Automatic Optical Imaging System for Mango Fruit using Hyperspectral Camera and Deep Learning Algorithm

This research paper explores focused on developing an automatic mango fruit quality detection system using a combination of artificial intelligence and the Internet of Things technologies. The system utilizes a hyperspectral camera to capture images of the mango fruit and image processing techniques to analyze the images. Deep learning algorithms are employed to classify the mango fruit based on quality parameters such as ripeness, size, and color. The proposed system aims to automate the mango fruit quality inspection process, improve the accuracy of quality assessment, and reduce human error. The results of this research could have applications in the food industry, specifically in the field of fruit quality inspection and sorting. Mango Fruit, Hyperspectral Camera, Image Processing, Deep Learning algorithms, Quality Recognition

Application of Data Visualization and Big Data Analysis in Intelligent Agriculture

Intelligent agriculture can renovate agricultural production and management, making agricultural production truly scientific and efficient. The existing data mining technology for agricultural information is powerful and professional. But the technology is not well adapted for intelligent agriculture. Therefore, this paper introduces data visualization and big data analysis into the application scenarios of intelligent agriculture. Firstly, an intelligent agriculture data visualization system was established, and the RadViz data visualization method was detailed for intelligent agriculture. Moreover, the intelligent agriculture data were processed using dimensionality reduction through principal component analysis (PCA) and further optimized through k-means clustering (KMC). Finally, the crop yield was predicted using the multiple regression algorithm and the residual principal component regression algorithm. The crop yield prediction model was proved effective through experiments

IoT Based Machine Learning Weather Monitoring and Prediction Using WSN

A novel approach to analysis and prediction is provided by the internet of things-based time monitoring and prediction system using wireless sensor networks (WSN) and machine learning techniques (ML). To give accurate meteorological data in real time, the integrated system uses IoT, WSN, and ML. Making informed decisions requires these insights. Includes strategically positioned infrared points that are used to gather meteorological information, such as temperature, humidity, pressure, and wind speed, among other things.The machine's automatic data processing methods are then used in a central processing unit to collect and analyse the data. By seeing patterns and drawing diagrams utilising previously collected data, ML models are able to comprehend intricate temporal dynamics. An important development in this system is its predictive capabilities. Artificial intelligence has the processing power to precisely forecast short-term weather patterns, enabling the rapid transmission of warnings for extreme localised events and the reduction of potential dangers.The combination of historical data, real-time sensor inputs, and automated analysis produces the predictive potential. The "Internet of Things" architecture used to develop this system makes it simpler to gather meteorological data. A number of industries, including as agriculture, transportation, emergency management, and event planning, are encouraged to make data-based decisions since users can quickly obtain current meteorological conditions and forecasts through user-friendly web interfaces or mobile applications

IoT-Based Environmental Control System for Fish Farms with Sensor Integration and Machine Learning Decision Support

In response to the burgeoning global demand for seafood and the challenges of managing fish farms, we introduce an innovative IoT-based environmental control system that integrates sensor technology and advanced machine learning decision support. Deploying a network of wireless sensors within the fish farm, we continuously collect real-time data on crucial environmental parameters, including water temperature, pH levels, humidity, and fish behavior. This data undergoes meticulous preprocessing to ensure its reliability, including imputation, outlier detection, feature engineering, and synchronization. At the heart of our system are four distinct machine learning algorithms: Random Forests predict and optimize water temperature and pH levels for the fish, fostering their health and growth; Support Vector Machines (SVMs) function as an early warning system, promptly detecting diseases and parasites in fish; Gradient Boosting Machines (GBMs) dynamically fine-tune the feeding schedule based on real-time environmental conditions, promoting resource efficiency and fish productivity; Neural Networks manage the operation of critical equipment like water pumps and heaters to maintain the desired environmental conditions within the farm. These machine learning algorithms collaboratively make real-time decisions to ensure that the fish farm's environmental conditions align with predefined specifications, leading to improved fish health and productivity while simultaneously reducing resource wastage, thereby contributing to increased profitability and sustainability. This research article showcases the power of data-driven decision support in fish farming, promising to meet the growing demand for seafood while emphasizing environmental responsibility and economic viability, thus revolutionizing the future of fish farming

