted using incorrect data, making the results useless. The graphical illustration of pre-processing is shown inFig. 3.
For the same chosen areas and the given year, CDL provides ground truth data. However, only regions with high accuracy crops are analysed because, as was mentioned in the previous section, the kind of crop and location affect the CDL data's accuracy. The PA has made extensive use of remote sensing to keep track of the health of its crops. By calculating the radiation that is emitted and re- flected from a distance, the phenomena of distant sensing enable us to see the physical conditions of the Earth from a distance. To cap- ture images for further analysis to discover the characteristics of a particular area, specialized cameras are used. These cameras that take pictures of the objects are mounted on a variety of platforms.
3.4. Feature selection
Data dimensions can be decreased by using the powerful transform method known as principal component analysis (PCA). As a re- sult, choosing the optimal variables is suitable. According to mathematics, the PCA is an extraneous elongate convert that fills a new reference frame with data with the biggest variance on the first axis, then will variability on the transverse axis, and so on. The parts of a dataset that have the biggest impact on variance are kept for this reason. The application of the PCA is dependent on suppositions like:
✓ The dataset is taken to be a linear combination of particular fundamentals.
✓ Based on probabilities, the mean and co-modified are trustworthy.
✓ Variation is the primary indicator of details.
Fig. 2.Block diagram of the proposed methodology.
Fig. 3.Satellite image preparation for a single index.
The PCA is defined as follows with regard to a data matrix with the formaT, when all cue contains statistics for an intimation with an empirical mean of zero, and each row represents a group of observation:
BT=ATV (1)
W∑VTIn this equation represents the breakdown of individualaTvalues. Transferring dataset, A of dimension N to dataset B of di- mension M is the aim of PCA, in accordance with its definition. Therefore, it is assumed that each of the vectors in the assumed ma- trix A,A1,A2,….,ANis arranged into a column. The table of data has a measurement of N × M, where N is the vectors' dimension. The resulting vector from the empirical mean is shown below:
S[N] = 1 M
M
∑
j=1
A[ N.j]
(2) The matrix rows were the only ones to which the empirical mean was applied. Afterwards, the distance-to-mean matrix was discov- ered:
Y=A−Sg (3)
The empirical mean was solely applied to the rows of the matrix. Then it was found the distance-to-mean matrix,gis a 1 × N vector with a value of 1 for each element in this matrix. It was possible to get theN×Ncovariance matrixCm:
w−1Cmw=Dm (4)
In the diagonal matrixDm, the diagonal elements are eigenvalues, andwis the eigenvector. Therefore, each eigenvalue has a corre- sponding eigenvector. Or to put it another way,wis a N ×Nmatrix with eigenvectors as the columns. On the qth column, the eigen- vectorwpis present, and theptheigenvalue (λp=Dp.P) is related to it. The magnitudes of their corresponding eigenvalues are used to arrange them. To put it another way, rearranging the eigenvectors to match the eigenvalues in decreasing order:
q≤p→𝜆
q≤𝜆
p (5)
Using eigenvalue analysis, a subset of eigenvectors is chosen. Considering the rearranging the previous step,w1,w1,….,wNis chosen as the final subset. Here, one could choose by using the total energy:
h[n] =
n
∑
p=1
𝜆p (6)
Even though g is supposed to be a valid value, Selection 1 ought to have the lowest value possible. One possible option is to choose the minimum value of 1:
h[n]≤S∗ (7)
Therefore:
V[ q.p]
=W[ q.p]
(8) where q=1,….N and p=1,…..,N
3.5. Classification using kernel-modified SVM
The SVM device is the heart of our framework. An earlier study integrated SVM into the system. We tried to set up an SVM model based on a data-dependent kernel function in order to enhance the accuracy of SVM prediction. The beginning and modification of SVM are covered below.
SVM fundamentals, which are required to explain our procedure, will be briefly introduced in this section. Training and testing data for classification tasks typically include a few data samples. Several features and one class label are present in each sample in the training set. Using only the features from the testing set, SVM attempts to predict the class labels. Provided a collection of test pairs used as instruction, the SVM needs the following optimization problem (original problem) to be solved, in (ai,bi)whereai∈Rmandbi∈ (−1,1)
min
𝜔∗,b∗,𝜉∗
1
2𝜔T∗𝜔∗+C∗
N
∑
i=1
𝜉∗i (9)
Were bi
(𝜔T∗𝜑∗( ai
)+y)
≥1−𝜉∗i And𝜉∗i ≥0
A kernel function called the inner product is then used to map the training vectors into a greater (possibly infinite) region with bound- aries in accordance with the aforementioned equation. The linear splitting hyper-plane with the largest margin is found using SVM as it is transferred into the higher-dimensional environment. The error term's penalty parameter is C, and the separating hyper-offset plane's is b. The SVM solves the original problem's dual problem in computer programming in the following ways:
min∝ ∑
i
∑
j
bibj∝i∝jn( ai,aj
)
−∑
j
∝i (10)
Where∑N
i=1
bi∝i=0and 0≤ ∝i≤C*, i = 1, 2, N The kernel function is defined as n (ai,aj)
. As a logical initial option, we choose the Radial Basis Function (RBF) as the SVM kernel function.
