Satin Bowerbird Optimization With Convolutional LSTM for Food Crop Classification on UAV Imagery

Abstract

Food crop classification and identification are crucial aspects of modern agriculture. With progression of drones or unmanned aerial vehicles (UAVs), crop detection from RGB images goes through a paradigm shift from traditional image processing methods to deep learning (DL) methods due to effective breakthroughs in convolutional neural networks (CNN). Drone images are reliable for identifying different crops because of its higher spatial resolution. Food crop classification utilizing deep learning on drone images includes machine learning techniques for distinguishing and identifying different types of crops in images captured by UAVs. It is beneficial for various applications, like crop monitoring and precision agriculture. This paper presents a new Satin Bowerbird Optimization with deep learning for Food Crop Classification (SBODL-FCC) technique on UAV images. The presented SBODL-FCC technique exploits DL models with hyperparameter optimizers for food crop classification on UAV images. To accomplish this, the presented SBODL-FCC technique employs adaptive bilateral filtering technique for image preprocessing. Besides, the SBODL-FCC technique uses MobileNetv2 feature extractor with Bayesian optimization (BO) algorithm for parameter optimization. Moreover, the food crop classification process is performed through convolutional long short-term memory (ConvLSTM) model. Furthermore, the hyperparameter tuning of the ConvLSTM method is accomplished through SBO algorithm. The experimental validation of the SBODL-FCC technique is validated on UAV image database and the results are inspected under different aspects. The simulation outcomes inferred that the SBODL-FCC technique reaches better performance over other models in terms of several performance measures.

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

“…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 accu y .…”

“…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 𝑎𝑐𝑐𝑢 .…”

“…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 .…”

GDMR-Net: A Novel Graphic Detection Neural Network via Multi-Crossed Attention and Rotation Annotation for Agronomic Applications in Supply Cyber Security

“…Kausar et al [26] proposed a pure convolutional neural network (PCNN) with a minimum number of parameters. Similarly, Mohammed et al [27] proposed a lightweight SBODL-FCC structure, which combined MobileNetv2 with convolutional short-term memory to improve classification performance for crop management. Although existing methods have been used in some applications in smart agriculture, there is still a lack of sufficient perception of fine-grained identification as well as scale attitude differences, due to the absence of specific designs or modules.…”