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Application of the Electrical Resistivity Method in precision agriculture of Coffee Cultivation, in the Kabiri Area, Ícolo e Bengo Township, Luanda, Angola

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International Journal of Geosciences 2024年第9期15卷 720-736页

作者： Gerson Itembo Artur Miguel Nelson Mateta Idvano Costa Miguel Clemente Júlio Kuende Department of Geosciences Instituto Superior Politcnico de Tecnologias e Cincias (ISPTEC) Luanda Angola

The electrical resistivity method is a geophysical tool used to characterize the subsoil and can provide an important information for precision agriculture. The lack of knowledge about agronomic properties of the soil tends to affect the agricultural coffee production system. Therefore, research related to geoelectrical properties of soil such as resistivity for characterization the region of the study for coffee cultivation purposes can improve and optimize the production. This resistivity method allows to investigate the subsurface through different techniques: 1D vertical electrical sounding and electrical imaging. The acquisition of data using these techniques permitted the creation of 2D resistivity cross section from the study area. The geoelectrical data was acquired by using a resistivity meter equipment and was processed in different softwares. The results of the geoelectrical characterization from 1D resistivity model and 2D resistivity electrical sections show that in the study area of Kabiri, there are 8 varieties of geoelectrical layers with different resistivity or conductivity. Near survey in the study area, the lowest resistivity is around 0.322 Ω·m, while the highest is about 92.1 Ω·m. These values illustrated where is possible to plant coffee for suggestion of specific fertilization plan for some area to improve the cultivation.

关键词： Electrical-Resistivity Method precision agriculture Coffee

Deep Learning-Based Model for Detection of Brinjal Weed in the Era of precision agriculture

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Computers, Materials & Continua 2023年第10期77卷 1281-1301页

作者： Jigna Patel Anand Ruparelia Sudeep Tanwar Fayez Alqahtani Amr Tolba Ravi Sharma Maria Simona Raboaca Bogdan Constantin Neagu Department of Computer Science and Engineering Institute of TechnologyNirma UniversityAhmedabad382481India Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Centre for Inter-Disciplinary Research and Innovation University of Petroleum and Energy StudiesDehradun248001India Doctoral School University Politehnica of BucharestBucharest060042Romania National Research and Development Institute for Cryogenic and Isotopic Technologies-ICSI Rm ValceaRamnicu Valcea240050Romania Power Engineering Department Gheorghe Asachi Technical University of IasiIasi700050Romania

The overgrowth of weeds growing along with the primary crop in the fields reduces crop *** solutions like hand weeding are labor-intensive,costly,and time-consuming;farmers have used *** application of herbicide is effective but causes environmental and health ***,precision agriculture(PA)suggests the variable spraying of herbicides so that herbicide chemicals do not affect the primary *** by the gap above,we proposed a Deep Learning(DL)based model for detecting Eggplant(Brinjal)weed in this *** key objective of this study is to detect plant and non-plant(weed)parts from crop *** the help of object detection,the precise location of weeds from images can be *** dataset is collected manually from a private farm in Gandhinagar,Gujarat,*** combined approach of classification and object detection is applied in the proposed *** Convolutional Neural Network(CNN)model is used to classify weed and non-weed images;further DL models are applied for object *** have compared DL models based on accuracy,memory usage,and Intersection over Union(IoU).ResNet-18,YOLOv3,CenterNet,and Faster RCNN are used in the proposed *** outperforms all other models in terms of accuracy,i.e.,88%.Compared to other models,YOLOv3 is the least memory-intensive,utilizing 4.78 GB to evaluate the data.

关键词： precision agriculture Deep Learning brinjal weed detection ResNet-18 YOLOv3 CenterNet Faster RCNN

Biosynthesized metallic nanoparticles as fertilizers:An emerging precision agriculture strategy

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Journal of Integrative agriculture 2022年第5期21卷 1225-1242页

作者： Busiswa NDABA Ashira ROOPNARAIN Haripriya RAMA Malik MAAZA Microbiology and Environmental Biotechnology Research Group Agricultural Research Council-Natural Resources and EngineeringPretoria 0001South Africa Department of Physics College of ScienceEngineering and TechnologyUniversity of South AfricaJohannesburg 1709South Africa Nanosciences African Network Materials Research DepartmentiThemba LABS-National Research FoundationCape Town 7131South Africa

Nanofertilizers increase efficiency and sustainability of agricultural crop *** to their nanosize properties,they have been shown to increase productivity through target delivery or slow release of nutrients,thereby limiting the rate of fertilizer application *** can be synthesized via different approaches ranging from physical and chemical to green(biological)*** green approach is preferable because it makes use of less chemicals,thereby producing less chemical contamination and it is safer in comparison to physicochemical ***,discussion on the use of green synthesized nanoparticles as nanofertilizers is pertinent for a sustainable approach in *** review discusses recent developments and applications of biologically synthesized metallic nanoparticles that can also be used as nanofertilizers,as well as their uptake mechanisms for plant *** concerns of nanoparticle applications in agriculture are also discussed.