N ai,aj
= exp
−𝛿
ai−bj
2
, 𝛿 >0 (11)
3.6. Use training data to change the kernel function
The selected kernel function is said to have a considerable effect on the accomplishment of an SVM model. Inducing a Riemannian metric occurs whenever a kernel function converts input data into a larger dimension (possibly infinite):
fi,j(a) = 𝜕
𝜕ai
𝜕
𝜕aj
n( a,a′)
(12) Increasing the margin of classes will help the SVM model. The boundary surface's spatial resolution must be increased in feature space. As a result, we increase the metricfi,j(a) near the boundary and decrease it elsewhere. In actuality, we are unable to obtain the boundary information. As a result, we make use of the empirical finding that Support Vectors (SVs) are most frequently found near boundaries. Such SVs can be acquired from the training phase that came before. For the change to function, the new kernel function has to have big values at the SV points. As a consequence, we produce a conformal convert of the kernel operate, where:
n′(a,a′)
=F(a)F(a′)n(a,a′)
(13) F(a) =∑
i∈SV
e−‖a−ai‖2∕𝜏2
i (14)
𝜏2
i = 1
N
∑
j
‖‖
‖aj−ai‖
‖‖
2 (15)
ajStands for M SVs nearest toai.𝜏2iis the mean squared distance fromaito its N nearest SV neighbors. The distance from SVs causes the functionF(a) to decrease exponentially. We can picture a collection of circles with their centres at each SV covering the boundary.
F(a) and its derivative have very small values outside of the circles.F(a) therefore, meets the requirement we set. The following is the modification step:
1. Using RBF to train the SVM, obtain SV information, and modify the kernel functionn′(a,a′).
2. Using the modified kernel functionn(a,a′) to train SVM.
3. Use the first two steps repeatedly to improve your outcome.
4. Result and discussion
This section shows result and discussion of proposed methodology, carried out Precision Agriculture system. The entertainment of the proposed computation is analysed and compared with the existing algorithm. The matrix performance such as accuracy, precision and Recall. NB algorithm, KNN algorithm, K-means and RF algorithm is used for analyse, and compared with the proposed algorithm and also in agriculture farm the identification of corps namely wheat and paddy are noted. The performance metrics are analysed be- low.
4.1. Performance metrics
This section explains the performance metrics for the suggested model, some of which have already undergone validation and test- ing. The following mathematical expression represents the analysis of the performance metrics found in the anticipated and current analyses:
i. Accuracy-Accuracy is the percentage of real outcomes in a community, whether real positive or real negative. It evaluates a diagnostic test's precision for a certain ailment. The sensitivity ratings depict how likely it is for a diagnostic test to find those who actually have the ailment.
Accuracy=Tp+TN∗TP+TN+FP+FN (16)
ii. Precision -Precision is calculated by dividing the total number of true positives by the total number of true positives + false positives.
precision= TP
TP+FP (17)
iii. Recall -Recall is calculated by dividing the total number of true positives by the total number of true positives + false negatives.
Recall= Tp
TP+FN (18)
4.2. Performance analysis
This section describes the detailed performance analysis and the comparison of existing and proposed method.
4.2.1. Analysis of performance metrics for proposed and existing approaches
Evaluation criteria for the proposed and present techniques are detailed in the table below. The proposed and current performance measures for accuracy, precision and Recall. The crops such as wheat and paddy are predicted and the performance is compared based on with and without optimization and the values are tabulated and plotted in graph.Table 1shows the performance of pro- posed model, with and without optimization based on performance matric andTable 2andTable 3depicts a performance and com- parison of the proposed and existing methodologies based on wheat and paddy identification.
