Artificial Intelligence in Agriculture最新文献

{"title":"A systematic review of machine learning techniques for cattle identification: Datasets, methods and future directions","authors":"Md Ekramul Hossain ,&nbsp;Muhammad Ashad Kabir ,&nbsp;Lihong Zheng ,&nbsp;Dave L. Swain ,&nbsp;Shawn McGrath ,&nbsp;Jonathan Medway","doi":"10.1016/j.aiia.2022.09.002","DOIUrl":"10.1016/j.aiia.2022.09.002","url":null,"abstract":"<div><p>Increased biosecurity and food safety requirements may increase demand for efficient traceability and identification systems of livestock in the supply chain. The advanced technologies of machine learning and computer vision have been applied in precision livestock management, including critical disease detection, vaccination, production management, tracking, and health monitoring. This paper offers a systematic literature review (SLR) of vision-based cattle identification. More specifically, this SLR is to identify and analyse the research related to cattle identification using Machine Learning (ML) and Deep Learning (DL). This study retrieved 731 studies from four online scholarly databases. Fifty-five articles were subsequently selected and investigated in depth. For the two main applications of cattle detection and cattle identification, all the ML based papers only solve cattle identification problems. However, both detection and identification problems were studied in the DL based papers. Based on our survey report, the most used ML models for cattle identification were support vector machine (SVM), k-nearest neighbour (KNN), and artificial neural network (ANN). Convolutional neural network (CNN), residual network (ResNet), Inception, You Only Look Once (YOLO), and Faster R-CNN were popular DL models in the selected papers. Among these papers, the most distinguishing features were the muzzle prints and coat patterns of cattle. Local binary pattern (LBP), speeded up robust features (SURF), scale-invariant feature transform (SIFT), and Inception or CNN were identified as the most used feature extraction methods. This paper details important factors to consider when choosing a technique or method. We also identified major challenges in cattle identification. There are few publicly available datasets, and the quality of those datasets are affected by the wild environment and movement while collecting data. The processing time is a critical factor for a real-time cattle identification system. Finally, a recommendation is given that more publicly available benchmark datasets will improve research progress in the future.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 138-155"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721722000125/pdfft?md5=5c303aaed4d0b316e8d46d6fdcabbce5&pid=1-s2.0-S2589721722000125-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"73539392","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Land suitability analysis for maize production using geospatial technologies in the Didessa watershed, Ethiopia","authors":"Mitiku Badasa Moisa ,&nbsp;Firdissa Sadeta Tiye ,&nbsp;Indale Niguse Dejene ,&nbsp;Dessalegn Obsi Gemeda","doi":"10.1016/j.aiia.2022.02.001","DOIUrl":"10.1016/j.aiia.2022.02.001","url":null,"abstract":"<div><p>Physical land suitability assessment is a prerequisite for enhancing yield production and enables the agricultural communities to use the right place for the right crops. Maize is one of stable one food crops of Ethiopia and cultivated in three agroecological zones: highland, midland and lowlands. Despite these facts, maize yield is very low due to a lack of knowledge and information gaps on land suitability. Physical land suitability for maize cultivation is essential to minimize the problem of food security. The present study aims to identify the potential land suitability for maize production in the Didessa watershed, Western Ethiopia using Multi-Criteria Evaluation (MCE) and geospatial technologies. Land use land cover (LULC) change, climate, topography, soil, and infrastructure facilities were considered for maize land suitability assessment. The MCE based pairwise comparison matrix was applied to estimate land suitability for maize crop cultivation. The results showed that, about 977.7 km² (14.1%) is highly suitable, 4794.9 km²(69.1%) is moderately suitable while 1118.8 km² (16.1%), and 51.5 km² (0.7%) of the study area were categorized under marginally and not suitable for maize production, respectively. This research provides crucial information for decision making organs and the farming community to utilize potential areas for maize cultivation.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 34-46"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721722000022/pdfft?md5=37f7314e4592335ab85dfba8861ac549&pid=1-s2.0-S2589721722000022-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46804279","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Transfer Learning for Multi-Crop Leaf Disease Image Classification using Convolutional Neural Network VGG","authors":"Ananda S. Paymode,&nbsp;Vandana B. Malode","doi":"10.1016/j.aiia.2021.12.002","DOIUrl":"10.1016/j.aiia.2021.12.002","url":null,"abstract":"<div><p>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.