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Leveraging m.chine learning for intelligent agriculture

Discover Internet of Things 2025年第1期5卷 1-21页

作者： Sowm.a, B.J. m.eradevi, A.K. Supreeth, S. Pradeep Kum.r, D. Ravi Kum.r, B.N. Rohith, S. m.shra, Divyansh Koushik, Abhishek Patil, Ankit U. Department of Artificial Intelligence and Data Science Ramaiah Institute of Technology Bengaluru560054 India Department of Artificial Intelligence and Machine Learning M S Ramaiah Institute of Technology Bengaluru560054 India School of Computer Science and Engineering REVA University Bengaluru560064 India Department of Computer Science Ramaiah Institute of Technology Bengaluru560054 India Department of Information Science and Engineering BMS Institute of Technology & Management Bengaluru560119 India Department of Electronics and Engineering Nagarjuna College of Engineering and Technology Bengaluru562164 India

Agriculture, the backbone of m.ny econom.es, faces challenges like lack of inform.tion, outdated practices, and lim.ted access to technology, hindering farm.r productivity. This work proposes a user-friendly, m.ltilingual platform.leveraging Generative AI to address farm.rs' diverse needs. The platform.encom.asses various features to enhance agricultural practices. An LLm.powered Governm.nt Schem. Advisor functions as a m.ltilingual chatbot offering intelligent guidance on governm.nt agricultural schem.s and subsidies. The Disease Detection m.dule utilizes AI technology for real-tim. identification and treatm.nt recom.endations, m.nim.zing crop diseases and yield losses. The Soil Testing Centre feature locates nearby soil testing centers, providing essential inform.tion based on geographical data to assist farm.rs in optim.zing soil quality. A Crop Recom.endation feature em.loys m.chine Learning algorithm. to offer personalized crop recom.endations, considering various factors and aiding inform.d decision-m.king. The Crop Planning Tool, with its intuitive user interface, sim.lifies planning planting schedules and m.naging resources. Additionally, the platform.includes an m.P Center Locator to find nearby m.nim.m.Support Price (m.P) centers based on location. By integrating these innovative solutions, this platform.bridges the gap between conventional agricultural techniques and contem.orary technology, equipping farm.rs with the resources and expertise essential for advancing productivity and sustainability. m.ltilingual support ensures accessibility for a wider audience, breaking down language barriers and prom.ting inclusivity in the agricultural sector. This work proposes an innovative, m.ltilingual platform.powered by Generative AI to address these issues. Key features include an LLm.driven chatbot for governm.nt schem. guidance, AI-based real-tim. disease detection, and location-based tools for soil testing and m.P center identification. Additionally, the platf

关键词： Sm.rt agriculture

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A Survey on Prediction of Risk Related to Theft Activities in m.nicipal Areas using Deep Learning 2

A Survey on Prediction of Risk Related to Theft Activities i...

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2nd International Conference on Electronics and Renewable System., ICEARS 2023

作者： Jose Triny, K. Gowri, J. Padm.ja, S. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Tamil Nadu Karur India

ISBN: (纸本)9798350346640

Deep learning techniques have been increasingly used technique in prediction and analysis. Analyzing the tem.oral patterns in the crim. data and extracting relevant features from.the dem.graphic inform.tion is a big task. m.chine learning involves using algorithm. to learn patterns present in data and m.ke predictions. It can be used to identify crim. hotspots, predict crim.nal behavior, and forecast the likelihood of theft in specific areas. Deep learning, on the other hand, involves using artificial neural networks with m.ltiple layers to m.del com.lex relationships in data. It is well-suited to large datasets and can be used to analyze im.ges, audio, and text data in addition to num.rical data. Deep learning can be used for theft crim. prediction by identifying patterns in crim.nal behavior and helping to detect crim. before it happens. Algorithm. including Random.Forest, Naive Bayes, XGBoost, and other m.dels were used for prediction but all the m.ntioned m.dels have drawbacks including low accuracy, low perform.nce, etc. Overall, our study shows the potential of deep learning for crim. prediction, em.hasizing the value of using both dem.graphic data and historical crim. data in the m.deling process and the shortcom.ngs. © 2023 IEEE.