Table 1 presents the performance metrics for a proposed crop classification model with and without optimization for two crops: paddy and wheat. The proposed model, which includes optimization, achieved an accuracy of 98.56% and 97.45% for paddy and wheat classification, respectively. On the other hand, the model without optimization achieved an accuracy of 95.44%
and 96.66% for paddy and wheat, respectively. The results indicate that the proposed model with optimization outperformed the model without optimization for both crops. The increase in accuracy can be attributed to the optimization technique used, which helps improve the model's ability to distinguish between different crops based on their spectral signatures. Overall, the proposed
Table 1
Performance matric based on with and without optimization.
Algorithm Accuracy
crops
Paddy Wheat
With optimization (Proposed model) 98.56 97.45
Without optimization 95.44 96.66
Table 2
Performance comparison of purposed and existing methods based on wheat identification.
Wheat crop identification
Algorithm Performance matrices
Precision Recall
NB 88.90 86.99
KNN 89.67 90.98
K-means 95.65 94.67
RF 96.89 97.01
Proposed model 98.09 98.55
Table 3
Performance comparison of purposed and existing methods based on paddy identification.
Paddy crop identification
Algorithm Performance matrices
Precision Recall
NB 86.90 87.99
KNN 88.67 92.98
K-means 94.85 96.67
RF 97.89 98.71
Proposed model 99.09 99.05
model with optimization demonstrated high accuracy in crop classification for both paddy and wheat, indicating its potential for use in precision agriculture and other related applications.
Table 2provides a performance comparison of various crop identification algorithms, including a proposed model, for wheat crop identification. The algorithms are evaluated based on two performance metrics: precision and recall. The Naive Bayes (NB) al- gorithm achieved a precision of 88.90% and a recall of 86.99%. The K-Nearest Neighbors (KNN) algorithm achieved a precision of 89.67% and a recall of 90.98%. The K-means algorithm achieved a precision of 95.65% and a recall of 94.67%. The Random Forest (RF) algorithm achieved a precision of 96.89% and a recall of 97.01%. The proposed model achieved the highest precision and re- call, with values of 98.09% and 98.55%, respectively. The high precision and recall values indicate that the proposed model has a high accuracy in identifying wheat crops based on their spectral signatures. Overall, the results indicate that the proposed model outperforms the other algorithms for wheat crop identification. The higher precision and recall values suggest that the proposed model can provide more accurate and reliable information for precision agriculture and related applications.
Table 3presents a performance comparison of various crop identification algorithms, including a proposed model, for paddy crop identification. The algorithms are evaluated based on two performance metrics: precision and recall. The Naive Bayes (NB) al- gorithm achieved a precision of 86.90% and a recall of 87.99%. The K-Nearest Neighbors (KNN) algorithm achieved a precision of 88.67% and a recall of 92.98%. The K-means algorithm achieved a precision of 94.85% and a recall of 96.67%. The Random Forest (RF) algorithm achieved a precision of 97.89% and a recall of 98.71%. The proposed model achieved the highest precision and re- call values, with values of 99.09% and 99.05%, respectively. These high precision and recall values indicate that the proposed model has a high accuracy in identifying paddy crops based on their spectral signatures. Overall, the results suggest that the pro- posed model outperforms the other algorithms for paddy crop identification. The higher precision and recall values demonstrate that the proposed model can provide more accurate and reliable information for precision agriculture and related applications.
Table 1shows the performance evaluation of proposed model and analysed data is obtained based on with and without opti- mization. The performance data analysed is carried in two crops, paddy and wheat and accuracy is detailed and the performance comparison of proposed and existing model through the pa5rameter metrics precision and recall for each crop, i.e., paddy and wheat are shown inTables 3 and 4.
4.3. Accuracy
This section shows the accuracy evaluated through the proposed model, with and without optimization of each crop, i.e., Wheat and paddy and data analysed and shown inFig. 4.
Fig. 4explains that the performance of model with optimization, when identifying crop and paddy crop is higher than model with- out optimization technique.
Table 4
Comparision with other findings.
References Accuracy Precision Recall
Proposed 98.56 99.09 99.05
R1 (Adugna et al., 2022) – 1.00 0.00
R1 (Adugna et al., 2022) – 0.78 0.83
R1 (Adugna et al., 2022) – 0.66 0.84
R2 (Gapper et al., 2019) 87.9% 0.837 1.000
R2 (Gapper et al., 2019) 69.2% 0.706 0.800
Fig. 4.Performance evaluated based on with and without optimization.
Fig. 5.Performance Comparison among planned and current model.
Fig. 6.Performance Comparison among planned and current model.
4.4. Precision and recall
The evaluated performance of existing and proposed model is compared and the data analysed is dine through recall and precision matrices for each wheat and paddy crop is detailed below.
