96 skills found · Page 1 of 4
danforthcenter / PlantcvPlant phenotyping with image analysis
NatLabRockies / OpenOAThis library provides a framework for assessing wind plant performance using operational assessment (OA) methodologies that consume time series data from wind plants. The goal of the project is to provide an open source implementation of common data structures, analysis methods, and utility functions relevant to wind plant OA.
digitalepidemiologylab / Plantvillage Deeplearning Paper AnalysisNo description available
openalea / PlantglAn open-source graphic toolkit for the creation, simulation and analysis of 3D virtual plants.
AnnadataHackfest / AnnadataAn application that provides complete assistance to farmers right from sowing to harvesting. Its features include plant disease detection, crop recommendation, real-time API support for environment analysis, detailed crop-cost analysis, buy/sell/rent farming equipment and an interactive farmers' community.
TiagoOlivoto / PlimanTools for Plant Image Analysis
whussain2 / R For Plant BreedingR for Plant Breeding is open source resource to lear the Plant Breeding and Genetics related data analysis and visualizations
SkyCol / PlantLidarPointCloud analysis focus on solving 3D plant phenotypes
nark / MagicboxOpen indoor growing platform
openalea / PhenomenalPhenomenal: An automatic open source library for 3D shoot architecture reconstruction and analysis for image-based plant phenotyping
dePamphilis / PlantTribesPlantTribes is a collection of automated gene family analysis pipelines for comparative plant genomics
Beckybams / Crop Disease Detection Via Image ProcessingCrop-Disease-Detection-via-Image-Processing uses machine learning and image analysis to identify plant diseases from leaf images. The system preprocesses images, extracts features, and applies a trained model to classify diseases accurately.
follow-the-vine-to-get-to-the-melon / PlantsVsZombies Cheat植物大战僵尸逆向分析与辅助开发系列教程，教程适合新手入门学习由浅入深递进式教学。 Plants vs. Zombies reverse analysis and auxiliary development series of tutorials are suitable for beginners to learn from the simple to the deep.
romi / 4d Plant AnalysisA new method for the space-time registration of a growing plant based on matching the plant at different geometric scales. The proposed method starts with the creation of a topological skeleton of the plant at each time step. This skeleton is then used to segment the plant into its different organs, including its main stem, its branches, etc. Then the organs are further divided into smaller segments that possess simpler geometric structures, for instance, cylinders, rectangular. Those segments are matched between two time steps using a random forest classifier based on their topological and geometric features. Then, for each pair of segments matched, a point-wise registration is devised using a non-rigid registration method based on a local ICP (Iterative Closest Point) algorithm.
SuryaNarayananDev / ECORAIZ The Smart PlantingEcoRaiz is an intelligent planting system designed to help transform dry, barren land into thriving green forests. By combining image processing and environmental analysis, it identifies the best zones for planting and recommends tree species based on sustainability, soil condition, and climate.
shishirdas / Rain Fall Data Analysis Using Data ScienceContext Rainfall is very crucial things for any types of agricultural task. Climate related data is important to analyse agricultural and crop seeding related field, where those data can be used to show the predict the rainfall in different season also for different types of crops. Developed application can be found from http://ml.bigalogy.com/ Paper: http://dspace.uiu.ac.bd/handle/52243/178 Abstract Mankind have been attempting to predict the weather from prehistory. For good reason for knowing when to plant crops, when to build and when to prepare for drought and flood. In a nation such as Bangladesh being able to predict the weather, especially rainfall has never been so vitally important. The proposed research work pursues to produce prediction model on rainfall using the machine learning algorithms. The base data for this work has been collected from Bangladesh Meteorological Department. It is mainly focused on the development of models for long term rainfall prediction of Bangladesh divisions and districts (Weather Stations). Rainfall prediction is very important for the Bangladesh economy and day to day life. Scarcity or heavy - both rainfall effects rural and urban life to a great extent with the changing pattern of the climate. Unusual rainfall and long lasting rainy season is a great factor to take account into. We want to see whether too much unusual behavior is taking place another pattern resulting new clamatorial description. As agriculture is dependent on rain and heavy rainfall caused flood frequently leading to great loss to crops, rainfall is a very complex phenomenon which is dependent on various atmospheric, oceanic and geographical parameters. The relationship between these parameters and rainfall is unstable. Beside this changing behavior of clamatorial facts making the existing meteorological forecasting less usable to the users. Initially linear regression models were developed for monthly rainfall prediction of station and national level as per day month year. Here humidity, temperatures & wind parameters are used as predictors. The study is further extended by developing another popular regression analysis algorithm named Random Forest Regression. After then, few other classification algorithms have been used for model building, training and prediction. Those are Naive Bayes Classification, Decision Tree Classification (Entropy and Gini) and Random Forest Classification. In all model building and training predictor parameters were Station, Year, Month and Day. As the effect of rainfall affecting parameters is embedded in rainfall, rainfall was the label or dependent variable in these models. The developed and trained model is capable of predicting rainfall in advance for a month of a given year for a given area (for area we used here are the stations (weather parameters values are measured by Bangladesh Meteorological Department). The accuracy of rainfall estimation is above 65%. Accuracy percentage varies from algorithm to algorithm. Two regression analysis and three classification analysis models has been developed for rainfall prediction of 33 Bangladeshi weather station. Apache Spark library has been used for machine library in Scala programming language. The main idea behind the use of classification and regression analysis is to see the comparative difference between types of algorithms prediction output and the predictability along with usability. This thesis is a contribution to the effort of rainfall prediction within Bangladesh. It takes the strategy of applying machine learning models to historical weather data gathered in Bangladesh. As part of this work, a web-based software application was written using Apache Spark, Scala and HighCharts to demonstrate rainfall prediction using multiple machine learning models. Models are successively improved with the rainfall prediction accuracy. Content The given data has weather station and year wise monthly rainfall data of Bangladesh. Data is two format - 46 year (33 Weather Station) : From 1970 to 2016 Daily Rainfall Data Monthly Rainfall Data Columns: Station (Weather Station, along with Station Index) Year Month Day [For daily data file]
compbioNJU / ScPlantA versatile framework for single-cell transcriptomic data analysis in plants.
programmingboy / Canadian Medicinal Plant Detection Using Convolutional Neural Network With Transfer LearningNowadays, computerized plant species classification systems are used to help the people in the detection of the various species. However, the automated analysis of plant species is challenging as compared to human interpretation. This research as been provided in this field for the better classification of plant species. Even now, these methodologies lack an exact classification of the plant species. The challenge is due to the inappropriate classification algorithm. In Particular, when we consider the medicinal plant species recognition, the accuracy will be the main criteria. In this research, the suggested system implements the deep learning technique to obtain high accuracy in the classification process using computer prediction methods.The Convolutional Neural Network (CNN) is employed beside transfer learning for deep learning of medicinal plant images. This research work has been carried out on the flower images dataset of four Canadian medical plants; namely, Clubmoss, Dandelion, Lobelia, and Bloodroot, which is fed as the training dataset for the CNN and machine learning-based proposed system. Finally, an accuracy of 96% has been achieved in classification of the medicinal plant species.
kausthub-kannan / Farmsense.AIfarmsense.ai is an AI assistant for exponentially growing Hybrid farming. To enhance farmers' yield, this application assists them by detecting diseases of plants and poultries and providing crop and egg incubation analysis. CNN, YOLO, and XGBoost are used for the models.
bpucker / MYB AnnotatorThis tool performs an automatic identification, annotation, and analysis of the MYB gene family in plants. It can be applied to new transcriptome of genome assemblies.
