Remote Sensing Imagery Data Analysis Using Marine Predators Algorithm with Deep Learning for Food Crop Classification

Recently, the usage of remote sensing (RS) data attained from unmanned aerial vehicles (UAV) or satellite imagery has become increasingly popular for crop classification processes, namely soil classification, crop mapping, or yield prediction. Food crop classification using RS images (RSI) is a significant application of RS technology in agriculture. It involves the use of satellite or aerial imagery to identify and classify different types of food crops grown in a specific area. This information can be valuable for crop monitoring, yield estimation, and land management. Meeting the criteria for analyzing these data requires increasingly sophisticated methods and artificial intelligence (AI) technologies provide the necessary support. Due to the heterogeneity and fragmentation of crop planting, typical classification approaches have a lower classification performance. However, the DL technique can detect and categorize crop types effectively and has a stronger feature extraction capability. In this aspect, this study designed a new remote sensing imagery data analysis using the marine predators algorithm with deep learning for food crop classification (RSMPA-DLFCC) technique. The RSMPA-DLFCC technique mainly investigates the RS data and determines the variety of food crops. In the RSMPA-DLFCC technique, the SimAM-EfficientNet model is utilized for the feature extraction process. The MPA is applied for the optimal hyperparameter selection process in order to optimize the accuracy of SimAM-EfficientNet architecture. MPA, inspired by the foraging behaviors of marine predators, perceptively explores hyperparameter configurations to optimize the hyperparameters, thereby improving the classification accuracy and generalization capabilities. For crop type detection and classification, an extreme learning machine (ELM) model can be used. The simulation analysis of the RSMPA-DLFCC technique is performed on two benchmark datasets. The extensive analysis of the results portrayed the higher performance of the RSMPA-DLFCC approach over existing DL techniques.


Introduction
Recent developments in remote sensing (RS) data and technologies deliver the ability of highly accessible, cheap and real time advantages [1].In recent years, a massive quantity of global coverage RS images have been openly available [2].In particular, Landsat 8 satellite offers high-resolution multispectral datasets including wealthy data on agricultural of the Sentinel 2 time series dataset which was projected for crop identification.Fresh DAM was applied to the removal of enlightened deep features using the advantages of spatial and spectral features of Sentinel 2 datasets.Reedha et al. [12] targeted the design of attention-related DL networks in a significant technique to state the earlier mentioned complications regarding weeds and crop detection with drone systems.The objective is to inspect visual transformers (ViT) and implement them in the identification of plants in UAV images.In [13], the results of accurate recognition were tested to associate the phenology of vegetation products by time series of Landsat8, digital elevation model (DEM), and Sentinel 1. Next, based on the agricultural phenology of crops, radar Sentinel1 and optical Landsat8 time-series data with DEM were used to enhance the performance classification.
Sun et al. [14] proposed a technique for attaining deduction of fine-scale crops by combining RS information from different satellite images by construction of chronological scale crop features inside the parcels employing Sentinel 2A, Gaofen-6, and Landsat 8.The authors adopted a feature-equivalent technique to fill in the missing values in the time series feature-building methods to prevent problems with unidentified crops.Li et al. [15] introduced a scale sequence object-based CNN (SS-OCNN) that identifies images at the object phase by taking segmented object crop parcels as the primary unit of analysis, therefore providing the limits between crop parcels that were defined precisely.Next, the segmented object was identified utilizing the CNN approach combined with an automated generating scale structure of input patch sizes.Zhai et al. [16] examined the contribution of the data to rice planting area mapping.Specifically, the introduction of the red-edge band was to build a red-edge agricultural index derived from Sentinel 2 data.C band quad pol Radar sat 2 data was also utilized.The authors employed the random forest technique and finally collaborated with radar and optical data to plot rice-planted regions.In [17], the authors designed an enhanced crop planting structure to plot the structure for rainy and cloudy regions using collective optical data and SAR data.First, the author removed geo parcels from optical images with high dimensional resolution.Next, the authors made an RNN-based classification appropriate for remote detecting images on a geo parcel scale.

