An Interpretable Artificial Intelligence Based Smart Agriculture System

With increasing world population the demand of food production has increased exponentially.Internet of Things(IoT)based smart agriculture system can play a vital role in optimising crop yield by managing crop requirements in real-time.Interpretability can be an important factor to make such systems trusted and easily adopted by farmers.In this paper,we propose a novel artificial intelligence-based agriculture system that uses IoT data to monitor the environment and alerts farmers to take the required actions for maintaining ideal conditions for crop production.The strength of the proposed system is in its interpretability which makes it easy for farmers to understand,trust and use it.The use of fuzzy logic makes the system customisable in terms of types/number of sensors,type of crop,and adaptable for any soil types and weather conditions.The proposed system can identify anomalous data due to security breaches or hardware malfunction using machine learning algorithms.To ensure the viability of the system we have conducted thorough research related to agricultural factors such as soil type,soil moisture,soil temperature,plant life cycle,irrigation requirement and water application timing for Maize as our target crop.The experimental results show that our proposed system is interpretable,can detect anomalous data,and triggers actions accurately based on crop requirements.

Understanding the potential applications of Artificial Intelligence in Agriculture Sector

Artificial Intelligence(AI)has been extensively applied in farming recently.To cultivate healthier crops,manage pests,monitor soil and growing conditions,analyse data for farmers,and enhance other management activities of the food supply chain,the agriculture sector is turning to AI technology.It makes it challenging for farmers to choose the ideal time to plant seeds.AI helps farmers choose the optimum seed for a particular weather scenario.It also offers data on weather forecasts.AI-powered solutions will help farmers produce more with fewer resources,increase crop quality,and hasten product time to reach the market.AI aids in understanding soil qualities.AI helps farmers by suggesting the nutrients they should apply to increase the quality of the soil.AI can help farmers choose the optimal time to plant their seeds.Intelligent equipment can calculate the spacing between seeds and the maximum planting depth.An AI-powered system known as a health monitoring system provides farmers with information on the health of their crops and the nutrients that need to be given to enhance yield quality and quantity.This study identifies and analyses relevant articles on AI for Agriculture.Using AI,farmers can now access advanced data and analytics tools that will foster better farming,improve efficiencies,and reduce waste in biofuel and food production while minimising the negative environmental impacts.AI and Machine Learning(ML)have transformed various industries,and the AI wave has now reached the agriculture sector.Companies are developing several technologies to make monitoring farmers'crop and soil health easier.Hyperspectral imaging and 3D laser scanning are the leading AI-based technologies that can help ensure crop health.These AI-powered technologies collect precise data on the health of the crops in greater volume for analysis.This paper studied AI and its need in Agriculture.The process of AI in Agriculture and some Agriculture parameters monitored by AI are briefed.Finally,we identified and discussed the significant applications of AI in agriculture.

A comprehensive review on automation in agriculture using artificial intelligence

Agriculture automation is the main concern and emerging subject for every country.The world population is increasing at a very fast rate and with increase in population the need for food increases briskly.Traditional methods used by farmers aren't sufficient enough to serve the increasing demand and so they have to hamper the soil by using harmful pesticides in an intensified manner.This affects the agricultural practice a lot and in the end the land remains barren with no fertility.This paper talks about different automation practices like IOT,Wireless Communications,Machine learning and Artificial Intelligence,Deep learning.There are some areas which are causing the problems to agriculture field like crop diseases,lack of storage management,pesticide control,weed management,lack of irrigation and water management and all this problems can be solved by above mentioned different techniques.Today,there is an urgent need to decipher the issues like use of harmful pesticides,controlled irrigation,control on pollution and effects of environment in agricultural practice.Automation of farming practices has proved to increase the gain from the soil and also has strengthened the soil fertility.This paper surveys the work of many researchers to get a brief overview about the current implementation of automation in agriculture.The paper also discusses a proposed system which can be implemented in botanical farm for flower and leaf identification and watering using IOT.

Integrating artificial intelligence and high-throughput phenotyping for crop improvement

Crop improvement is crucial for addressing the global challenges of food security and sustainable agriculture.Recent advancements in high-throughput phenotyping(HTP)technologies and artificial intelligence(AI)have revolutionized the field,enabling rapid and accurate assessment of crop traits on a large scale.The integration of AI and machine learning algorithms with HTP data has unlocked new opportunities for crop improvement.AI algorithms can analyze and interpret large datasets,and extract meaningful patterns and correlations between phenotypic traits and genetic factors.These technologies have the potential to revolutionize plant breeding programs by providing breeders with efficient and accurate tools for trait selection,thereby reducing the time and cost required for variety development.However,further research and collaboration are needed to overcome the existing challenges and fully unlock the power of HTP and AI in crop improvement.By leveraging AI algorithms,researchers can efficiently analyze phenotypic data,uncover complex patterns,and establish predictive models that enable precise trait selection and crop breeding.The aim of this review is to explore the transformative potential of integrating HTP and AI in crop improvement.This review will encompass an in-depth analysis of recent advances and applications,highlighting the numerous benefits and challenges associated with HTP and AI.

Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and crops.The recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,etc.On the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss.Besides,earlier and precise apple leaf disease detection can minimize the spread of the disease.Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf diseases.With this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision agriculture.The proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal processes.In addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature vectors.Moreover,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet model.Finally,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf images.The design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the work.Awide range of experiments was performed to showcase the supremacy of the AIE-ALDC technique.The experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.

Automation and digitization of agriculture using artificial intelligence and internet of things

The growing population and effect of climate change have put a huge responsibility on the agriculture sector to increase food-grain production and productivity.In most of the countries where the expansion of cropland is merely impossible,agriculture automation has become the only option and is the need of the hour.Internet of things and Artificial intelligence have already started capitalizing across all the industries including agriculture.Advancement in these digital technologies has made revolutionary changes in agriculture by providing smart systems that can monitor,control,and visualize various farmoperations in real-time andwith comparable intelligence of human experts.The potential applications of IoT and AI in the development of smart farmmachinery,irrigation systems,weed and pest control,fertilizer application,greenhouse cultivation,storage structures,drones for plant protection,crop health monitoring,etc.are discussed in the paper.The main objective of the paper is to provide an overview of recent research in the area of digital technology-driven agriculture and identification of the most prominent applications in the field of agriculture engineering using artificial intelligence and internet of things.The research work done in the areas during the last 10 years has been reviewed from the scientific databases including PubMed,Web of Science,and Scopus.It has been observed that the digitization of agriculture using AI and IoT hasmatured fromtheir nascent conceptual stage and reached the execution phase.The technical details of artificial intelligence,IoT,and challenges related to the adoption of these digital technologies are also discussed.This will help in understanding how digital technologies can be integrated into agriculture practices and pave the way for the implementation of AI and IoT-based solutions in the farms.

Crossing the Achilles Heel of Algorithms:Identifying the Developmental Dilemma of Artificial Intelligence-Assisted Judicial Decision-Making

In the developmental dilemma of artificial intelligence(AI)-assisted judicial decision-making,the technical architecture of AI determines its inherent lack of transparency and interpretability,which is challenging to fundamentally improve.This can be considered a true challenge in the realm of AI-assisted judicial decision-making.By examining the court’s acceptance,integration,and trade-offs of AI technology embedded in the judicial field,the exploration of potential conflicts,interactions,and even mutual shaping between the two will not only reshape their conceptual connotations and intellectual boundaries but also strengthen the cognition and re-interpretation of the basic principles and core values of the judicial trial system.

Transforming organic chemistry research paradigms:Moving from manual efforts to the intersection of automation and artificial intelligence

Organic chemistry is undergoing a major paradigm shift,moving from a labor-intensive approach to a new era dominated by automation and artificial intelligence(AI).This transformative shift is being driven by technological advances,the ever-increasing demand for greater research efficiency and accuracy,and the burgeoning growth of interdisciplinary research.AI models,supported by computational power and algorithms,are drastically reshaping synthetic planning and introducing groundbreaking ways to tackle complex molecular synthesis.In addition,autonomous robotic systems are rapidly accelerating the pace of discovery by performing tedious tasks with unprecedented speed and precision.This article examines the multiple opportunities and challenges presented by this paradigm shift and explores its far-reaching implications.It provides valuable insights into the future trajectory of organic chemistry research,which is increasingly defined by the synergistic interaction of automation and AI.

Robotic Process Automation with New Future Trends

The invention concept of Robotic Process Automation (RPA) has emerged as a transformative technology that has revolved the local business processes by programming repetitive task and efficiency adjusting the operations. This research had focused on developing the RPA environment and its future features in order to elaborate on the projected policies based on its comprehensive experiences. The current and previous situations of industry are looking for IT solutions to fully scale their company Improve business flexibility, improve customer satisfaction, improve productivity, accuracy and reduce costs, quick scalability in RPA has currently appeared as an advance technology with exceptional performance. It emphasizes future trends and foresees the evolution of RPA by integrating artificial intelligence, learning of machine and cognitive automation into RPA frameworks. Moreover, it has analyzed the technical constraints, including the scalability, security issues and interoperability, while investigating regulatory and ethical considerations that are so important to the ethical utilization of RPA. By providing a comprehensive analysis of RPA with new future trends in this study, researcher’s ambitions to provide valuable insights the benefits of it on industrial performances from the gap observed so as to guide the strategic decision and future implementation of the RPA.

