|
|
|
题名
|
作者
|
年代
|
出处
|
被引量
|
| 1 | Applications of electronic nose (e-nose) and electronic tongue (e-tongue) in food quality-related properties determination: A review显示文摘Background:An e-nose or an e-tongue is a group of gas sensors or chemical sensors that simulate human nose or human tongue.Both e-nose and e-tongue have showngreat promise and utility in improving assessments of food quality characteristics compared with traditional detection methods.Scope and approach:This review summarizes the application of e-nose and e-tongue in determining the quality-related properties of foods.The working principles,applications,and limitations of the sensors employed by electronic noses and electronic tongueswere introduced and compared.Widelyemployed pattern recognition algorithms,including artificial neural network(ANN),convolutional neural network(CNN),principal component analysis(PCA),partial least square regression(PLS),and support vector machine(SVM),were introduced and compared in this review.Key findings and conclusions:Overall,e-nose or e-tongue combining pattern recognition algorithms are very powerful analytical tools,which are relatively low-cost,rapid,and accurate.E-nose and e-tongue are also suitable for both in-line and off-line measurements,which are very useful in monitoring food processing and detecting the end product quality.The user of e-nose and e-tongue need to strictly control sample preparation,sampling,and data processing. | Juzhong Tan Jie Xu | 2020 | Artificial Intelligence in Agriculture2020，，1： | 26 |
| 2 | A computer vision system for defect discrimination and grading in tomatoes using machine learning and image processing显示文摘With large-scale production and the need for high-quality tomatoes to meet consumer and market standards criteria,have led to the need for an inline,accurate,reliable grading system during the post-harvest process.This study introduced a tomato grading machine vision system based on RGB images.The proposed system performed calyx and stalk scar detection at an average accuracy of 0.9515 for both defected and healthy tomatoes by histogramthresholding based on themean g-r value of these regions of interest.Defected regionswere detected by an RBF-SVMclassifier using the LAB color-space pixel values.Themodel achieved an overall accuracy of 0.989 upon validation.Four grading categories recognitionmodelswere developed based on color and texture features.The RBF-SVMoutperformed all the explored modelswith the highest accuracy of 0.9709 for healthy and defected category.However,the grading accuracy decreased as the number of grading categories increased.A combination of color and texture features achieved the highest accuracy in all the grading categories in image features evaluation.This proposed system can be used as an inline tomato sorting tool to ensure that quality standards are adhered to and maintained. | David Ireri Eisa Belal Cedric Okinda Nelson Makange Changying Ji | 2019 | Artificial Intelligence in Agriculture2019，，2： | 10 |
| 3 | Fusion of machine vision technology and AlexNet-CNNs deep learning network for the detection of postharvest apple pesticide residues显示文摘Pesticide residue is an important factor that affects food safety.In order to achieve effective detection of pesticide residues in apples,a machine-vision-based segmentation algorithm and hyperspectral techniques were used to segment the foreground and background regions of the apple image.By calculating the roundness value and extracting the region with the highest roundness value in the connected region,a region of interest(ROI)maskwas created for the apple.Four pesticides(chlorpyrifos,carbendazimand two mixed pesticides)and an inactive control were used at the same concentration of 100 ppm(except for the control group),and the hyperspectral region of the corresponding sample image was extracted by obtaining the different types of pesticide residues in the ROI masks.To increase the diversity of the samples and to expand the dataset,Gaussianwhite noise with a varying signal-to-noise ratio was added to each of the hyperspectral images of the apple.The number of samples was increased from four types of 12 samples to four types of 72 samples,giving 4608 hyperspectral data images in each category.The structure and parameters of a convolutional neural network(CNN)were determined using theoretical analysis and experimental verification.All the extracted hyperspectral images of apples were normalized to 227×227×3 pixels as the input of the CNN network for pesticide residue detection.There were 18,432 sample data of four types for 72 samples.Of these,12,288 images were selected using a bootstrap sampling method as the training set,and 6144 as the test set,with no overlap.The test results showthatwhen the number of training epochswas 10,the accuracy of the test set detectionwas 99.09%,and the detection accuracy of the single-band average imagewas 95.35%.A comparison with traditional k-nearest neighbor(KNN)and support vectormachine classification algorithms showed that the detection accuracy for KNNwas 43.75%and the average time was 0.7645 s.These results demonstrate that our method is a small-sample,noncontact,fast,effective and low-cost technique that can provide effective pesticide residue detection in postharvest apples. | Bo Jiang Jinrong He Shuqin Yang Hongfei Fu Tong Li Huaibo Song Dongjian He | 2019 | Artificial Intelligence in Agriculture2019，，1： | 8 |