关键词： biosynthesis metallic nanoparticles nanofertilizers precision agriculture food security

Computer Vision and Deep Learning-enabled Weed Detection Model for precision agriculture

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Computer Systems Science & Engineering 2023年第3期44卷 2759-2774页

作者： R.Punithavathi A.Delphin Carolina Rani K.R.Sughashinir Chinnarao Kurangit M.Nirmala Hasmath Farhana Thariq Ahmed S.P.Balamurugan Department of Information Technology M.Kumarasamy College of EngineeringKarur6391 I3India Department of Computer Science and Engineering K.Ramakrishnan College of TechnologyTiruchirapalli621112India Department of Electronics and Instrumentation Easwani Engineering CollegeTamil Nadu600089India Pondichery University Puducherry605014India Department of computer science and engineering The Oxford college of EngineeringBangalore560068India Department of Computer Science Engineering Saveetha School of EngineeringSaveetha Institute of Medical and Technical SciencesSaveetha UniversityChennai602105India Department of Computer and Information Science Faculty of ScienceAnnamalai University Chidambaram608002India

Presently,precision agriculture processes like plant disease,crop yield prediction,species recognition,weed detection,and irrigation can be accom-plished by the use of computer vision(CV)*** plays a vital role in influencing crop *** wastage and pollution of farmland's natural atmosphere instigated by full coverage chemical herbicide spraying are *** the proper identification of weeds from crops helps to reduce the usage of herbicide and improve productivity,this study presents a novel computer vision and deep learning based weed detection and classification(CVDL-WDC)model for precision *** proposed CVDL-WDC technique intends to prop-erly discriminate the plants as well as *** proposed CVDL-WDC technique involves two processes namely multiscale Faster RCNN based object detection and optimal extreme learning machine(ELM)based weed *** parameters of the ELM model are optimally adjusted by the use of farmland fertility optimization(FFO)algorithm.A comprehensive simulation analysis of the CVDL-WDC technique against benchmark dataset reported the enhanced out-comes over its recent approaches interms of several measures.

关键词： precision agriculture smart farming weed detection computer vision deep learning

A tree counting algorithm for precision agriculture tasks

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International Journal of Digital Earth 2013年第1期6卷 94-102页

作者： Franco Santoro Eufemia Tarantino Benedetto Figorito Stefania Gualano Anna Maria D’Onghia Centre International de Hautes Etudes Agronomiques Me´diterrane´ennes(CIHEAM) BariItaly School of Environmental Engineering Polytechnic University of BariBariItaly Institute of Intelligent Systems for Automation(ISSIA CNR) BariItaly

This study proposes an automatic procedure for individual fruit tree identification using GeoEye-1 sensor *** on site-specific pruning practices,the morphologic characteristics of tree crowns may generate one or more brightness peaks(tree top)on the *** optimize tree counting and to minimize typical background noises from orchards(*** soil,weeds,and man-made objects),a four-step algorithm was implemented with spatial transforms and functions suitable for spaced stands(asymmetrical smoothing filter,local minimum filter,mask layer,and spatial aggregation operator).System perfor-mance was evaluated through objective criteria,showing consistent results in fast capturing tree position for precision agriculture tasks.

关键词： individual tree identification image processing GeoEye-1 data precision agriculture geospatial data integration remote sensing sensor agri-culture

Design of Machine Learning Based Smart Irrigation System for precision agriculture

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Computers, Materials & Continua 2022年第7期72卷 109-124页

作者： Khalil Ibrahim Mohammad Abuzanouneh Fahd N.Al-Wesabi Amani Abdulrahman Albraikan Mesfer Al Duhayyim M.Al-Shabi Anwer Mustafa Hilal Manar Ahmed Hamza Abu Sarwar Zamani K.Muthulakshmi Department of Information Technology College of ComputerQassim UniversityAl-Bukairiyah52571Saudi Arabia Department of Computer Science King Khalid UniversityMuhayel Aseer62529Saudi Arabia&Faculty of Computer and ITSana’a UniversitySana’a61101Yemen Department of Computer Science College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11564Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Management Information System College of Business and AdministrativeTaibah UniversityMedina42353Saudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAl-Kharj16278Saudi Arabia Department of Electronics and Communication Engineering Dr.N.G.P Institute of TechnologyCoimbatore641048India