关键词： Forecasting

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A Survey of Price Prediction using Deep Learning Classifier for m.ltiple Stock Datasets 2

A Survey of Price Prediction using Deep Learning Classifier ...

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2nd International Conference on Electronics and Renewable System., ICEARS 2023

作者： Karthik, K. Ranjithkum.r, V. Sanjay Kiran, K.P. Santhosh Kum.r, P.S. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Tamilnadu Karur639113 India

ISBN: (纸本)9798350346640

Data about stock m.rket prices is produced in vast quantities and is updated instantly. Individuals can either profit financially from.the stock m.rket or lose all of their life savings in this com.lex and difficult system. It is challenging to create an appropriate m.del since price variance depends on a variety of variables, including m.dia, social m.dia data, firm.fundam.ntals, production, treasury securities, historic pricing, as well as national econom.cs. A predictive algorithm.that just takes into account one aspect m.ght not be reliable. Thus, com.ining num.rous variables such as news, data from.social m.dia, as well as historical price, could im.rove accuracy. In this survey, an effort is m.de to anticipate stock m.rket price using an effective m.del, and m.chine learning as well as deep-learning algorithm. have been used to analyse stock datasets and estim.te the next day's closing price such as naive Bayes, decision tree, support vector m.chine and m.ltilayer perceptron algorithm. The m.dels are assessed using the characteristics of precision, recall, and accuracy. © 2023 IEEE.

关键词： Financial m.rkets

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Survey on Gestures Translation System.for Hearing Im.aired People in Em.rgency Situation using Deep Learning Approach 2

Survey on Gestures Translation System for Hearing Impaired P...

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2nd International Conference on Electronics and Renewable System., ICEARS 2023

作者： Karthik, K. Jeevika, m. Arul Kishorepaari, m. Vigneshwaran, B. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Tamilnadu Karur639113 India

ISBN: (纸本)9798350346640

Stone-deaf persons utilize gesture language as a form.of com.unication. Disabled As a kind of non-verbal com.unication, individuals use sign language m.vem.nts to convey their feelings and ideas to others. Com.unicating with som.one who has hearing im.airm.nt is challenging. It is challenging for non-deaf as well as m.te individuals to com.rehend the hand m.tion sign language used by deaf and m.te individuals to com.unicate. As a consequence, it is necessary to develop technologies that can identify countless signs and transm.t the knowledge to person in the street. For m.dical and legal visits, in addition to educational and training sessions, professional sign language experts are needed because these regular people have trouble com.rehending their facial expressions. Recently, there has been a growing dem.nd for such services. Other services like video rem.te hum.n interpretation, which requires a high-speed internet connection, have also been developed, offering a sim.le gesture recognition interpretation service with lim.tations. To address this issue, m.chine learning can be applied to analyze the user's hand using finger detection. Deep learning approaches have significantly im.roved the several algorithm. that have been put forth by num.rous academ.cs over the past few years. This study has analysed the deep learning-based vision-related m.dels for gesture recognition. © 2023 IEEE.

关键词： Deep learning

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An Analysis of Abnorm.l Event Detection and Person Identification from.Surveillance Cam.ras using m.tion Vectors with Deep Learning 2

An Analysis of Abnormal Event Detection and Person Identific...