| 4 | Recent advances in emerging techniques for non-destructive detection of seed viability: A review显示文摘Over the past decades,imaging and spectroscopy techniques have been developed rapidly with widespread applications in non-destructive agro-food quality determination.Seeds are one of themost fundamental elements of agriculture and forestry.Seed viability is of great significance in seed quality characteristics reflecting potential seed germination,and there is a great need for a quick and effective method to determine the germination condition and viability of seeds prior to cultivate,sale and plant.Some researches based on spectra and/or image processing and analysis have been explored in terms of the external and internal quality of a variety of seeds.Many attempts have been made in image segmentation and spectra correction methods to predict seed quality using various traditional and novel methods.This review focuses on the comparative introduction,development and applications of emerging techniques in the analysis of seed viability,in particular,near infrared spectroscopy,hyperspectral and multispectral imaging,Raman spectroscopy,infrared thermography,and soft X-ray imaging methods.The basic theories,principle components,relative chemometric processing,analytical methods and prediction accuracies are reported and compared.Additionally,on the foundation of the observed applications,the technical challenges and future outlook for these emerging techniques are also discussed. | Yu Xia Yunfei Xu Jiangbo Li Chi Zhang Shuxiang Fan | 2019 | Artificial Intelligence in Agriculture2019，，1： | 7 |
| 5 | Artificial cognition for applications in smart agriculture: A comprehensive review显示文摘Agriculture contributes to 6.4%of the entire world's economic production.In at least nine countries of the world,agriculture is the dominant sector of the economy.Agriculture not only provides the fuel for billions of people but also employment opportunities to a large number of people.The agricultural industries are seeking innovative approaches for improving crop yielding because of unpredictable climatic changes,the rapid increase in population growth and food security concerns.Thus,artificial intelligence in agriculture also called“Agriculture Intelligence”is progressively emerging as a part of the industry's technological revolution.The aim of this paper is to review various applications of agriculture intelligence such as precision farming,disease detection,and crop phenotyping with the help of numerous tools such as machine learning,deep learning,image processing,artificial neural network,deep learning,convolution neural network,Wireless Sensor Network(WSN)technology,wireless communication,robotics,Internet of Things(IoT),different genetic algorithms,fuzzy logic and computer vision to name a few.With the help of these technologies,the use of the colossal volume of chemicals can be used reduced,which would result in reduced expenditure improved soil fertility along with elevated productivity. | Misbah Pathan Nivedita Patel Hiteshri Yagnik Manan Shah | 2020 | Artificial Intelligence in Agriculture2020，，1： | 6 |
| 6 | A comprehensive review on automation in agriculture using artificial intelligence显示文摘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. | Kirtan Jha Aalap Doshi Poojan Patel Manan Shah | 2019 | Artificial Intelligence in Agriculture2019，，2： | 6 |
| 7 | Study on body temperature detection of pig based on infrared technology: A review显示文摘Body temperature is an important physiological indicator in the whole process of pig breeding.Temperature measurement is also an effective means to assist in disease diagnosis and pig health monitoring.In the conventional method of measuring body temperature,a mercury column is used to obtain the rectal temperature.The operation of thismethod is complicated and requires a large amount of labor.This kind of temperature measurement method is contact and canmake the pig stressed,which is disadvantageous for the healthy growth of pigs.Therefore,rectal temperaturemeasurement no longer meets the needs of the large-scale pig industry in China's welfare agriculture.In recent years,the emerging pig body temperature detection technologies are electronic temperaturemeasurement technology,infrared temperature measurement technology and so on.Infrared temperature measurement technology has been the main means of measuring the temperature of pig body