Emerging use of artificial intelligence in inflammatory bowel disease

Inflammatory bowel disease(IBD)is a complex,immune-mediated gastrointestinal disorder with ill-defined etiology,multifaceted diagnostic criteria,and unpredictable treatment response.Innovations in IBD diagnostics,including developments in genomic sequencing and molecular analytics,have generated tremendous interest in leveraging these large data platforms into clinically meaningful tools.Artificial intelligence,through machine learning facilitates the interpretation of large arrays of data,and may provide insight to improving IBD outcomes.While potential applications of machine learning models are vast,further research is needed to generate standardized models that can be adapted to target IBD populations.

A Survey on Smart Agriculture:Development Modes,Technologies,and Security and Privacy Challenges

With the deep combination of both modern information technology and traditional agriculture,the era of agriculture 4.0,which takes the form of smart agriculture,has come.Smart agriculture provides solutions for agricultural intelligence and automation.However,information security issues cannot be ignored with the development of agriculture brought by modern information technology.In this paper,three typical development modes of smart agriculture(precision agriculture,facility agriculture,and order agriculture)are presented.Then,7 key technologies and 11 key applications are derived from the above modes.Based on the above technologies and applications,6 security and privacy countermeasures(authentication and access control,privacy-preserving,blockchain-based solutions for data integrity,cryptography and key management,physical countermeasures,and intrusion detection systems)are summarized and discussed.Moreover,the security challenges of smart agriculture are analyzed and organized into two aspects:1)agricultural production,and 2)information technology.Most current research projects have not taken agricultural equipment as potential security threats.Therefore,we did some additional experiments based on solar insecticidal lamps Internet of Things,and the results indicate that agricultural equipment has an impact on agricultural security.Finally,more technologies(5 G communication,fog computing,Internet of Everything,renewable energy management system,software defined network,virtual reality,augmented reality,and cyber security datasets for smart agriculture)are described as the future research directions of smart agriculture.

Implications of Implementation of Artificial Intelligence in the Banking Business with Correlation to the Human Factor

Banks daily interact with a vast number of customers and are still depending on a legacy system. With today’s advances in technology, regarding lifting almost all processes to automation, from start of production to finish, there is a need for revolution in archaic monetary management institutes. By not being in tune with the contemporary trends and times, banks are losing on an opportunity to transform some of their business models and relieve humans of repetitive work, prevent frauds, make better decisions and consequently gain losses. Banks can engage in implementation of new Virtual Assistants and Artificial Intelligence (A.I.) machine learning technologies, just as the other industries have engaged in modernizing i.e. medical checks, medical reports and evaluations, and this research paper will elaborate and emphasize the impact of artificial intelligence implementation on the banking sector processes. This research is based on both quantitative and model-based proofs of system performance by using several analytical tools, such as SPSS. The automation process helps institutions to enhance profitability, performance and to reduce human dependency. In a nutshell, Virtual Assistants powered with Artificial Intelligence improve the business process performance in every sector of business, especially the banking sector making it fast, reliable and not human dependent.

Assessing Policy Measures Safeguarding Workers from Artificial Intelligence in the United States

With the proliferation of artificial intelligence (AI) technology, the profound impact it has had on the economy cannot be ignored. With each passing day, AI advancements grow increasingly significant, demanding the close attention of both corporations and governments. It is imperative for all stakeholders to grasp the ramifications of AI on the workforce and societal inequality. While past research has predominantly revolved around the potential of AI-driven automation and the specter of job displacement, a crucial aspect often overlooked has been policy evaluation that considers those directly impacted—employers and employees within the workplace. Through a comprehensive survey encompassing the perspectives of over 5000 individuals and 2000 firms, we endeavor to unravel the intricate web of AI implementation within professional settings and, by proxy, potential policy solutions to combat the various aspects of AI. The revelations stemming from our study are telling Training emerges as an indispensable catalyst in the assimilation of AI, rendering it more effective and, notably, enhancing the perceptions of AI among the workforces. Furthermore, consultations surrounding AI integration within organizations prove to be a positive force, facilitating its harmonious coexistence with human labor. However, it is the vital nexus of communication between employers and employees that stands as the linchpin to the successful incorporation of AI into the modern workplace. Furthermore, examples of federal and regulatory policy are provided that could be used to combat concerns that will arise in accompaniment with AI. In essence, our findings implore a balanced and nuanced approach—One that empowers rather than alienates employees. Only through such an approach can we hope to foster coexistence between AI and the invaluable human workforce.