The Proposed Model
This manuscript offered the development of automated food crop classification using the RSMPA-DLFCC technique.The RSMPA-DLFCC technique mainly investigates the RS data and determines different types of food crops.In the RSMPA-DLFCC technique, three major phases of operations are involved, namely the SimAM-EfficientNet feature extractor, MPA-based hyperparameter tuning, and ELM classification.Figure 1 represents the entire process of the RSMPA-DLFCC approach.

Feature Extraction Using SimAM-EfficientNet Model
The RSMPA-DLFCC technique applies the SimAM-EfficientNet model to derive feature vectors.A novel CNN called EfficientNet was launched by Google researchers [18].The study uses a multi-dimensional hybrid method scaling model making them consider the speed and accuracy of the model even though the existing network has advanced considerably in speed and accuracy.Through compound scaling factors, ResNet raises the network depth to optimize the performance.By improving accuracy and ensuring speed, EfficientNet balances the network depth, width, and resolution.EfficientNet-B0 is the initial EfficientNet model.The most basic model B0 is: concerning resolution, layers, and channels, B1-B7 overall of 7 models adapted from B0.
Many existing attention modules generate 1D or 2D weights.Next, the weights created are extended for channel and spatial attention.Generally, the present attention module faces the two subsequent challenges.The former is the attention module could extract features through channel and space that results in the flexibility of attention weight.Moreover, CNN is influenced by a series of factors and has a complex structure.SimAM considers these spaces and channels in contrast to them.Without adding parameters, it presents 3D attention weights to the original network.Based on neuroscience theory, an energy function can be defined and, in turn, derive a solution that converges faster.This operation is executed in ten lines of code.An additional benefit of SimAM is that it prevents excessive adjustment to the network architecture.Hence, SimAM is lightweight, more flexible, and modular.In numerous instances, SimAM is better than the conventional CBAM and SE attention models.Figure 2 illustrates the architecture of SimAM-EfficientNet.

Feature Extraction Using SimAM-EfficientNet Model
The RSMPA-DLFCC technique applies the SimAM-EfficientNet model to derive feature vectors.A novel CNN called EfficientNet was launched by Google researchers [18].The study uses a multi-dimensional hybrid method scaling model making them consider the speed and accuracy of the model even though the existing network has advanced

Hyperparameter Tuning Using MPA
For the optimal hyperparameter selection process, the MPA is applied.The MPA is derived from the foraging tactics of the ocean predator [19].MPA is a population-based metaheuristic approach.The optimization technique begins with the arbitrary solution.
where  and  denotes the lower and upper boundaries, and  is a randomly generated integer in the range [0,1].In the MPA, Prey and Elite are two different matrices with similar dimensions.The optimum solution is selected as the fittest predator while creating the Elite matrix.
The finding of and search for prey is checked through these matrices. ⃗ indicates the dominant predator vector,  is the searching agent, and , the dimension.Both prey and predator are the search agents.The SimAM model defines an energy function and looks for important neurons.It adds regular terms and uses binary labels.At last, the minimal energy is evaluated by the following expression: where µ t and σ 2 t are the mean and variance of each neuron.t is the target neuron.λ indicates the regularization coefficient.Using M = H × W, the neuron count on that channel is attained.Finally, the dissimilarity between neurons and peripheral neurons is associated with the energy used.The implication of all the neurons is evaluated by 1/e * .The scaling operator is used to refine the feature and it can be formulated as follows: The sigmoid function is used to limit the size of the E value.In Equation ( 3), E group each e across the channel and spatial sizes.
EfficientNet-B0 has a total of nine phases.The initial phase is 3 × 3 convolutional layers.The second to the eighth phases are MBConv, which is the building block of these network models.The last phase is made up of a pooling layer, a 1 × 1 convolutional layer, and the FC layer.MBConv has five different parts.The initial part is a 1 × 1 convolutional layer.The next part is a depth-wise convolution layer.The third part is the SE attention mechanism.The fourth part is a 1 × 1 convolutional layer for reduction dimension.Lastly, the dropout layer lessens the over-fitting problem.After the first convolutional layer, the SimAM module was added to increase channel and spatial weights.The original EfficientNet comprises the SE attention mechanism.
The SimAM-EfficientNet is made up of seven SimAM-MBConv models, one FC layer, two convolution layers, and one pooling layer.At first, the images with 224 × 224 × 3 dimensions are ascended by the 3 × 3 convolution layers.The dimensions of the images obtained with features are 112 × 112 × 32.Next, the image features are extracted by the SimAM-Conv.The connection will be deactivated when both SimAM-Convs are the same, and the input will connect.The FC layer is utilized for classification and the original channel is restored after a 1 × 1 point-wise convolutional layer.