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 23-33"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721721000416/pdfft?md5=6efb65071e9550352409895eda1a2383&pid=1-s2.0-S2589721721000416-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47509400","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"A comparative analysis of paddy crop biotic stress classification using pre-trained deep neural networks","authors":"Naveen N. Malvade ,&nbsp;Rajesh Yakkundimath ,&nbsp;Girish Saunshi ,&nbsp;Mahantesh C. Elemmi ,&nbsp;Parashuram Baraki","doi":"10.1016/j.aiia.2022.09.001","DOIUrl":"10.1016/j.aiia.2022.09.001","url":null,"abstract":"<div><p>The agriculture sector is no exception to the widespread usage of deep learning tools and techniques. In this paper, an automated detection method on the basis of pre-trained Convolutional Neural Network (CNN) models is proposed to identify and classify paddy crop biotic stresses from the field images. The proposed work also provides the empirical comparison among the leading CNN models with transfer learning from the ImageNet weights namely, Inception-V3, VGG-16, ResNet-50, DenseNet-121 and MobileNet-28. Brown spot, hispa, and leaf blast, three of the most common and destructive paddy crop biotic stresses that occur during the flowering and ripening growth stages are considered for the experimentation. The experimental results reveal that the ResNet-50 model achieves the highest average paddy crop stress classification accuracy of 92.61% outperforming the other considered CNN models. The study explores the feasibility of CNN models for the paddy crop stress identification as well as the applicability of automated methods to non-experts.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 167-175"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721722000113/pdfft?md5=2c29b7fc4906082786b3953f41cebabd&pid=1-s2.0-S2589721722000113-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41854928","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Predicting the true density of commercial biomass pellets using near-infrared hyperspectral imaging","authors":"Lakkana Pitak ,&nbsp;Khwantri Saengprachatanarug ,&nbsp;Kittipong Laloon ,&nbsp;Jetsada Posom","doi":"10.1016/j.aiia.2022.11.004","DOIUrl":"10.1016/j.aiia.2022.11.004","url":null,"abstract":"<div><p>The use of biomass is increasing because it is a form of renewable energy that provides high heating value. Rapid measurements could be used to check the quality of biomass pellets during production. This research aims to apply a near-infrared (NIR) hyperspectral imaging system for the evaluation of the true density of individual biomass pellets during the production process. Real-time measurement of the true density could be beneficial for the operation settings, such as the ratio of the binding agent to the raw material, the temperature of operation, the production rate, and the mixing ratio. The true density could also be used for rough measurement of the bulk density, which is a necessary parameter in commercial production. Therefore, knowledge of the true density is required during production in order to maintain the pellet quality as well as operation conditions. A prediction model was developed using partial least squares (PLS) regression across different wavelengths selected using different spectral pre-treatment methods and variable selection methods. After model development, the performance of the models was compared. The best model for predicting the true density of individual pellets was developed with first-derivative spectra (D1) and variables selected by the genetic algorithm (GA) method, and the number of variables was reduced from 256 to 53 wavelengths. The model gave R²_cal, R²_val, SEC, SEP, and RPD values of 0.88, 0.89, 0.08 g/cm³, 0.07 g/cm³, and 3.04, respectively. The optimal prediction model was applied to construct distribution maps of the true density of individual biomass pellets, with the level of the predicted values displayed in colour bars. This imaging technique could be used to check visually the true density of biomass pellets during the production process for warnings to quality control equipment.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 266-275"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721722000228/pdfft?md5=cb77a6134aff04eafcac2350ee68b17f&pid=1-s2.0-S2589721722000228-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47398371","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Prediction of exchangeable potassium in soil through mid-infrared spectroscopy and deep learning: From prediction to explainability","authors":"Franck Albinet ,&nbsp;Yi Peng ,&nbsp;Tetsuya Eguchi ,&nbsp;Erik Smolders ,&nbsp;Gerd Dercon","doi":"10.1016/j.aiia.2022.10.001","DOIUrl":"10.1016/j.aiia.2022.10.001","url":null,"abstract":"<div><p>The ability to characterize rapidly and repeatedly exchangeable potassium (K_ex) content in the soil is essential for optimizing remediation of radiocaesium contamination in agriculture. In this paper, we show how this can be now achieved using a Convolutional Neural Network (CNN) model trained on a large Mid-Infrared (MIR) soil spectral library (40,000 samples with K_ex determined with 1 M NH₄OAc, pH 7), compiled by