Electronic System Using Artificial Intelligence for Queue Management

The Covid-19 pandemic has brought changes in behaviour in public places. Indeed, the health and political authorities, in order to fight against the virus in a preventive manner, require the respect of barrier gestures: social distance, mask, vaccine, gel. Still in terms of public health, long waits in a place for a service have a negative impact on the health of fragile categories such as the disabled, pregnant women and the elderly. The technical devices used for queue management must now take into account the health context, identity, particularity and behaviour of people. This paper presents an electronic system developed with artificial intelligence for queue management in public facilities. This design personalises the user’s ticket by automatically integrating the name, facial image, age and possible disability status. At the counters, a system of name calling, sound and screen display, allows users to follow the queue without having a ticket printed on thermal paper with a high carbon footprint. This solution also makes illiterate users autonomous in the queue, allowing them to maintain their dignity and to respect the safety distance between people. The device allows the establishment’s manager, depending on the context, to activate positive discrimination of the disabled or the elderly, to control the Covid-19 mark or the health pass by QR Code. This queue manager performs biometric authentication by facial recognition before the user is registered in the queue register, which prevents fraud by people who do not want to respect the order of arrival of users. This work has led to the improvement of the technical management of queues by introducing more equity, inclusion, solidarity, health and ecology.

Utilizing Artificial Intelligence (AI) for the Identification and Management of Marine Protected Areas (MPAs): A Review

The article discusses the application of artificial intelligence (AI) and automation in marine conservation, specifically in relation to the protection of marine ecosystems and the definition of marine protected areas (MPAs). It highlights the threats that marine ecosystems face due to human activities and emphasizes the importance of effective management and conservation efforts. By improving data gathering, processing, monitoring, and analysis, artificial intelligence, and automation, they can revolutionize marine research. In conclusion, this study emphasizes the importance of AI and automation in marine conservation responsibly and ethically. In order to integrate these technologies into decision-making processes, stakeholders and marine conservation professionals must collaborate. Through the use of artificial intelligence and automation, marine conservation efforts can be transformed by establishing new methods of collecting and analyzing data, making informed decisions, and managing marine ecosystems.

Deep learning automation of radiographic patterns for hallux valgus diagnosis

Artificial intelligence(AI)and deep learning are becoming increasingly powerful tools in diagnostic and radiographic medicine.Deep learning has already been utilized for automated detection of pneumonia from chest radiographs,diabetic retinopathy,breast cancer,skin carcinoma classification,and metastatic lymphadenopathy detection,with diagnostic reliability akin to medical experts.In the World Journal of Orthopedics article,the authors apply an automated and AIassisted technique to determine the hallux valgus angle(HVA)for assessing HV foot deformity.With the U-net neural network,the authors constructed an algorithm for pattern recognition of HV foot deformity from anteroposterior highresolution radiographs.The performance of the deep learning algorithm was compared to expert clinician manual performance and assessed alongside clinician-clinician variability.The authors found that the AI tool was sufficient in assessing HVA and proposed the system as an instrument to augment clinical efficiency.Though further sophistication is needed to establish automated algorithms for more complicated foot pathologies,this work adds to the growing evidence supporting AI as a powerful diagnostic tool.

Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides

Agriculture plays a significant role in the economic sector.The automation in agriculture is themain concern and the emerging subject across theworld.The population is increasing tremendously and with this increase the demand of food and employment is also increasing.The traditional methodswhich were used by the farmers,were not sufficient enough to fulfill these requirements.Thus,new automated methods were introduced.These new methods satisfied the food requirements and also provided employment opportunities to billions of people.Artificial Intelligence in agriculture has brought an agriculture revolution.This technology has protected the crop yield fromvarious factors like the climate changes,population growth,employment issues and the food security problems.This main concern of this paper is to audit the various applications of Artificial intelligence in agriculture such as for irrigation,weeding,spraying with the help of sensors and other means embedded in robots and drones.These technologies saves the excess use of water,pesticides,herbicides,maintains the fertility of the soil,also helps in the efficient use of man power and elevate the productivity and improve the quality.This paper surveys the work of many researchers to get a brief overview about the current implementation of automation in agriculture,the weeding systems through the robots and drones.The various soil water sensing methods are discussed alongwith two automatedweeding techniques.The implementation of drones is discussed,the various methods used by drones for spraying and crop-monitoring is also discussed in this paper.