The accuracy, precision, and recall of a proposed model were compared with those reported in four other studies. The proposed model achieved an accuracy of 98.56%, precision of 99.09%, and recall of 99.05%. In comparison, study R1 reported a precision of 1.00 but a recall of 0.00, another precision of 0.78 and recall of 0.83, and a third precision of 0.66 and recall of 0.84. Study R2 achieved an accuracy of 87.9%, precision of 0.837, and recall of 1.000, and a lower accuracy of 69.2%, precision of 0.706, and recall of 0.800. These results suggest that the proposed model outperforms the models reported in the other studies in terms of accuracy, precision, and recall.
(a) Wheat crop
In this section the identification of wheat crop in an agriculture field is done by a proposed model and the performance of the planned model is shown by comparing the existing and planned methods and evaluated data is shown inFig. 5.
(b) Paddy crop
The identification of paddy crop in an agricultural field is carried out by a proposed model in this section, and the effectiveness of the planned model is demonstrated by contrasting the current and planned approaches, with evaluated data displayed inFig. 6.
5. Conclusion
In conclusion, the proposed model for crop classification based on multi-spectral and multi-temporal satellite data, using a UAV platform, PCA feature selection, and kernel modified SVM classification, demonstrated high accuracy in identifying paddy and wheat crops. The model with optimization outperformed the model without optimization, and also outperformed existing algorithms such as Naive Bayes, K-Nearest Neighbors, K-means, and Random Forest for crop identification. The high precision and recall values indicate that the proposed model can provide more accurate and reliable information for precision agriculture and related applications. There- fore, this model can be a promising tool for farmers and decision-makers in the agricultural sector to optimize crop management and enhance crop productivity. Further research can explore the potential of this model for other crop identification and land use classifi- cation applications.
Ethical statement
No ethics statement is required.
CRediT authorship contribution statement
Ramalingam Sugumar:Conceptualization, Investigation, Methodology, Project administration, Resources, Supervision, Vali- dation, Visualization.D. Suganya:Conceptualization, Data curation, Formal analysis, Funding acquisition, Investigation, Method- ology, Resources, Software, Visualization, Writing –original draft, Writing –review & editing.
Declaration of competing interest
The authors declare that we have no conflict of interest.
Data availability
The authors do not have permission to share data.
References
Adugna, T., Xu, W., Fan, J., 2022. Comparison of random forest and support vector machine classifiers for regional land cover mapping using coarse resolution FY-3C images. Rem. Sens. 14 (3), 574.
Ayaz, M., Ammad-Uddin, M., Sharif, Z., Mansour, A., Aggoune, E.H.M., 2019. Internet-of-Things (IoT)-based smart agriculture: toward making the fields talk. IEEE Access 7, 129551–129583.
Bodkhe, U., Tanwar, S., Bhattacharya, P., Kumar, N., 2020. Blockchain for Precision Irrigation: Opportunities and Challenges. Transactions on Emerging Telecommunications Technologies, p. e4059.
do Nascimento Bendini, H., Fonseca, L.M.G., Schwieder, M., Körting, T.S., Rufin, P., Sanches, I.D.A., Leitao, P.J., Hostert, P., 2019. Detailed agricultural land classification in the Brazilian cerrado based on phenological information from dense satellite image time series. Int. J. Appl. Earth Obs. Geoinf. 82, 101872.
Fawakherji, M., Potena, C., Pretto, A., Bloisi, D.D., Nardi, D., 2021. Multi-spectral image synthesis for crop/weed segmentation in precision farming. Robot. Autonom.
Syst. 146, 103861.
Filippi, P., Jones, E.J., Wimalathunge, N.S., Somarathna, P.D., Pozza, L.E., Ugbaje, S.U., Jephcott, T.G., Paterson, S.E., Whelan, B.M., Bishop, T.F., 2019. An approach to forecast grain crop yield using multi-layered, multi-farm data sets and machine learning. Precis. Agric. 20 (5), 1015–1029.
Gapper, J.J., El-Askary, H., Linstead, E., Piechota, T., 2019. Coral Reef change Detection in Remote Pacific islands using support vector machine classifiers. Rem. Sens.
11 (13), 1525.
Gudmann, A., Csikós, N., Szilassi, P., Mucsi, L., 2020. Improvement in satellite image-based land cover classification with landscape metrics. Rem. Sens. 12 (21), 3580.
Hamada, M.A., Kanat, Y., Abiche, A.E., 2019. Multi-spectral image segmentation based on the K-means clustering. Int. J. Innovative Technol. Explor. Eng. 9, 1016–1019.