A Comprehensive Review on Intelligent Techniques in Crop Pests and Diseases

Artificial intelligence (AI) has transformative potential in the agricultural sector, particularly in managing and preventing crop diseases and pest infestations. This review discusses the significance of early detection and precise diagnosis of various AI tools and techniques for disease identification, such as image processing, machine learning, and deep learning. It also addresses the challenges of AI implementation in agriculture, including data quality, costs, and ethical concerns. The analysis classifies the hurdles and AI offers benefits such as improved resource management, timely interventions, and enhanced productivity. Collaborative efforts are essential to harness AI's potential for sustainable and resilient agriculture

AICropCAM: Deploying classification, segmentation, detection, and counting deep-learning models for crop monitoring on the edge

Precision Agriculture (PA) promises to meet the future demands for food, feed, fiber, and fuel while keeping their production sustainable and environmentally friendly. PA relies heavily on sensing technologies to inform site-specific decision supports for planting, irrigation, fertilization, spraying, and harvesting. Traditional point-based sensors enjoy small data sizes but are limited in their capacity to measure plant and canopy parameters. On the other hand, imaging sensors can be powerful in measuring a wide range of these parameters, especially when coupled with Artificial Intelligence. The challenge, however, is the lack of computing, electric power, and connectivity infrastructure in agricultural fields, preventing the full utilization of imaging sensors. This paper reported AICropCAM, a field-deployable imaging framework that integrated edge image processing, Internet of Things (IoT), and LoRaWAN for low-power, long-range communication. The core component of AICropCAM is a stack of four Deep Convolutional Neural Networks (DCNN) models running sequentially: CropClassiNet for crop type classification, CanopySegNet for canopy cover quantification, PlantCountNet for plant and weed counting, and InsectNet for insect identification. These DCNN models were trained and tested with \u3e43,000 field crop images collected offline. AICropCAM was embodied on a distributed wireless sensor network with its sensor node consisting of an RGB camera for image acquisition, a Raspberry Pi 4B single-board computer for edge image processing, and an Arduino MKR1310 for LoRa communication and power management. Our testing showed that the time to run the DCNN models ranged from 0.20 s for InsectNet to 20.20 s for CanopySegNet, and power consumption ranged from 3.68 W for InsectNet to 5.83 W for CanopySegNet. The classification model CropClassiNet reported 94.5 % accuracy, and the segmentation model CanopySegNet reported 92.83 % accuracy. The two object detection models PlantCountNet and InsectNet reported mean average precision of 0.69 and 0.02 for the test images. Predictions from the DCNN models were transmitted to the ThingSpeak IoT platform for visualization and analytics. We concluded that AICropCAM successfully implemented image processing on the edge, drastically reduced the amount of data being transmitted, and could satisfy the real-time need for decision-making in PA. AICropCAM can be deployed on moving platforms such as center pivots or drones to increase its spatial coverage and resolution to support crop monitoring and field operations

A Systematic Review of Data Quality in CPS and IoT for Industry 4.0

The Internet of Things (IoT) and Cyber-Physical Systems (CPS) are the backbones of Industry 4.0, where data quality is crucial for decision support. Data quality in these systems can deteriorate due to sensor failures or uncertain operating environments. Our objective is to summarize and assess the research efforts that address data quality in data-centric CPS/IoT industrial applications. We systematically review the state-of-the-art data quality techniques for CPS and IoT in Industry 4.0 through a systematic literature review (SLR) study. We pose three research questions, define selection and exclusion criteria for primary studies, and extract and synthesize data from these studies to answer our research questions. Our most significant results are (i) the list of data quality issues, their sources, and application domains, (ii) the best practices and metrics for managing data quality, (iii) the software engineering solutions employed to manage data quality, and (iv) the state of the data quality techniques (data repair, cleaning, and monitoring) in the application domains. The results of our SLR can help researchers obtain an overview of existing data quality issues, techniques, metrics, and best practices. We suggest research directions that require attention from the research community for follow-up work.acceptedVersio