surface with its advantages of non-contact,long distance and real-time.At present,infrared temperature measurement technology and infrared image processing technology used in pig breeding are still in the exploration stage.Nowadays,the infrared temperature measurement equipment based on point-by-point analysis represented by infrared thermometer and temperature measurement equipment based on full-field analysis represented by infrared thermal imager have been applied to pig breeding industry.These types of temperaturemeasurement are more in line with the needs of the pig breeding industry to transform and upgrade to the automation,in line with the development concept of welfare farming and smart agriculture,and its development prospects are very impressive. | Zaiqin Zhang Hang Zhang Tonghai Liu | 2019 | Artificial Intelligence in Agriculture2019，，1： | 6 |
| 8 | Real-time hyperspectral imaging for the in-field estimation of strawberry ripeness with deep learning显示文摘Strawberry is one of the popular fruits with numerous nutrients.The ripeness of this fruits was estimated using the hyperspectral imaging(HSI)system in field and laboratory conditions in this study.Strawberry at early ripe and ripe stageswere collected HSI data,coveredwavelength ranges from370 to 1015 nm.Spectral featurewavelengths were selected using the sequential feature selection(SFS)algorithm.Two wavelengths selected for field(530 and 604 nm)and laboratory(528 and 715 nm)samples,respectively.Then,reliability of such spectral featureswas validated based on support vectormachine(SVM)classifier.Performance of SVMclassification models had good resultswith receiver operating characteristic values for samples under both field and laboratory conditions higher than 0.95.Meanwhile,the spatial feature images were extracted from the spectral feature wavelength and the first three principal components for laboratory samples.Pretrained AlexNet convolutional neural network(CNN)was used to classify the early ripe and ripe strawberry samples,which obtained the accuracy of 98.6%for test dataset.The above results indicated real-time HSI system was promising for estimating strawberry ripeness under field and laboratory conditions,which could be a potential application technique for evaluating the harvesting time management for farmers and producers. | Zongmei Gao Yuanyuan Shao Guantao Xuan Yongxian Wang Yi Liu Xiang Han | 2020 | Artificial Intelligence in Agriculture2020，，1： | 6 |
| 9 | Sunflower leaf diseases detection using image segmentation based on particle swarm optimization显示文摘Sun flower(Helianthus annuus L.)is one of the important oil seed crops and potentially fit in agricultural system and oil production sector of India.Sunflower crop gets damaged by the impact of various diseases,insects and nematodes resulting in wide range of loss in production.Disease detection is possible through naked eye observation,but this method is unsuccessful when one has to monitor the large farms.As a solution to this problem,we developed and present a system for segmentation and classification of Sunflower leaf images.This research paper presents surveys conducted on different diseases classification techniques that can be used for sunflower leaf disease detection.Segmentation of Sunflower leaf images,which is an important aspect for disease classification,is done by using Particle swarm optimization algorithm.Satisfactory results have been given by the experiments done on leaf images.The average accuracy of classification of proposed algorithm is 98.0%compared to 97.6 and 92.7%reported in state-of-the-art methods. | Vijai Singh | 2019 | Artificial Intelligence in Agriculture2019，，3： | 5 |
| 10 | ChatGPT as an Educational Tool: Opportunities, Challenges, and Recommendations for Communication, Business Writing, and Composition Courses显示文摘This empirical study examines ChatGPT as an educational and learning tool.It investigates the opportunities and challenges that ChatGPT provides to the students and instructors of communication,business writing,and composition courses.It also strives to provide recommendations.After conducting 30 theory-based and application-based ChatGPT tests,it is found that ChatGPT has the potential of replacing search engines as it provides accurate and reliable input to students.For opportunities,the study found that ChatGPT provides a platform for students to seek answers to theory-based questions and generate ideas for application-based questions.It also provides a platform for instructors to integrate technology in classrooms and conduct workshops to discuss and evaluate generated responses.For challenges,the study found that ChatGPT,if unethically used by students,may lead to human unintelligence and unlearning.This may also present a challenge to instructors as the use of ChatGPT negatively affects their ability to differentiate between meticulous and automation-dependent students,on the one hand,and