Higgins, S., Schellberg, J., Bailey, J.S., 2019. Improving productivity and increasing the efficiency of soil nutrient management on grassland farms in the UK and Ireland using precision agriculture technology. Eur. J. Agron. 106, 67–74.
Jung, J., Maeda, M., Chang, A., Bhandari, M., Ashapure, A., Landivar-Bowles, J., 2021. The potential of remote sensing and artificial intelligence as tools to improve the resilience of agriculture production systems. Curr. Opin. Biotechnol. 70, 15–22.
Loures, L., Chamizo, A., Ferreira, P., Loures, A., Castanho, R., Panagopoulos, T., 2020. Assessing the effectiveness of precision agriculture management systems in mediterranean small farms. Sustainability 12 (9), 3765.
Löw, F., Michel, U., Dech, S., Conrad, C., 2013. Impact of feature selection on the accuracy and spatial uncertainty of per-field crop classification using support vector machines. ISPRS J. Photogrammetry Remote Sens. 85, 102–119.
Mazzia, V., Comba, L., Khaliq, A., Chiaberge, M., Gay, P., 2020. UAV and machine learning based refinement of a satellite-driven vegetation index for precision agriculture. Sensors 20 (9), 2530.
Mekonnen, Y., Namuduri, S., Burton, L., Sarwat, A., Bhansali, S., 2019. Machine learning techniques in wireless sensor network-based precision agriculture. J.
Electrochem. Soc. 167 (3), 037522.
Muangprathub, J., Boonnam, N., Kajornkasirat, S., Lekbangpong, N., Wanichsombat, A., Nillaor, P., 2019. IoT and agriculture data analysis for smart farm. Comput.
Electron. Agric. 156, 467–474.
Nguyen, T.T., Hoang, T.D., Pham, M.T., Vu, T.T., Nguyen, T.H., Huynh, Q.T., Jo, J., 2020. Monitoring agriculture areas with satellite images and deep learning. Appl.
Soft Comput. 95, 106565.
Radoglou-Grammatikis, P., Sarigiannidis, P., Lagkas, T., Moscholios, I., 2020. A compilation of UAV applications for precision agriculture. Comput. Network. 172, 107148.
Singh, P., Pandey, P.C., Petropoulos, G.P., Pavlides, A., Srivastava, P.K., Koutsias, N., Deng, K.A.K., Bao, Y., 2020. Hyperspectral remote sensing in precision agriculture:
present status, challenges, and future trends. In: Hyperspectral Remote Sensing. Elsevier, pp. 121–146.
Singhal, G., Bansod, B., Mathew, L., Goswami, J., Choudhury, B.U., Raju, P.L.N., 2019. Chlorophyll estimation using multi-spectral unmanned aerial system based on machine learning techniques. Remote Sens. Appl.: Soc. Environ. 15, 100235.
Toscano, P., Castrignanò, A., Di Gennaro, S.F., Vonella, A.V., Ventrella, D., Matese, A., 2019. A precision agriculture approach for durum wheat yield assessment using remote sensing data and yield mapping. Agronomy 9 (8), 437.
Tseng, F.H., Cho, H.H., Wu, H.T., 2019. Applying big data for intelligent agriculture-based crop selection analysis. IEEE Access 7, 116965–116974.
Tumelienė, E., Visockienė, J.S., Malienė, V., 2021. The influence of seasonality on the multi-spectral image segmentation for identification of abandoned land.
Sustainability 13 (12), 6941.
War, J.M., Fazili, M.A., Mushtaq, W., Wani, A.H., Bhat, M.Y., 2020. Role of nanotechnology in crop improvement. In: Nanobiotechnology in Agriculture. Springer, Cham, pp. 63–97.
Xie, B., Zhang, H.K., Xue, J., 2019. Deep convolutional neural network for mapping smallholder agriculture using high spatial resolution satellite image. Sensors 19 (10), 2398.
Yang, C., 2020. Remote sensing and precision agriculture technologies for crop disease detection and management with a practical application example. Engineering 6 (5), 528–532.
Zahid, A., Abbas, H.T., Ren, A., Alomainy, A., Imran, M.A., Abbasi, Q.H., 2020. Application of Terahertz Sensing at Nano-scale for Precision Agriculture. Wireless Automation as an Enabler for the Next Industrial Revolution. pp. 241–257.
Zhang, T., Su, J., Liu, C., Chen, W.H., 2019. Bayesian calibration of AquaCrop model for winter wheat by assimilating UAV multi-spectral images. Comput. Electron.
Agric. 167, 105052.