the National Soil Survey Center of the United States Department of Agriculture. Using Partial Least Squares Regression as a baseline, we found that our implemented CNN leads to a significantly higher prediction performance of K_ex when a large amount of data is available (10000), increasing the coefficient of determination from 0.64 to 0.79, and reducing the Mean Absolute Percentage Error from 135% to 31%. Furthermore, in order to provide end-users with required interpretive keys, we implemented the GradientShap algorithm to identify the spectral regions considered important by the model for predicting K_ex. Used in the context of the implemented CNN on various Soil Taxonomy Orders, it allowed (i) to relate the important spectral features to domain knowledge and (ii) to demonstrate that including all Soil Taxonomy Orders in CNN-based modeling is beneficial as spectral features learned can be reused across different, sometimes underrepresented orders.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 230-241"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721722000186/pdfft?md5=8126426530126bf7ca26081e52cbb6d7&pid=1-s2.0-S2589721722000186-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49217627","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Developing a multi-label tinyML machine learning model for an active and optimized greenhouse microclimate control from multivariate sensed data","authors":"Ilham Ihoume,&nbsp;Rachid Tadili,&nbsp;Nora Arbaoui,&nbsp;Mohamed Benchrifa,&nbsp;Ahmed Idrissi,&nbsp;Mohamed Daoudi","doi":"10.1016/j.aiia.2022.08.003","DOIUrl":"10.1016/j.aiia.2022.08.003","url":null,"abstract":"<div><p>In the uncertainties within which the worldwide food security lies nowadays, the agricultural industry is raising a crucial need for being equipped with the state-of-the-art technologies for a more efficient, climate-resilient and sustainable production. The traditional production methods have to be revisited, and opportunities should be given for the innovative solutions henceforth brought by big data analytics, cloud computing and internet of things (IoT). In this context, we develop an optimized tinyML-oriented model for an active machine learning-based greenhouse microclimate management to be integrated in an on-field microcontroller. We design an experimental strawberry greenhouse from which we collect multivariate climate data through installed sensors. The obtained values' combinations are labeled according to a five-action multi-label control strategy, then used to prepare a machine learning-ready dataset. The dataset is used to train and five-fold cross-validate 90 Multi-Layer Perceptrons (MLPs) with varied hyperparameters to select the most performant –yet optimized– model instance for the addressed task. Our multi-label control approach enables designing highly scalable models with reduced computational complexity, comprising only <em>n</em> control neurons instead of (1 + ∑_<em>n</em>^<em>k</em>=1<em>C</em>_<em>n</em>^<em>k</em>) neurons (usually generated from a classic single-label approach from <em>n</em> input variables). Our final selected model incorporates 2 hidden layers with 7 and 8 neurons respectively and 151 parameters; it scored a mean accuracy of 97% during the cross-validation phase, then 96% on our supplementary test set. The model enables an intelligent and autonomous greenhouse management with the less required computations. It can be efficiently deployed in microcontrollers within real world operating conditions.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 129-137"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721722000101/pdfft?md5=73a9c3cd093ea0be14dfa96d10299fd2&pid=1-s2.0-S2589721722000101-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"49336654","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Analysis of land surface temperature using Geospatial technologies in Gida Kiremu, Limu, and Amuru District, Western Ethiopia","authors":"Mitiku Badasa Moisa ,&nbsp;Bacha Temesgen Gabissa ,&nbsp;Lachisa Busha Hinkosa ,&nbsp;Indale Niguse Dejene ,&nbsp;Dessalegn Obsi Gemeda","doi":"10.1016/j.aiia.2022.06.002","DOIUrl":"10.1016/j.aiia.2022.06.002","url":null,"abstract":"<div><p>Degradation of vegetation cover and expansion of barren land are remained the leading environmental problem at global level. Land surface temperature (LST), Normalized Difference Vegetation Index (NDVI), Normalized Difference Barren Index (NDBaI), and Modified Normalized Difference Water Index (MNDWI) were used to quantify the changing relationships using correlation analysis. This study attempted to analyze the relationship between LST and NDVI, NDBaI, and MNDWI using Geospatial technologies in Gida Kiremu, Limu, and Amuru districts in Western Ethiopia. All indices were estimated by using thermal bands and multispectral bands from Landsat TM 1990, Landsat ETM+ 2003, and Landsat OLI/TIRS 2020. The correlation of LST with NDVI, NDBaI and MNDWI were analyzed by using scatter plot. Accordingly, the NDBaI was positive correlation with LST (R² = 0.96). However, NDVI and MNDWI were substantially negative relationship with LST (R² = 0.99, 0.95), respectively. The result shows that, LST was increased by 5 °C due to decline of vegetation cover and increasing of bare land over the study periods. Finally, our result recommended that, decision-makers and environmental analysts should give attention on the importance of vegetation cover, water bodies and wetland in climate change mitigation, particularly, LST in the study area.