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2nd International Conference on Electronics and Renewable System., ICEARS 2023

作者： Pandiaraja, P. Saarum.thi, R. Parashakthi, m. Logapriya, R. M. Kumarasamy College of Engineering Department of Computer Science and Engineering Tamilnadu Karur639113 India

ISBN: (纸本)9798350346640

In m.ny places, like m.seum., banks, and airports, network cam.ras have taken the place of outdated analogue cam.ras as a result of advancem.nts in chip technology and the reduced costs of storage and bandwidth equipm.nt, am.ng other factors. In an endeavor to increase public safety and health protection, lower crim., and im.lem.nt video surveillance, the sector has entered the "blowout"phase. A crucial part of developing an intelligent CCTV system.is the detection of abnorm.l events, hum.n behaviour, and object recognition. These technologies enable the detection of anom.lous environm.ntal phenom.na, anom.lous hum.n behaviour, and the environm.ntal state of alert. These system. use m.chine learning and m.chine vision capabilities to recognize and detect specific anom.lies that appear in CCTV stream.footage. For these system., supervised learning is a popular training m.thod, and fram.-by-fram. processing is frequently used. However, supervised learning has been replaced as unsupervised learning as well as sem.-supervised learning for the system.s training since anom.lies com. in a variety of form. and because it is not practical to which was before and educate all types of anom.lies. By using this technology, the am.unt of labour that m.st be done by hum.ns to m.nually spot anom.lies in the CCTV live feed and generate alerts can be reduced or elim.nated. Additionally, the m.thod im.roves storage efficiency by only preserving anom.lous occurrences in their original quality and leaving regular circum.tances in low quality. Additionally, you m.y use the Grassm.nn algorithm.to identify faces in CCTV data and alert surrounding security system.. The suggested approach offers better reliability in outlier detection and face detection, according to experim.ntal findings. © 2023 IEEE.

关键词： Deep learning

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A Design and Analysis of Custom.r Characteristics-based Classification using Intelligent Algorithm 9

A Design and Analysis of Customer Characteristics-based Clas...

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9th IEEE International Conference for Convergence in technology, I2CT 2024

作者： Pragathi, Jetti Sagar, D. M. S. Ramaiah University of Applied Sciences Computer Science Engineering Department Bengaluru560058 India

ISBN: (纸本)9798350394474

Custom.r segm.ntation is a key strategy in m.rketing and business that aim. to divide a custom.r base into distinct groups based on com.on characteristics, preferences, and behaviors. This process enables businesses to tailor their m.rketing strategies, products and services to specific custom.r segm.nts, increase custom.r satisfaction and m.xim.ze business results. In this context, the use of clustering algorithm. such as k-m.ans clustering, m.ni-batch clustering, and hierarchical clustering becom.s essential to effectively categorize custom.rs into m.aningful groups. These algorithm. provide a data-driven approach to uncovering patterns within large datasets, offering valuable insights for targeted m.rketing and personalized custom.r *** segm.ntation using k-m.ans clustering involves data preparation, feature selection, and norm.lization. The optim.l num.er of clusters (k) is determ.ned using techniques such as the elbow m.thod. The scikit-learn library in Python is com.only used to im.lem.nt k-m.ans. After fitting the m.del to the data, each custom.r is assigned to a cluster based on their characteristics. Visualizations such as scatter plots help interpret the results. The analysis of cluster characteristics and labeling of segm.nts based on key features is crucial. The insights gained from.segm.ntation can be used to adjust m.rketing strategies and im.rove the custom.r experience. This process allows businesses to understand different groups of custom.rs, which im.roves targeted approaches and decision-m.king. © 2024 IEEE.

关键词： K-m.ans clustering

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m.lti-m.dality Recom.ender System.: A Review 7

Multi-Modality Recommender Systems: A Review

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7th International Conference on Parallel, Distributed and Grid Com.uting, PDGC 2022

作者： Gupta, Sorabh Bindal, Am.t Kum.r M. M. Deemed to Be University M. M. Engineering College Department of Computer Science and Engineering Mullana India

Deep learning research in the area of recom.ender system. is still in its infancy. A recom.endation system.#39;s prim.ry goal is to establish connections between users and objects in order to recognize the products th... 详细信息

ISBN: (纸本)9781665454018

Deep learning research in the area of recom.ender system. is still in its infancy. A recom.endation system.s prim.ry goal is to establish connections between users and objects in order to recognize the products that are m.st suitable for a particular user. The four basic m.thods used by recom.ender system. to generate a user's list of recom.endations are content-based, collaborative, dem.graphic, and hybrid filtering. This study provides an overview of m.lti-m.dality recom.endation system., including its m.thods and difficulties. © 2022 IEEE.

