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Quality assessment of fruits plays a key part in the global economy’s agricultural sector. In recent years, it has been shown that fruits are affected by different diseases, which can lead to widespread economic failure in the agricultural industry. Traditional manual visual grading of fruits could be more accurate, making it difficult for agribusinesses to assess quality efficiently. Automatic grading of fruits using computer vision has become a prominent area of study for many researchers. In this study, a deep learning-based model called FruitVision is proposed for the automatic grading of various fruits. The results showed that FruitVision performed all the existing models and obtained an accuracy of 99.42, 99.50, 99.24, 99.12, 99.38, 99.38, 99.17, 98.86, and 97.96% for the apple, banana, guava, lime, orange, pomegranate, Ajwa date, Mabroom date, and mango, respectively, using 5-fold cross-validation. This is a remarkable achievement in the field of AI-based fruit grading systems.

Agriculture encompasses the study, practice, and discipline of plant cultivation. Agriculture has an extensive history dating back thousands of years. Depending on climate and terrain, it began independently in various locations on the planet. In comparison to what could be sustained by foraging and gathering, agriculture has the potential to significantly increase the human population. Throughout the twenty-first century, precision farming (PF) has increased the agricultural output. precision agriculture (PA) is a technology-enabled method of agriculture that assesses, monitors, and evaluates the needs of specific fields and commodities. The primary objective of this farming method, as opposed to conventional farming, is to increase crop yields and profitability through the precise application of inputs. This work describes in depth the development and function of artificial intelligence (AI) and the internet of things (IoT) in contemporary agriculture. Modern day-to-day applications rely extensively on AI and the IoT. Modern agriculture leverages AI and IoT for technological advancement. This improves the accuracy and profitability of modern agriculture. The use of AI and IoT in modern smart precision agricultural applications is highlighted in this work and the method proposed incorporates specific steps in PF and demonstrates superior performance compared to existing classification methods. It achieves a remarkable accuracy of 98.65%, precision of 98.32%, and recall rate of 97.65% while retaining competitive execution time of 0.23 s, when analysing PF using the FAOSTAT benchmark dataset. Additionally, crucial equipment and methods used in PF are described and the vital advantages and real-time tools utilised in PA are covered in detail.

A fully fledged face recognition system consists of face detection, face alignment, and face recognition. Facial recognition has been challenging due to various unconstrained factors such as pose variation, illumination, aging, partial occlusion, low resolution, etc. The traditional approaches to face recognition have some limitations in an unconstrained environment. Therefore, the task of face recognition is improved using various deep learning architectures. Though the contemporary deep learning techniques for face recognition systems improved overall efficiency, a resilient and efficacious system is still required. Therefore, we proposed a hybrid ensemble convolutional neural network (HE-CNN) framework using ensemble transfer learning from the modified pre-trained models for face recognition. The concept of progressive training is used for training the model that significantly enhanced the recognition accuracy. The proposed modifications in the classification layers and training process generated best-in-class results and improved the recognition accuracy. Further, the suggested model is evaluated using a self-created criminal dataset to demonstrate the use of facial recognition in real-time. The suggested HE-CNN model obtained an accuracy of 99.35, 91.58, and 95% on labeled faces in the wild (LFW), cross pose LFW, and self-created datasets, respectively.

Radio telemetry is being used in India to capture the location and movements of radio-collared wild animals. Radio telemetry data analysis has changed the scenario for wildlife conservation. It has provided many models and solutions for natural habitat utilization, genetic diversity, and even implementing green corridors for the species. Insights drawn from the data collected through radio telemetry from radio-collared animals have provided impactful information to understand the wild animal’s ecology. In the past few years, India has remarkably improved the population of tigers using telemetry studies and other conservation methods. In this study, a review of the telemetry studies conducted for tigers in India in the past decade has been presented. This study summarizes the telemetry studies of tigers in India and discusses how telemetry has enhanced the knowledge of ecologists and scientists to understand tiger ecology. This study also discusses the challenges and issues of conducting telemetry studies on tigers in India. Finally, the authors address the gaps in understanding the tiger’s ecology based on these studies and outline how radio telemetry can address these issues to better understand and conserve tigers.

Internet of Things (IoT) is a physical network of physical devices, such as widgets, structures, and other objects, which can store program, sensors, actuators, and screen configurations to allow the objects to assemble, control, display, and exchange data. The aim of this research was to develop an autonomous system with automated navigation. Using this approach, we are able to make use of deep neural networks for automatic navigation as well as the identification of pot holes and road conditions. Additionally, it displays potholes in traffic and the correct lane on the screen. The system stresses how important it is to select the path from one node to the next.