agriculture 4.0,as the future of farming technology,comprises numerous key enabling technologies towards sustainable *** use of state-of-the-art technologies,such as the Internet of Things,transform traditional cultivation practices,like irrigation,to modern solutions of precision *** achieve effectivewater resource usage and automated irrigation in precision agriculture,recent technologies like machine learning(ML)can be *** this motivation,this paper design an IoT andML enabled smart irrigation system(IoTML-SIS)for precision *** proposed IoTML-SIS technique allows to sense the parameters of the farmland and make appropriate decisions for *** proposed IoTML-SIS model involves different IoT based sensors for soil moisture,humidity,temperature sensor,and ***,the sensed data are transmitted to the cloud server for processing and decision ***,artificial algae algorithm(AAA)with least squares-support vector machine(LS-SVM)model is employed for the classification process to determine the need for ***,the AAA is applied to optimally tune the parameters involved in the LS-SVM model,and thereby the classification efficiency is significantly *** performance validation of the proposed IoTML-SIS technique ensured better performance over the compared methods with the maximum accuracy of 0.975.

关键词： Automatic irrigation precision agriculture smart farming machine learning cloud computing decision making internet of things

Artificial Intelligence Enabled Apple Leaf Disease Classification for precision agriculture

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Computers, Materials & Continua 2022年第3期70卷 6223-6238页

作者： Fahd N.Al-Wesabi Amani Abdulrahman Albraikan Anwer Mustafa Hilal Majdy M.Eltahir Manar Ahmed Hamza Abu Sarwar Zamani Department of Computer Science King Khalid UniversityMuhayel AseerSaudi Arabia Faculty of Computer and IT Sana’a UniversityYemen Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversitySaudi Arabia Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlkharjSaudi Arabia

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

关键词： Artificial intelligence apple leaf plant disease precision agriculture deep learning data augmentation

Clustered Wireless Sensor Network in precision agriculture via Graph Theory

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Intelligent Automation & Soft Computing 2023年第5期36卷 1435-1449页

作者： L.R.Bindu P.Titus D.Dhanya Department of Science and Humanities Mar Ephraem College of Engineering and TechnologyTamil Nadu629171India. Department of Science and Humanities University College of Engineering NagercoilAnna University Constituent CollegeKonamNagercoil629004India Department of Computer Science and Engineering Mar Ephraem College of Engineering and TechnologyTamil Nadu629171India

Food security and sustainable development is making a mandatory move in the entire human *** attainment of this goal requires man to strive for a highly advanced state in theﬁeld of agriculture so that he can produce crops with a minimum amount of water and *** though our agricultural methodol-ogies have undergone a series of metamorphoses in the process of a present smart-agricultural system,a long way is ahead to attain a system that is precise and accurate for the optimum yield and proﬁ*** such a futuristic method of cultivation,this paper proposes a novel method for monitoring the efﬁcientﬂow of a small quantity of water through the conventional irrigation system in cultiva-tion using Clustered Wireless Sensor Networks(CWSN).The performance measure is simulated the creation of edge-ﬁxed geodetic clusters using Mat lab’s Cup-carbon tool in order to evaluate the suggested irrigation process model’s ***ﬁndings of blocks 1 and 2 are *** signal takes just a little amount of energy to communicate,according to the *** is feasible to save energy while maintaining uninterrupted communication between nodes and cluster ***,the need for proper placement of a dynamic control station in WSN still exists for maintaining connectivity and for improving the lifetime fault tolerance of *** on the minimum edgeﬁxed geodetic sets of the connected graph,this paper offers an innovative method for optimizing the placement of control *** edge-ﬁxed geodetic cluster makes the network fast,efﬁcient and ***,it also solves routing and congestion problems.

关键词： Wireless sensor networks edgeﬁxed geodetic set agriculture cluster control station precision agriculture

DESIGN OF FARMLAND GIS FOR precision agriculture

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Chinese Geographical Science 2003年第1期13卷 20-24页

作者： AN Kai, XIE Gao-di, LENG Yun-fa, XIAO Yu (Institute of Geographical Sciences and Natural Resources Research, the Chinese Academy of Sciences, Beijing 100101, P. R. China) Institute of Geographical Sciences and Natural Resources Research the Chinese Academy of Sciences Beijing P. R. China

precision agriculture, also known as precision Farming, or Prescription Farming, is a modern agriculture technology system, which brings ' precision' into agriculture system. All concepts of precision agriculture are established on the collection and management of variable cropland information. As the tool of collecting, managing and analyzing spatial data, GIS is the key technology of integrated precision agriculture system. This article puts forward the concept of Farmland GIS and designs Farmland GIS into five modules, and specifies the functions of the each module, which builds the foundation for practical development of the software. The study and development of Farmland GIS will propel the spreading of precision agriculture technology in China.

关键词： precision agriculture farmland GIS site-specific crop management prescription map