Intelligent Decision-Making in Warehouse Management: How AI Automation Improves Inventory Tracking, Order Fulfillment, and Logistics Efficiency Compared to Drone Technology

This paper analyzes how artificial intelligence (AI) automation can improve warehouse management compared to emerging technologies like drone usage. Specifically, we evaluate AI’s impact on crucial warehouse functions—inventory tracking, order fulfillment, and logistics efficiency. Our findings indicate AI automation enables real-time inventory visibility, optimized picking routes, and dynamic delivery scheduling, which drones cannot match. AI better leverages data insights for intelligent decision-making across warehouse operations, supporting improved productivity and lower operating costs.

Modeling of Sensor Enabled IrrigationManagement for Intelligent Agriculture Using Hybrid Deep Belief Network

Artificial intelligence(AI)technologies and sensors have recently received significant interest in intellectual agriculture.Accelerating the application of AI technologies and agriculture sensors in intellectual agriculture is urgently required for the growth of modern agriculture and will help promote smart agriculture.Automatic irrigation scheduling systems were highly required in the agricultural field due to their capability to manage and save water deficit irrigation techniques.Automatic learning systems devise an alternative to conventional irrigation management through the automatic elaboration of predictions related to the learning of an agronomist.With this motivation,this study develops a modified black widow optimization with a deep belief network-based smart irrigation system(MBWODBN-SIS)for intelligent agriculture.The MBWODBN-SIS algorithm primarily enables the Internet of Things(IoT)based sensors to collect data forwarded to the cloud server for examination purposes.Besides,the MBWODBN-SIS technique applies the deep belief network(DBN)model for different types of irrigation classification:average,high needed,highly not needed,and not needed.The MBWO algorithm is used for the hyperparameter tuning process.A wideranging experiment was conducted,and the comparison study stated the enhanced outcomes of the MBWODBN-SIS approach to other DL models with maximum accuracy of 95.73%.

Artificial intelligence powered glucose monitoring and controlling system:Pumping module

BACKGROUND Diabetes,a globally escalating health concern,necessitates innovative solutions for efficient detection and management.Blood glucose control is an essential aspect of managing diabetes and finding the most effective ways to control it.The latest findings suggest that a basal insulin administration rate and a single,highconcentration injection before a meal may not be sufficient to maintain healthy blood glucose levels.While the basal insulin rate treatment can stabilize blood glucose levels over the long term,it may not be enough to bring the levels below the post-meal limit after 60 min.The short-term impacts of meals can be greatly reduced by high-concentration injections,which can help stabilize blood glucose levels.Unfortunately,they cannot provide long-term stability to satisfy the postmeal or pre-meal restrictions.However,proportional-integral-derivative(PID)control with basal dose maintains the blood glucose levels within the range for a longer period.AIM To develop a closed-loop electronic system to pump required insulin into the patient's body automatically in synchronization with glucose sensor readings.METHODS The proposed system integrates a glucose sensor,decision unit,and pumping module to specifically address the pumping of insulin and enhance system effectiveness.Serving as the intelligence hub,the decision unit analyzes data from the glucose sensor to determine the optimal insulin dosage,guided by a pre-existing glucose and insulin level table.The artificial intelligence detection block processes this information,providing decision instructions to the pumping module.Equipped with communication antennas,the glucose sensor and micropump operate in a feedback loop,creating a closed-loop system that eliminates the need for manual intervention.RESULTS The incorporation of a PID controller to assess and regulate blood glucose and insulin levels in individuals with diabetes introduces a sophisticated and dynamic element to diabetes management.The simulation not only allows visualization of how the body responds to different inputs but also offers a valuable tool for predicting and testing the effects of various interventions over time.The PID controller's role in adjusting insulin dosage based on the discrepancy between desired setpoints and actual measurements showcases a proactive strategy for maintaining blood glucose levels within a healthy range.This dynamic feedback loop not only delays the onset of steady-state conditions but also effectively counteracts post-meal spikes in blood glucose.CONCLUSION The WiFi-controlled voltage controller and the PID controller simulation collectively underscore the ongoing efforts to enhance efficiency,safety,and personalized care within the realm of diabetes management.These technological advancements not only contribute to the optimization of insulin delivery systems but also have the potential to reshape our understanding of glucose and insulin dynamics,fostering a new era of precision medicine in the treatment of diabetes.