Enhancing model performance in agricultural image analysis faces challenges due to limited datasets, biological variability, and uncontrolled environments. Deep learning models require large, realistic datasets, which are often difficult to obtain. Data augmentation, especially through Generative Adversarial Networks (GANs), has become essential in farming applications, generating synthetic images to improve model training and reduce the need for extensive image collection. This review explores various GAN approaches for image augmentation in farming, investigating their challenges and limitations. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines, 128 publications were analyzed to identify research trends and gaps in GAN applications within the farming industry. Key applications of GANs include plant classification, weed detection, animal detection and behavior recognition, animal health and disease analysis, plant disease detection, phenotyping, and fruit quality assessment. Persistent issues like limited training datasets, occlusion challenges, and imbalanced data hinder model performance across these applications. Recognizing these challenges is critical for enhancing the efficiency and effectiveness of farming operations. Finally, this review concludes with insights and future directions to foster progress in this field.

The demand for bandwidth-critical applications has stimulated the research community not only to develop new ways of communication, but also to use the existing spectrum efficiently. Networks have become dynamic and heterogeneous. Receivers have received various signals that can be modulated differently. Automatic modulation classification (AMC) is a key procedure for present and next-generation communication networks, and facilitates the demodulation process at the receiver side. Under the presence of noise from the channel, the transmitter and receiver with its unknown parameters, such as carrier frequency, phase offset, signal power, and timing information, have become cumbersome because detecting the modulation scheme of the received signal is a complicated procedure. Two main methods, namely maximum likelihood functions and the signal statistical feature-based (FB) approach, are used for the automatic classification of modulated signals. In this study, a comprehensive survey of various modulation techniques based on FB approach is conducted. In this research, a number of basic features that are usually used in determining and discriminating modulation types were investigated. The classifier that was used in the discrimination process is studied in detail and compared to other types of classifiers to help the reader determine the limitations associated with the FB approach. Both classifiers and basic features were compared, and their advantages and disadvantages were investigated based on previous researches to determine the best type of classifier and the set of features in relation to each discrimination environment. This work serves as a guide for researchers of AMC to determine the suitable features and algorithms.

A new theory called Vivaldi nonuniform slot profile antenna (VNSPA) is developed based on the nonuniform transmission lines (NTLs) theory and applied to reduce the size of the predesigned compact ultrawide band (UWB) Vivaldi-tapered slot antenna (VTSA) without affecting its performance in terms of matching and bandwidth. Using this theory, 33% of the taper slot length is reduced, resulting in a novel compact UWB Vivaldi nonuniform slot antenna (VNSA). Furthermore, compactness is achieved by performing parametric studies on VNSA with a final 51.94% size reduction. MATLAB optimization codes, CST simulation, and prototype manufacturing and measurement are used to validate the new theory. The proposed antenna provides a measured S ₁₁ < −10.89 dB through 2.9–13.55 GHz with 2.91% and 5.8% bandwidth and maximum realized gain enhancement, respectively, as compared with VTSA’s results. The effectiveness of VNSPA theory is proven by comparing the simulated and measured findings of VNSA and VTSA.

An engineered composite conductor is essential for developing a wearable antenna that is not only flexible but also stretchable. This paper presents the use of polydimethylsiloxane (PDMS) as the substrate and custom polydimethylsiloxane-silver conductive paste for wearable applications. The antenna is designed with an air gap PDMS substrate between the patch and sawtooth partial ground at 3.5 GHz to enhance the bandwidth and gain. Furthermore, the proposed antenna is flexible and can be bent as well as stretched up to 20%, making it suitable for use on the human body. This study investigates the antenna’s performance under bending and stretching to mimic the human body’s structure and movements. Additionally, the specific absorption rate (SAR) of the wearable antenna was analyzed for safety purposes.

The automatic modulation classification (AMC) of a detected signal has gained considerable prominence in recent years owing to its numerous facilities. Numerous studies have focused on feature-based AMC. However, improving accuracy under low signal-to-noise ratio (SNR) rates is a serious issue in AMC. Moreover, research on the enhancement of AMC performance under low and high SNR rates is limited. Motivated by these issues, this study proposes AMC using a feature clustering-based two-lane capsule network (AMC2N). In the AMC2N, accuracy of the MC process is improved by designing a new two-layer capsule network (TL-CapsNet), and classification time is reduced by introducing a new feature clustering approach in the TL-CapsNet. Firstly, the AMC2N executes blind equalization, sampling, and quantization in trilevel preprocessing. Blind equalization is executed using a binary constant modulus algorithm to avoid intersymbol interference. To extract features from the preprocessed signal and classify signals accurately, the AMC2N employs the TL-CapsNet, in which individual lanes are incorporated to process the real and imaginary parts of the signal. In addition, it is robust to SNR variations, that is, low and high SNR rates. The TL-CapsNet extracts features from the real and imaginary parts of the given signal, which are then clustered based on feature similarity. For feature extraction and clustering, the dynamic routing procedure of the TL-CapsNet is adopted. Finally, classification is performed in the SoftMax layer of the TL-CapsNet. This study proves that the AMC2N outperforms existing methods, particularly, convolutional neural network(CNN), Robust-CNN (R-CNN), curriculum learning(CL), and Local Binary Pattern (LBP), in terms of accuracy, precision, recall, F-score, and computation time. All metrics are validated in two scenarios, and the proposed method shows promising results in both.

Automatic modulation classification (AMC) is a method that supported different wireless communication systems for modulation type classification. Currently, orthogonal frequency division multiplexing, multiple-input, multiple-output systems are widely using this technique. Recent AMC methods are designed for a single-carrier system identifying a few modulation types. To motivate the AMC for the current communication systems, we present an intelligent pyramid model for automatic multi-carrier modulation classification (AMC2-pyramid) which alleviates the existing works challenges such as high degradation of accuracy for higher order modulation schemes, inefficient feature extraction and lack of effectiveness in low SNR environments. The proposed work contains three significant operations, namely, signal fortification, feature engineering and modulation classification. First, signal quality is estimated to reduce the complexity in classification because some signals are affected by noise and other environmental or channel artefacts. Hence, before pre-processing the signal, the quality is assessed according to the channel state information, signal to inference plus noise ratio, received signal strength indicator and spectral efficiency. For low quality, quality augmentation is applied. Then, quality augmentation is implemented with noise elimination, equalisation, quantisation and channel frequency offset compensation. In the feature engineering step, feature extraction and clustering are presented using the Gated Feature Response Pyramid Network (GaFP), and a twin-functioned human mental search algorithm is used.