Hyperparameter Tuning Using MPA
For the optimal hyperparameter selection process, the MPA is applied.The MPA is derived from the foraging tactics of the ocean predator [19].MPA is a population-based metaheuristic approach.The optimization technique begins with the arbitrary solution.
where X min and X max denotes the lower and upper boundaries, and rand is a randomly generated integer in the range [0, 1].In the MPA, Prey and Elite are two different matrices with similar dimensions.The optimum solution is selected as the fittest predator while creating the Elite matrix.
The finding of and search for prey is checked through these matrices.
→ X I indicates the dominant predator vector, n is the searching agent, and d, the dimension.Both prey and predator are the search agents.
where the j th dimension of i th prey is represented as X i,j .The optimization method is connected to both matrices.Predator uses these matrices for updating the position.
In the MPA, there are three stages discussed in detail.Phase 1 occurs if < ((Max − Iter)/3).Iter and Max − Iter denote the existing and maximal iteration counter.P shows the constant number with the value of 0.5.The appropriate tactic is one where the predator should stop.In Equation ( 7) of stage 1, vector R B portrays the Brownian motion and uniformly distributed random number in [0,1].

−−−−→
Phase 2 realized if ((Max − Iter)/3) < Iter < ((2Max − Iter)/3.Once the prey movement is Lévy, then the predator movement should be Brownian.The prey is responsible for exploitation, and the predator is responsible for exploration.The multiplication of → R L and Prey represent the prey movement, and the prey movement can be exemplified by adding the stepsize to the prey position.The → R L vector is a random number representing Lévy motion.CF denotes an adaptive parameter.stepsize for the predator movement can be controlled by the CF.
The factors including fish aggregating devices (FADs) or eddy formation may affect the predator strategy are called the FADs effect.r is a randomly generated value within [0,1].
→ U shows the binary vector with an array of 0 and 1. r1 and r2 depict the random indexes of prey matrices.→ X min and → X max denote the lower and upper boundaries of the dimension.

− − → Prey
The fitness selection is a major factor in the MPA technique.An encoded solution is used for evaluating the outcome of the solution candidate.The accuracy values are the foremost conditions used to design an FF.
where TP and FP represent the true and false positive values.

Classification Using ELM Model
The ELM algorithm is applied for the automated detection and classification of food crops.The ELM model is used to generate the weight between the hidden and the input layers at random, and during the training process, it does not need to be adjusted and only needs to set the number of HL neurons in order to attain an optimum result [20].Assume N arbitrary sample (X, t), where X j = x j1 , x j2 . . .
The weight of i th neurons in the input layer and HL is W i = [w i1 , w i2 . . .w in ] T , chosen at random.The resultant weight is β i , and the learning objective is to obtain the fittest β i .The j th input vector is X j .The inner product of W i and X j is W i • X j .The bias of i th HL neuron is b i .The set non-linear activation function is g(x).The output vector of the i th neurons is g W i • X j + b i .The target vector attained from the j th input vector is t j .It can be represented in the matrix form: The output of the HL node is H, the output weight is β, and the desired output is T. The following equation is used to get Ŵi , βi , ˆbi as follows: As shown in Equation (17), this corresponds to minimalizing the loss function, Since the HL offset and the input weight W i are determined randomly, then the output matrix of HL is also defined.As shown in Equation (18), the training purpose is transmuted into resolving a linear formula Hβ = T: where the optimum output weight is β.The Moore-Penrose generalized the inverse of H matrix is H + , and it is shown that the norm of the obtained solution is unique and minimal.Thus, ELM has better robustness and generalization.