measure the achievement of learning outcomes,on the other hand.Based on the outcome of the analysis,this study recommends communication,business writing,and composition instructors to(1)refrain from making theory-based questions as take-home assessments,(2)provide communication and business writing students with detailed case-based and scenario-based assessment tasks that call for personalized answers utilizing critical,creative,and imaginative thinking incorporating lectures and textbook material,(3)enforce submitting all take-home assessments on plagiarism detection software,especially for composition courses,and(4)integrate ChatGPT generated responses in classes as examples to be discussed in workshops.Remarkably,this study found that ChatGPT skillfully paraphrases regenerated responses in a way that is not detected by similarity detection software.To maintain their effectiveness,similarity detection software providers need to upgrade their software to avoid such incidents from slipping unnoticed. | Mohammad Awad AlAfnan Samira Dishari Marina Jovic Koba Lomidze | 2023 | Journal of Artificial Intelligence and Technology2023，3，2： | 5 |
| 11 | Nondestructive determining the soluble solids content of citrus using near infrared transmittance technology combined with the variable selection algorithm显示文摘Nondestructive determination the internal quality of thick-skin fruits has always been a challenge.In order to investigate the prediction ability of full transmittance mode on the soluble solid content(SSC)in thick-skin fruits,the full transmittance spectra of citrus were collected using a visible/near infrared(Vis/NIR)portable spectrograph(550–1100 nm).Three obvious absorption peakswere found at 710,810 and 915 nmin the original spectra curve.Four spectral preprocessing methods including Smoothing,multiplicative scatter correction(MSC),standard normal variate(SNV)and first derivativewere employed to improve the quality of the original spectra.Subsequently,the effective wavelengths of SSC were selected from the original and pretreated spectra with the algorithms of successive projections algorithm(SPA),competitive adaptive reweighted sampling(CARS)and genetic algorithm(GA).Finally,the prediction models of SSC were established based on the full wavelengths and effectivewavelengths.Results showed that SPA performed the best performance on eliminating the useless information variable and optimizing the number of effective variables.The optimal predictionmodel was established based on 10 characteristic variables selected from the spectra pretreated by SNV with the algorithmof SPA,with the correlation coefficient,root mean square error,and residual predictive deviation for prediction set being 0.9165,0.5684°Brix and 2.5120,respectively.Overall,the full transmittance mode was feasible to predict the internal quality of thick-skin fruits,like citrus.Additionally,the combination of spectral preprocessing with a variable selection algorithmwas effective for developing the reliable predictionmodel.The conclusions of this study also provide an alternative method for fast and real-time detection of the internal quality of thick-skin fruits using Vis/NIR spectroscopy. | Xi Tian Jiangbo Li Shilai Yi Guoqiang Jin Xiaoying Qiu Yongjie Li | 2020 | Artificial Intelligence in Agriculture2020，，1： | 5 |
| 12 | Development of a metering mechanism with serial robotic arm for handling paper pot seedlings in a vegetable transplanter显示文摘This paper describes the development of an automated metering mechanism for vegetable transplanter.It consisted of a 3-DOF serial robotic arm and an automatic feeding conveyor.The robotic arm was developed to pick and drop tomato seedlings raised in biodegradable paper pots.The volume of each pot was 50 cm^(3)(3.5 cmdiameter and 5.2 cmheight)with a maximumtotalweight of 47 g including potmix and seedling.Amatrix type feeding conveyorwas developed to convey the pot seedlings to a predefined positionwhere the robotic armcould pick up these seedlings one by one.LDR(Light Dependent Resistor)-LED(Light Emitting Diode)sensing unit was used to perform the intermittent movement of the conveyor.The developed system was evaluated under both laboratory and field conditions.The robotic arm was able to pick and drop 20 seedlings per minute and its effective cycle time per handling one seedling was varying from 2.5 to 3.1 s.Power consumption of the conveyor of the developed system and the robotic arm was 18 W and 16 W,respectively.The conveying,metering and overall efficiency of the developed metering mechanism under laboratory condition were 96.83%,95.91%and 92.86%,respectively as compared to 94.7%,93.28%and 88.33%,under field condition.The developed robotic armbasedmeteringmechanismwas simple,light inweight and effectively handled the pot seedlings without damage and would thus help in mechanizing transplanting of vegetable seedlings. | Vikas Paradkar Hifjur Raheman Rahul K. | 2021 | Artificial Intelligence in Agriculture2021，，1： | 5 |