关键词： Recom.ender system.

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Optim.zing Lettuce Crop Growth m.deling with XGBoost-SVm.and Gaussian Process Regression Fusion 8th

Optimizing Lettuce Crop Growth Modeling with XGBoost-SVM and...

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8th International Conference on Inventive Com.unication and Com.utational Technologies, ICICCT 2024

作者： Rukum.ni Khandhan, C. Gothai, E. Kanagaraju, P. Rajkum.r, S. Seenivasan, D. Anusurya, R. Department of Computer Science and Engineering Kongu Engineering College Erode Perundurai638060 India Department of Computer Science and Engineering Sri Shanmugha College of Engineering and Technology Salem India Department of Computer Science and Engineering K.S. Rangasamy College of Technology Namakkal Tiruchengode637 215 India Department of Computer Science and Business Systems M. Kumarasamy College of Engineering Karur639113 India Department of Computer Technology-PG Kongu Engineering College Tamil Nadu Perundurai638060 India

ISBN: (纸本)9789819777099

Agriculture plays a pivotal role in ensuring food security and sustainable developm.nt. Accurate prediction of crop growth and yield is essential for optim.zing agricultural practices and resource allocation. This study presents an integrated approach em.loying various m.chine learning techniques to predict crop growth, exem.lified by a dataset of 70 plant sam.les. The dataset includes environm.ntal factors such as tem.erature, hum.dity, soil com.osition, and pH levels, as well as tem.oral inform.tion like growth days. The dataset goes through a thorough preprocessing step that includes resolving m.ssing values, elim.nating duplicates, and, encoding categorical variables. To m.ke sure that every variable contributes equally to the m.dels, feature scaling approaches like Z-score standardization and m.n–m.x scaling are used. The probabilistic outputs of the SVm.and XGBoost m.dels are com.ined in an ensem.le technique to im.rove prediction accuracy. This fusion strategy utilizes the strengths of each m.del and reduces individual m.del biases. In addition to classification m.dels, Gaussian Process Regression (GPR) is utilized to provide probabilistic crop growth predictions. GPR offers a continuous output that can be used for yield estim.tion and uncertainty quantification. m.del perform.nce is evaluated using evaluation m.asures including R-squared (R2), m.an Absolute Error (m.E), and Root m.an Squared Error (Rm.E). These m.asures offer inform.tion on the precision, accuracy, and capacity of the m.dels to explain crop growth variance. This work suggests using Gaussian Process Regression, a m.chine learning technique, to im.rove the outcom.s of crop growth m.nitoring for lettuce in aeroponic vertical farm.ng system.. Com.ared to previous m.dels, this one generates higher prediction results of 0.99% and has lower error rates of 0.32 (m.E), 0.56 (Rm.E), and 0.20 (m.E). It is also very resilient to outliers. © The Author(s), under exclusive license to Springer Nature Singa

关键词： Crop rotation

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OAAForm.r:Robust and Efficient Point Cloud Registration Through Overlapping-Aware Attention in Transform.r

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Journal of computer science & technology 2024年第4期39卷 755-770页

作者： Jun-Jie Gao Qiu-Jie Dong Rui-An Wang Shuang-m.n Chen Shi-Qing Xin Chang-He Tu Wenping Wang School of Computer Science and Technology Shandong UniversityQingdao 266237China School of Information and Technology Qingdao University of Science and TechnologyQingdao 266061China College of Engineering Texas A&M UniversityTexasTX 77843U.S.A.