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 90-99"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721722000071/pdfft?md5=d8215559b09e2b7f75f1579019af14bd&pid=1-s2.0-S2589721722000071-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"54191449","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Deep learning based computer vision approaches for smart agricultural applications","authors":"V.G. Dhanya ,&nbsp;A. Subeesh ,&nbsp;N.L. Kushwaha ,&nbsp;Dinesh Kumar Vishwakarma ,&nbsp;T. Nagesh Kumar ,&nbsp;G. Ritika ,&nbsp;A.N. Singh","doi":"10.1016/j.aiia.2022.09.007","DOIUrl":"10.1016/j.aiia.2022.09.007","url":null,"abstract":"<div><p>The agriculture industry is undergoing a rapid digital transformation and is growing powerful by the pillars of cutting-edge approaches like artificial intelligence and allied technologies. At the core of artificial intelligence, deep learning-based computer vision enables various agriculture activities to be performed automatically with utmost precision enabling smart agriculture into reality. Computer vision techniques, in conjunction with high-quality image acquisition using remote cameras, enable non-contact and efficient technology-driven solutions in agriculture. This review contributes to providing state-of-the-art computer vision technologies based on deep learning that can assist farmers in operations starting from land preparation to harvesting. Recent works in the area of computer vision were analyzed in this paper and categorized into (a) seed quality analysis, (b) soil analysis, (c) irrigation water management, (d) plant health analysis, (e) weed management (f) livestock management and (g) yield estimation. The paper also discusses recent trends in computer vision such as generative adversarial networks (GAN), vision transformers (ViT) and other popular deep learning architectures. Additionally, this study pinpoints the challenges in implementing the solutions in the farmer’s field in real-time. The overall finding indicates that convolutional neural networks are the corner stone of modern computer vision approaches and their various architectures provide high-quality solutions across various agriculture activities in terms of precision and accuracy. However, the success of the computer vision approach lies in building the model on a quality dataset and providing real-time solutions.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 211-229"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721722000174/pdfft?md5=a432376ae19a8efc430e8ac20394f2b0&pid=1-s2.0-S2589721722000174-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42036657","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Automatic marker-free registration of single tree point-cloud data based on rotating projection","authors":"Xiuxian Xu ,&nbsp;Pei Wang ,&nbsp;Xiaozheng Gan ,&nbsp;Jingqian Sun ,&nbsp;Yaxin Li ,&nbsp;Li Zhang ,&nbsp;Qing Zhang ,&nbsp;Mei Zhou ,&nbsp;Yinghui Zhao ,&nbsp;Xinwei Li","doi":"10.1016/j.aiia.2022.09.005","DOIUrl":"10.1016/j.aiia.2022.09.005","url":null,"abstract":"<div><p>Point-cloud data acquired using a terrestrial laser scanner play an important role in digital forestry research. Multiple scans are generally used to overcome occlusion effects and obtain complete tree structural information. However, the placement of artificial reflectors in a forest with complex terrain for marker-based registration is time-consuming and difficult. In this study, an automatic coarse-to-fine method for the registration of point-cloud data from multiple scans of a single tree was proposed. In coarse registration, point clouds produced by each scan are projected onto a spherical surface to generate a series of two-dimensional (2D) images, which are used to estimate the initial positions of multiple scans. Corresponding feature-point pairs are then extracted from these series of 2D images. In fine registration, point-cloud data slicing and fitting methods are used to extract corresponding central stem and branch centers for use as tie points to calculate fine transformation parameters. To evaluate the accuracy of registration results, we propose a model of error evaluation via calculating the distances between center points from corresponding branches in adjacent scans. For accurate evaluation, we conducted experiments on two simulated trees and six real-world trees. Average registration errors of the proposed method were 0.026 m around on simulated tree point clouds, and 0.049 m around on real-world tree point clouds.</p></div>","PeriodicalId":52814,"journal":{"name":"Artificial Intelligence in Agriculture","volume":"6 ","pages":"Pages 176-188"},"PeriodicalIF":0.0,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2589721722000150/pdfft?md5=6ad6a8d0665e0efd291bc1d6b93e8101&pid=1-s2.0-S2589721722000150-main.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42279619","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}