| 13 | Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG显示文摘In recent times,the use of artificial intelligence(AI)in agriculture has become the most important.The technology adoption in agriculture if creatively approached.Controlling on the diseased leaves during the growing stages of crops is a crucial step.The disease detection,classification,and analysis of diseased leaves at an early stage,as well as possible solutions,are always helpful in agricultural progress.The disease detection and classification of different crops,especially tomatoes and grapes,is a major emphasis of our proposed research.The important objective is to forecast the sort of illness that would affect grapes and tomato leaves at an early stage.The Convolutional Neural Network(CNN)methods are used for detecting Multi-Crops Leaf Disease(MCLD).The features extraction of images using a deep learning-based model classified the sick and healthy leaves.The CNN based Visual Geometry Group(VGG)model is used for improved performance measures.The crops leaves images dataset is considered for training and testing the model.The performance measure parameters,i.e.,accuracy,sensitivity,specificity precision,recall and F1-score were calculated and monitored.The main objective of research with the proposed model is to make on-going improvements in the performance.The designed model classifies disease-affected leaves with greater accuracy.In the experiment proposed research has achieved an accuracy of 98.40%of grapes and 95.71%of tomatoes.The proposed research directly supports increasing food production in agriculture. | Ananda S.Paymode Vandana B.Malode | 2022 | Artificial Intelligence in Agriculture2022，，1： | 4 |
| 14 | A Survey of Collaborative Filtering Techniques显示文摘 | Xiaoyuan Su Taghi M. Khoshgoftaar Jun Hong | 2009 | Advances in Artificial Intelligence2009，，： | 4 |
| 15 | Seedling-lump integrated non-destructive monitoring for automatic transplanting with Intel RealSense depth camera显示文摘Non-destructive plant growth parameters measurement is an important concern in automatic-seedling transplanting.Recently,several image-basedmonitoring approaches have been proposed and potentially developed for several agricultural applications.The presented study proposed and developed a RealSense-based machine vision system for the close-shot seedling-lump integrated monitoring.The strategy was based on the close-shot depth information.Further,the point cloud clustering and suitable algorithms were applied to obtain the segmentation of 3D seedling models.In addition,the data processing pipeline was developed to assess the differentmorphological parameter of 4 different seedling varieties.The experiments were carried out with 4 different seedling varieties(pepper,tomato,cucumber,and lettuce)and trained under different light conditions(light and dark).Moreover,analysis results showed that therewas not significantly different(p<0.05)found towards light and dark environments due to close-shot near-infrared detection.However,the results revealed that the stem diameter relationship between RealSense and the manual method was found for R^2=0.68 cucumber,R^2=0.54 tomato,R^2=0.35 pepper,and R^2=0.58 lettuce seedlings.Whereas,the seedling height relationship between RealSense and the manual methodwas found higher than R^2=0.99,0.99,0.99,and 0.99 for pepper,tomato,cucumber,and lettuce,respectively.Based on the experiment results,it was concluded that the RGB-D integrated monitoring system with the purposed method could be practiced for nursery seedlings most promisingly without high labour requirements in terms of ease of use.The system revealed a good sturdiness and relevance for plant growth monitoring.Additionally,it has the perspective for future practical value to real-time vision servo operations for transplanting robots. | Tabinda Naz Syed Liu Jizhan Zhou Xin Zhao Shengyi Yuan Yan Sami Hassan Ahmed Mohamed Imran Ali Lakhiar | 2019 | Artificial Intelligence in Agriculture2019，，3： | 4 |
| 16 | Automation and digitization of agriculture using artificial intelligence and internet of things显示文摘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. | A.Subeesh C.R.Mehta | 2021 | Artificial Intelligence in Agriculture2021，，1： | 4 |