Results Analysis
The proposed model is simulated using the Python 3.8.5 tool.The proposed model is experimented on PC i5-8600k, GeForce 1050Ti 4 GB, 16 GB RAM, 250 GB SSD, and 1 TB HDD.The food crop classification performance of the RSMPA-DLFCC system is validated on the UAV image dataset [21], comprising 6450 samples with six classes.For experimental validation, we have used 80:20 and 70:30 of training (TR)/testing (TS) set.
Figure 3   In Table 1 and Figure 4, the food crop classification analysis of the RSMPA-DLFCC methodology is calculated at 80:20 of the TR phase/TS phase.The observational data specified that the RSMPA-DLFCC system properly categorizes seven types of crops.With 80% of the TR phase, the RSMPA-DLFCC technique offers an average  of 98.12%,  of 93.23%,  of 90.76%,  of 91.89%, and MCC of 90.77%.Additionally, with 20% of TS phase, the RSMPA-DLFCC method offers an average  of 98.22%,  of 93.06%,  of 90.42%,  of 91.57%, and MCC of 90.56%, respectively.In Table 1 and Figure 4, the food crop classification analysis of the RSMPA-DLFCC methodology is calculated at 80:20 of the TR phase/TS phase.The observational data specified that the RSMPA-DLFCC system properly categorizes seven types of crops.With 80% of the TR phase, the RSMPA-DLFCC technique offers an average accu y of 98.12%, prec n of 93.23%, reca l of 90.76%, F score of 91.89%, and MCC of 90.77%.Additionally, with 20% of TS phase, the RSMPA-DLFCC method offers an average accu y of 98.22%, prec n of 93.06%, reca l of 90.42%, F score of 91.57%, and MCC of 90.56%, respectively.In Table 2 and Figure 5, the food crop classification analysis of the RSMPA-DLFCC technique is calculated at 70:30 of TR Phase/TS Phase.The experimental values indicate that the RSMPA-DLFCC technique appropriately categorizes seven types of crops.With 70% of the TR phase, the RSMPA-DLFCC algorithm offers an average  of 97.98%,  of 91.79%,  of 88.64%,  of 90.02%, and MCC of 88.90%, respectively.In addition, with 30% of TS phase, the RSMPA-DLFCC system offers average  of 98.07%,  of 92.13%,  of 90.13%,  of 91.06%, and MCC of 89.92%, correspondingly.In Table 2 and Figure 5, the food crop classification analysis of the RSMPA-DLFCC technique is calculated at 70:30 of TR Phase/TS Phase.The experimental values indicate that the RSMPA-DLFCC technique appropriately categorizes seven types of crops.With 70% of the TR phase, the RSMPA-DLFCC algorithm offers an average accu y of 97.98%, prec n of 91.79%, reca l of 88.64%, F score of 90.02%, and MCC of 88.90%, respectively.In addition, with 30% of TS phase, the RSMPA-DLFCC system offers average accu y of 98.07%, prec n of 92.13%, reca l of 90.13%, F score of 91.06%, and MCC of 89.92%, correspondingly.To calculate the performance of the RSMPA-DLFCC methodology on 80:20 of TR Phase/TS Phase, TR and TS accu y curves are defined, as shown in Figure 6.The TR and TS accu y curves demonstrate the performance of the RSMPA-DLFCC technique over numerous epochs.The figure offers the details about the learning task and generalization capabilities of the RSMPA-DLFCC system.With a rise in epoch count, it is observed that the TR and TS accu y curves attained are enhanced.It is noted that the RSMPA-DLFCC approach enriches testing accuracy that has the ability to identify the patterns in the TR and TS data.
Biomimetics 2023, 8, x FOR PEER REVIEW 12 of 18 approach enriches testing accuracy that has the ability to identify the patterns in the TR and TS data.The PR curve of the RSMPA-DLFCC approach on 80:20 of TR phase/TS phase, illustrated by plotting precision against recall as described in Figure 8, confirms that the RSMPA-DLFCC technique achieves improved PR values under all classes.The figure represents that the model learns to identify different class labels.The RSMPA-DLFCC achieves improved effectiveness in the recognition of positive samples with reduced false positives.
The ROC analysis, provided by the RSMPA-DLFCC system on 80:20 of TR phase/TS phase demonstrated in Figure 9, has the ability the differentiate between class labels.The figure shows valuable insights into the trade-off between the TPR and FPR rates over dissimilar classification thresholds and differing numbers of epochs.It introduces the accurately predicted performance of the RSMPA-DLFCC methodology on the classification of various classes.
The ROC analysis, provided by the RSMPA-DLFCC system on 80:20 of TR phase/TS phase demonstrated in Figure 9, has the ability the differentiate between class labels.The figure shows valuable insights into the trade-off between the TPR and FPR rates over dissimilar classification thresholds and differing numbers of epochs.It introduces the accurately predicted performance of the RSMPA-DLFCC methodology on the classification of various classes.In Table 3, detailed comparative results of the RSMPA-DLFCC technique are demonstrated with current models [22,23].Figure 10 investigates a comparative analysis of the RSMPA-DLFCC with recent approaches in terms of  .The experimental values highlighted that the RSMPA-DLFCC technique reaches an increased  of 98.22%, whereas the SBODL-FCC, DNN, AlexNet, VGG-16, ResNet, and SVM models obtain decreased  values of 97.43%, 86.23%, 90.49%, 90.35%, 87.70%, and 86.69%, respectively.Figure 11 investigates a comparative analysis of the RSMPA-DLFCC system with cent techniques, with respect to  and  .The observational data highlighted t the RSMPA-DLFCC system attains a raised  of 93.06%, while the SBODL-FC DNN, AlexNet, VGG-16, ResNet, and SVM methods obtain reduced  values 89.02%, 86.11%, 87.68%, 85.28%, 86.42%, and 87.99%, correspondingly.In addition, RSMPA-DLFCC system attains  values of 90.42% whereas SBODL-FCC, DN AlexNet, VGG-16, ResNet, and SVM systems get decreased  values of 85.0 84.39%, 81.7%, 81.35%, 81.18%, and 83.61%, respectively.These experimental data in cated that the RSMPA-DLFCC methodology reaches the maximum food crop classifi tion process.

