# A Metaheuristic Harris Hawks Optimization Algorithm for Weed Detection Using Drone Images
## 2 Related Work  
Traditional machine-learning approaches require a limited sample size, a quick training period, and a few graphics-processing units to calculate picture texture, shape, hue, or spectral properties to identify crops or weeds.  
There have been several attempts in the literature to generate weed maps from unmanned aerial vehicle (UAV) images using a variety of classification approaches [23; 24]. However, recent state-of-art work shows [25] that machine-learning algorithms, when dealing with complex data, are more accurate than conventional algorithms. The utilization of high-resolution UAV imagery and the application of the random forest (RF) algorithm for agricultural mapping have been demonstrated to yield advantageous outcomes [7; 8; 26]. SVM classifier is also commonly used for the classification of crops and weeds [27; 28].  
For early cranesbill seedling extraction [29], improved Mask RCNN was used. The proposed method separated weeds from the original image, allowing an increase in yield.  
Deep neural networks (DNNs) were used to segregate weed crops [30]. Four additional elements improved segmentation accuracy, improving weed detection for complicated shapes in tough environments without image preprocessing or data conversion. Recognition accuracy is higher than the machine-learning techniques that use manually specified features. Deep learning was used to detect ryegras species from weeds in [31]. The dataset included photos of ryegras seedlings, maculata, hederacea, and officiale weeds--16,116 weed-free samples and 18,230 weedy samples. The dataset was trained with GoogLeNet, AlexNet, and VGGNet. VGGNet's F-score was the highest. Maculata, hederacea, and officiale had success rates of 92.65%, 99.84%, and 97.37%, respectively.  
Convolutional Neural Network (CNN) architectures are better at classifying plant species. The AlexNet model had a 99.5% accuracy on a grass-broadleaf dataset, according to [32]. In categorizing four weed species, [PERSON] et al. [33] attained an average accuracy of 98.90% for VGG16, 97.80% for ResNet50, and 96.70% for Inceptionv3.  
[PERSON] and [PERSON] [34] tested ANN and AlexNet weed recognition algorithms on UAV photos. AlexNet's weed-detection rate was over 99%, while the ANN's was 48% on the same dataset.  
Metaheuristic optimization rarely optimizes weed categorization models. Computer vision is used in [35] to recognize potato plants and three weed categories. The proposed method improves NN-classifier performance using two metaheuristic methods, the cultural algorithm by which the five most significant features are selected and the harmony search method for finding the network architecture. The system's identification accuracy was 98.38%.  
ANN was used to categorize narrowband and broadleaf weed species and rice species in [36]. A total of 97 rice images, 77 narrowband, and 52 broadleaf images were used. Particleswarm optimization (PSO) and the bee algorithm (BA) identified efficient features. The ANN-BA method was the best. The study found an arithmetic mean accuracy of 92.02% and geometric mean accuracy of 90.7%.  
A unique optimization strategy to improve weed image-classification accuracy was given by the authors in [37], which categorizes spraying drone weed and wheat shots differently. SVM, NN, and KNN were the classifiers optimized using cosine, sine, and gray wolf optimizers. The optimized voting classifier showed 97.70% detection accuracy, 98.60% F-score, 95.20% specificity, and 98.40% sensitivity.  
The authors in Ref. [38] introduce a technique called Modified Barnacles Mating Optimization with Deep-Learning-based Weed Detection (MBMODL-WD). MBMODL-WD uses Gabor filtering (GF) for noise removal, DenseNet-121 for feature extraction, and the MBMO algorithm for hyperparameter optimization. Elman Neural Network (ENN) was used to classify weeds. MBMODL-WD performed with maximum accuracy of 98.99%.