| 17 | Optimizing the seed-cell filling performance of an inclined plate seed metering device using integrated ANN-PSO approach显示文摘Uniformseed distribution within the row is the prime objective of precision planters for better crop growth and yield.Inclined plate planters are generally used for sowing bold seeds likemaize,groundnut,chickpea,and their operating parameters like the forward speed of operation,the seedmetering plate inclination,and the seed level in the hopper affect the cell fill and subsequently the uniformseed distribution.Therefore,to achieve precise seed distribution,these parameters need to be optimized.In the present study,out of the different optimization techniques,a new intelligent optimization technique based on the integrated ANN-PSO approach has been used to achieve the set goal.A 3–5-1 artificial neural network(ANN)model was developed for predicting the cell fill of inclined plate seedmetering device,and the particle swarmoptimization(PSO)algorithmwas applied to obtain the optimum values of the operating parameters corresponding to 100%cell fill.The most appropriate optimal values of the forward speed of operation,the seed metering plate inclination,and the seed level in the hopper for achieving 100%cell fill were found to be 3 km/h,50-degree,and 75%of total height,respectively.The proposed integrated ANN-PSO approach was capable of predicting the optimal values of operating parameters with amaximumdeviation of 2%compared to the experimental results,thus confirmed the reliability of the proposed optimization technique. | C.M.Pareek V.K.Tewari Rajendra Machavaram Brajesh Nare | 2021 | Artificial Intelligence in Agriculture2021，，1： | 4 |
| 18 | Implementation of artificial intelligence in agriculture for optimisation of irrigation and application of pesticides and herbicides显示文摘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. | Tanha Talaviya Dhara Shah Nivedita Patel Hiteshri Yagnik Manan Shah | 2020 | Artificial Intelligence in Agriculture2020，，1： | 4 |
| 19 | Design and testing of the mechanical picking function of a high-speed seedling auto-transplanter显示文摘To improve the low transplanting efficiencies and simplify the complex structures of current automatic transplanters,a mechanical high-speed transplanter for picking plug seedlings that is suitable for planting on plastic filmswas designed.Themain components were an automatic seedling picking systemand a basket-type planting system,whichwere used for the following processes:automatic picking,planting,soil covering,and suppression of plug seedlings.The performance test was performed on the automatic transplanter with 60-day-old pepper seedlings.The transplanting efficiency was tested at speeds of 40,60,90,and 120 plants·min−1.The results showed that the coefficient of variation(CV)of the plant spacing and the missed transplanting rate increased with the planting frequency,whereas the qualified rate of planting perpendicularity and the qualified rate of planting decreasedwith the increase in the planting frequency.All planting indicesmet the JB/T 102912013 standards.The results of this study showed that the auto-transplanter could performhigh-speed transplanting on the basis of completing the following functions:automatic picking,planting,soil covering,and suppression of plug seedlings. | Changjie Han Xianwei Hu Jing Zhang Jia You Honglei Li | 2021 | Artificial Intelligence in Agriculture2021，，1： | 4 |
| 20 | Design of a 4 DOF parallel robot arm and the firmware implementation on embedded system to transplant pot seedlings显示文摘This paper presents a firmware design and its implementation on a real time embedded system for driving a 4 DOF parallel robot arm.The firmware primarily comprised of two components to produce motion of the robot arm:a)generation of continuous position coordinates and b)generation of actuating signals.These two componentswere processed in two different microcontrollerswith a common communication bus.The position generation algorithm produced and transmitted continuous position data to the motion generation algorithm in the form of G-code strings by reading the input positionswhichwere previously stored by the user in EEPROMmemory of the microcontroller.The receipt of a handshake signal synchronized the data transmission between these components through a communication bus.An LCD display and keypad were used as human-machine interface(HMI)to communicate with the user to set the robot target coordinates.The mechanical structure of the robot arm comprised of multiple links which were actuated by steppermotors.The workspace boundary were sensed by limit switches.The kinematic equations represented the gripper position for the corresponding input joint angles.A microcontroller was used to compute kinematic equations of the robot arm with the help of motion generation algorithm to generate actuation signals for the simultaneous movement of robot joints.The kinematic equations were solved with the dual-core capability of the microcontroller using real time operating system(RTOS),which made the computation faster with an average computation time of 198μs per step.The developed firmware was implemented and tested on a 4 DOF parallel manipulator using embedded microcontrollers for continuous pickup and place of the paper pot seedlings for automating the metering of pot seedlings.The cycle time taken for pickup and dropping of each seedling was 3.5 s with a success rate of 93.3%. | Rahul K Hifjur Raheman Vikas Paradkar | 2020 | Artificial Intelligence in Agriculture2020，，1： | 4 |
您所在的机构暂未开通此部分的功能，请联系管理员