Conclusions
This manuscript offered the development of automated food crop classification usin the RSMPA-DLFCC technique.The RSMPA-DLFCC technique mainly investigates the R data and determines different types of food crops.In the RSMPA-DLFCC technique, th SimAM-EfficientNet model is utilized for the feature extraction process.The MPA is a plied for the optimum hyperparameter selection in order to optimize the accuracy SimAM-EfficientNet architecture.The simulation analysis of the RSMPA-DLFCC metho takes place on benchmark UAV image dataset.The widespread result analysis portraye the higher performance of the RSMPA-DLFCC approach over existing DL models, with maximum accuracy of 98.22%.In future work, real-time remote sensing data will be priority, enabling the model to adapt dynamically to changing crop conditions and emer ing threats.Moreover, future work can focus on the integration of multi-modal da sources, such as thermal imaging or hyperspectral data, and will broaden the scope crop classification, providing a more comprehensive understanding of crop health an types.Finally, field tests can be performed to assess the real-world performance and a curacy of the RSMPA-DLFCC technique in diverse agricultural settings and will be esse tial for its practical deployment and validation.lid University for funding this work through large group Research Project under grant numb

Conclusions
This manuscript offered the development of automated food crop classification using the RSMPA-DLFCC technique.The RSMPA-DLFCC technique mainly investigates the RS data and determines different types of food crops.In the RSMPA-DLFCC technique, the SimAM-EfficientNet model is utilized for the feature extraction process.The MPA is applied for the optimum hyperparameter selection in order to optimize the accuracy of SimAM-EfficientNet architecture.The simulation analysis of the RSMPA-DLFCC method takes place on benchmark UAV image dataset.The widespread result analysis portrayed the higher performance of the RSMPA-DLFCC approach over existing DL models, with a maximum accuracy of 98.22%.In future work, real-time remote sensing data will be a priority, enabling the model to adapt dynamically to changing crop conditions and emerging threats.Moreover, future work can focus on the integration of multi-modal data sources, such as thermal imaging or hyperspectral data, and will broaden the scope of crop classification, providing a more comprehensive understanding of crop health and types.Finally, field tests can be performed to assess the real-world performance and accuracy of the RSMPA-DLFCC technique in diverse agricultural settings and will be essential for its practical deployment and validation.
demonstrates the confusion matrices produced by the RSMPA-DLFCC technique under 80:20 and 70:30 of the TR phase/TS phase.The experimental values specified the efficient recognition of all six classes.Biomimetics 2023, 8, x FOR PEER REVIEW 9 of 18 HDD.The food crop classification performance of the RSMPA-DLFCC system is validated on the UAV image dataset [21], comprising 6450 samples with six classes.For experimental validation, we have used 80:20 and 70:30 of training (TR)/testing (TS) set. Figure 3 demonstrates the confusion matrices produced by the RSMPA-DLFCC technique under 80:20 and 70:30 of the TR phase/TS phase.The experimental values specified the efficient recognition of all six classes.