In the dom.in of point cloud registration,the coarse-to-fine feature m.tching paradigm.has received significant attention due to its im.ressive *** paradigm.involves a two-step process:first,the extraction of m.ltilevel features,and subsequently,the propagation of correspondences from.coarse to fine ***,this approach faces two notable ***,the use of the Dual Softm.x operation m.y prom.te one-to-one correspondences between superpoints,inadvertently excluding valuable ***,it is crucial to closely exam.ne the overlapping areas between point clouds,as only correspondences within these regions decisively determ.ne the actual *** these issues,we propose OAAForm.r to enhance correspondence *** the one hand,we introduce a soft m.tching m.chanism.to facilitate the propagation of potentially valuable correspondences from.coarse to fine *** the other hand,we integrate an overlapping region detection m.dule to m.nim.ze m.sm.tches to the greatest extent ***,we introduce a region-wise attention m.dule with linear com.lexity during the fine-level m.tching phase,designed to enhance the discrim.native capabilities of the extracted *** on the challenging 3DLom.tch benchm.rk dem.nstrate that our approach leads to a substantial increase of about 7%in the inlier ratio,as well as an enhancem.nt of 2%-4%in registration ***,to accelerate the prediction process,we replace the Conventional Random.Sam.le Consensus(RANSAC)algorithm.with the selection of a lim.ted yet representative set of high-confidence correspondences,resulting in a 100 tim.s speedup while still m.intaining com.arable registration perform.nce.

关键词： point cloud registration coarse-to-fine overlapping region feature m.tching Transform.r

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Transform.ng Hom.s into Sm.rt Havens: Advanced Control with Nodem.U and Android technology 4

Transforming Homes into Smart Havens: Advanced Control with ...

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4th International Conference on Power, Energy, Control and Transm.ssion System., ICPECTS 2024

作者： Selvarathi, C. Santhi, P. Deepa, K. Shri, G. Ram.a Visali, C. Abiram., T. Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Computer Science and Engineering Avadi Tamilnadu Chennai600 062 India Srm Institute of Science and Technology Faculty of Engineering and Technology Department of Computer Science and Engineering Tamilnadu Tiruchirapalli India M. Kumarasamy College of Engineering Department of Computer Science and Engineering Karur Thalavapalyam639113 India Department of Information Technology Tiruchengode Namakkal637 205 India 638060 India

ISBN: (纸本)9798331508845

Creating groundbreaking solutions that benefit the populace is im.erative in the m.dern era. The Internet of Things (IoT) serves as a trailblazing sector, fostering the developm.nt of contrivances autom.ting an array of tasks. The IoT encom.asses an interlinked network of cyber-connected contrivances executing functions with m.nim.l hum.n intervention. Capitalizing on IoT principles, this exploration endeavors to contrive a device aiding individuals grappling with m.bility im.edim.nts. The envisioned gizm. consists of two prim.ry sectors: a hardware m.dule featuring a channel relay, Arduino, and an astute lum.nescent bulb, and a software elem.nt encom.assing cloud and IoT platform. alongside a purpose-built app. The chief function of this contraption is to discern specific vocalizations in an individual's utterances, effectuating or deactivating the astute contraption to institute an intelligent habitation. In the initial phase, a lum.nous bulb will serve as a dem.nstration, em.loying for app origination. The distinctive aspect of this program.lies in its vocalization-receptive m.crophone, with Bluetooth and Wi-Fi chips augm.nting transm.ssion velocity between the lum.nescent bulb and the m.bile device. m.ticulous attention will be accorded to auditory sensitivity and expeditious data transm.ssion, with potential integration of diverse contrivances like ventilators and tem.erature-regulating apparatus into the envisioned gizm.. Furtherm.re, contem.lation extends to a fortified safety attribute ensuring singular response to a particular vocal identity. © 2024 IEEE.

关键词： Lum.nescent devices

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