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Agriculture is India’s most common job, yet it lacks innovation and technology. As the world’s population expands, so does the demand for more food. Pesticides are used on farms to boost yield. The toxicity of the fertilizer has serious health repercussions for the farmer. So, it’s recommended to measure the amount of pesticide used and only apply it when necessary. We devised an insect-finding and insecticide-spraying mechanism. This is accomplished by employing a drone or Uninterrupted Ariel Vehicle. The drone has a camera that can photograph fields and lift pesticides weighing 3 to 4kg. After locating the insect, the insecticide is sprayed through the nozzles. In the proposed model, the Deep Convolutional Neural Network (CNN) has reached state of the art in image processing and object detection issues. Deep CNN has the potential to self-learn hidden features that help with insect detection. When compared to other similar approaches, experimental findings on a real dataset to illustrate the usefulness of the suggested methodology. We identified insects on the crop with 90% accuracy using deep CNN. It helps farmers to increase crop yield while also shielding them from the detrimental effects of spraying pesticides on the field manually.

Emotion detection involves distinguishing the complex nature of emotions, cross-cultural variations, and ethical considerations. The proposed model addresses cross-cultural issues through diverse training and ensures ethical data usage through advanced machine-learning techniques to enhance accuracy. The research aims to develop a Dusky Canidae Optimization-based Deep Convolutional Neural Network model (DCO opt deep CNN) for emotion recognition in audio-visual data, benefiting various advancements. During preprocessing, audio and video are separated to enhance sound quality and eliminate data redundancy while retaining vital information. Features are then extracted and employed for training and testing using the automation and effectiveness of Deep CNNs. The concatenated features are optimized using a hybrid approach that combines Dusky Hawk and Canidae Wolf Optimization (CWO), offering simplicity and interpretability with optimal parameter settings. The proposed DCO model effectively classifies emotions within the data. Evaluating the database, the DCO model exhibits a performance enhancement of 1.28%, 2.91%, and 1.28% in accuracy, sensitivity, and specificity with respect to the existing learner memorizing optimization-based deep NN model.

Video content analysis (VCA) is the process of analyzing the contents in the video for various applications. Video classification and content analysis are two of the most difficult challenges that computer vision researchers must solve. Object detection plays an important role in the VCA and is used for identification, detection and classification of objects in the images. The Chaser Prairie Wolf optimization-based deep Convolutional Neural Network classifier (CPW opt-deep CNN classifier) is used in this research to identify and classify the objects in the videos. The deep CNN classifier correctly detected the objects in the video, and the CPW optimization boosted the deep CNN classifier’s performance, where the decision-making behavior of the chasers is enhanced by the sharing nature of the prairie wolves. The classifier’s parameters were successfully tuned by the enabled optimization, which also aids in producing better results. The Ensemble model developed for the object detection adds value to the research and is initiated by the standard hybridization of the YOLOv4 and Resnet 101 model, which evaluated the research’s accuracy, sensitivity, and specificity, improving its efficacy. The proposed CPW opt-deep CNN classifier attained the values of 89.74%, 89.50%, and 89.19% while classifying objects in dataset 1, 91.66%, 86.01%, and 91.52% while classifying objects in dataset 2, compared to the preceding method that is efficient.

Object detection from underwater sea images based on deep learning techniques provides preferable results in a controlled environment. Yet, these techniques experience some challenges in detecting underwater objects due to color distortion, noise, and scattering. Hence, enhancing the underwater imaginary is important for accurately determining the objects under water. This research presents a deep learning approach for perceiving underwater objects from enhanced underwater images. Very Deep Super-Resolution Network (VDSR), which exhibits a higher visual quality, is utilized for improving the underwater image quality, thereby it is sufficient for object detection. Then, the object is detected from the enhanced underwater image through the proposed Border Collie Flamingo optimization-based deep CNN classifier (BCFO-based deep CNN). The developed BCFO-based algorithm is the main highlight of the research, which effectively tunes the classifier’s hyperparameter. The evaluation is established using the UIEB and DUO datasets on the basis of performance standards, such as specificity, accuracy, and sensitivity. When the training percentage is 80 and the $K$-fold is 10, the suggested model achieved accuracy results of 93.89% and 95.24%, sensitivity results of 95.93 and 97.29%, and specificity results of 98.64% and 99%, which is very efficient compared to some existing approaches.

Anomaly detection in public places using the video surveillance gains significance due to the real-time monitoring and security that ensures the personal assets and public security. Accordingly, in this research, a deep CNN model with Timber–Prairie wolf optimization algorithm (TPWO) optimization is proposed for surveillance-based anomaly detection. To support the TPWO-based deep CNN anomaly detection model, tracking model named OptSpatio tracking model tracks the location and movement of the anomalous objects in any locality. The OptSpatio tracking model uses both visual and spatial tracking models to monitor any anomalous activity. On the other hand, TPWO is designed to tune the deep classifier for acquiring better detection performance. The TPWO-based model surpasses the competent methods in terms of accuracy by 97.214%, sensitivity by 97.831% and specificity by 96.668% with minimal EER of 2.786%. The MOTP values are also obtained at a rate of 0.7325; moreover, the effectiveness of the TPWO method is justified at the object-, frame-, and pixel-level analysis.