Figure 5 .
Figure 5. Average of RSMPA-DLFCC algorithm at 70:30 of TR phase/TS phase.To calculate the performance of the RSMPA-DLFCC methodology on 80:20 Phase/TS Phase, TR and TS  curves are defined, as shown in Figure 6.The TR TS  curves demonstrate the performance of the RSMPA-DLFCC technique ove merous epochs.The figure offers the details about the learning task and generaliz capabilities of the RSMPA-DLFCC system.With a rise in epoch count, it is observed the TR and TS  curves attained are enhanced.It is noted that the RSMPA-DL

Figure 7
Figure 7 illustrates an overall TR and TS loss value of the RSMPA-DLFCC methodology on 80:20 of TR Phase/TS Phase over epochs.The TR loss shows the model loss acquired reduces over epochs.Mainly, the loss values are decreased as the model adapts the weight to diminish the predicted error on the TR and TS data.The loss analysis illustrates the level where the model is fitting the training data.It is evidenced that the TR and TS loss is progressively minimized and described that the RSMPA-DLFCC technique effectively learns the patterns revealed in the TR and TS data.It is also observed that the RSMPA-DLFCC methodology modifies the parameters for reducing the difference between the predicted and actual training labels.The PR curve of the RSMPA-DLFCC approach on 80:20 of TR phase/TS phase, illustrated by plotting precision against recall as described in Figure8, confirms that the RSMPA-DLFCC technique achieves improved PR values under all classes.The figure represents that the model learns to identify different class labels.The RSMPA-DLFCC achieves improved effectiveness in the recognition of positive samples with reduced false positives.The ROC analysis, provided by the RSMPA-DLFCC system on 80:20 of TR phase/TS phase demonstrated in Figure9, has the ability the differentiate between class labels.The figure shows valuable insights into the trade-off between the TPR and FPR rates over dissimilar classification thresholds and differing numbers of epochs.It introduces the accurately predicted performance of the RSMPA-DLFCC methodology on the classification of various classes.In Table3, detailed comparative results of the RSMPA-DLFCC technique are demonstrated with current models[22,23].Figure10investigates a comparative analysis of the RSMPA-DLFCC with recent approaches in terms of accu y .The experimental values highlighted that the RSMPA-DLFCC technique reaches an increased accu y of 98.22%, whereas the SBODL-FCC, DNN, AlexNet, VGG-16, ResNet, and SVM models obtain decreased accu y values of 97.43%, 86.23%, 90.49%, 90.35%, 87.70%, and 86.69%, respectively.

Figure 7 .
Figure 7. Loss curve of RSMPA-DLFCC algorithm at 80:20 of TR phase/TS phase.The PR curve of the RSMPA-DLFCC approach on 80:20 of TR phase/TS phase, illustrated by plotting precision against recall as described in Figure8, confirms that the RSMPA-DLFCC technique achieves improved PR values under all classes.The figure represents that the model learns to identify different class labels.The RSMPA-DLFCC achieves improved effectiveness in the recognition of positive samples with reduced false positives.
, confirms that th RSMPA-DLFCC technique achieves improved PR values under all classes.The figur represents that the model learns to identify different class labels.The RSMPA-DLFCC achieves improved effectiveness in the recognition of positive samples with reduced fals positives.

Figure 10 .
Figure 10.Accu y Comparative outcome of RSMPA-DLFCC algorithm with other systems.

Table 2 .
Food crop classifier outcome of RSMPA-DLFCC algorithm at 70:30 of TR phase/TS p

Table 3 .
Comparative outcome of RSMPA-DLFCC with other systems .

Table 3 .
Comparative outcome of RSMPA-DLFCC with other systems.