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Advancement of UAV, deep Learning and cutting edged technologies and papers in Precision Agriculture.

Awesome Advanced Precision Agriculture Awesome

Advancement of UAV, deep Learning and cutting edged technologies and papers in Precision Agriculture. (contribution are welcome)

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Table of Contents

Overview of PA

Recent Advanced Plant Phenotyping

The implementation of PA could be divided into monitoring and plant management. For plant monitoring/phenotyping, the considerable diversity of vegetation indicators can broadly be divided into three categories: compositional, structural, and functional indicators (Noss, 1990). Where two main approaches have been applied to monitoring vegetation condition: site-based assessments and remote sensing. (Methodology Reference) The management of plant is based on spatial and temporal scales.

There have been attempts to use a long list monitoring the plant. However, due to the complex environment and plant characteristics, specific topics need to be discussed separately.

In this list, the most popular 20 advancements of topic are selected. The link of topic directly connect to the discussion part.

Table XX. Topics of Implement of PA

Area factors/indicators Topic Methods Reference (Review)
Plant Monitoring compositional richness UAV, RS, site-based 
diversity
vegetation classification UAV, RS, site-based 
threated species
disease detection
weed detection UAV, RS, site-based 
crop detection/counting UAV, RS, site-based 
structural spatial-temporal variability
canopy cover
plant density
landscape context
functional indicators disturbance history
water stress  Gerhards et al.2019, Ihuoma et al.2016
forest/tree health UAV, RS, site-based  Lausch et al.2016
nutrient cycling
Plant Management in-situ fertilizer VRT,GPS
soil management sampling
yield management
water management
environment management
risk insect  weather
cost crop selection,market,loans and insurance

Topic 1. Crop Yield Prediction

  • DL for Crop Yield Prediction
  • traditional ML for Crop Yield Prediction
  • Datasets, contests
  • Conceptional diagram of relationship between yield, historical yield, environment.

Table 1. ML methods for Crop Yield Prediction

Year Method Keywords Publication (Re-)Implementation
2006 piecewise linear regression NDVI, Iowa, Soybean, Corn Crop yield estimation model for Iowa using remote sensing and surface parameters(Prasad et al.2006) ML library
2015 InnerProductLayer Agriculture,RS,ML,Feature extraction,Indexes,Data models Estimating crop yields with deep learning and remotely sensed data(Kuwata et al.2015) Caffe
2016 Random Forest Wheat, RF Random Forests for Global and Regional Crop Yield Predictions(Jeong et al.2016) ML library
2017 CNN with Gaussian Process Gaussian Process, DL Deep gaussian process for crop yield prediction based on remote sensing data (You et al.2017) Tensorflow 2+(Code)
2018 LSTM LSTM,RNN,soybean,maize A Scalable Machine Learning System for Pre-Season Agriculture Yield Forecast(Cunha et al.2018) Keras(Code)
2019 LSTM with Gaussian Process Gaussian Process Deep Learning For Crop Yield Prediction in Africa(Kaneko et al.2019) Tensorflow(Code)
2019 CNN-RNN DL, CNN, Feature Selection, RNN A CNN-RNN Framework for Crop Yield Prediction(Khaki et al.2019) Python 3.0 Tensorflow 2+ (Code)
2019 CNN-LSTM soybean, yield prediction, county-level, Google Earth Engine, CNN-LSTM County-Level Soybean Yield Prediction Using Deep CNN-LSTM Model(Sun et al.2019) Google Earth Engine
2019 CNN based on Regression CNN, Regression Winter Wheat Yield Estimation from Multitemporal Remote Sensing Images based on Convolutional Neural Networks (Mu et al.2019) tensorflow 2.0, gdal(Code)
2020 Two-branch DNN RS,crop,yield prediction,yield detrending Winter Wheat Yield Prediction at County Level and Uncertainty Analysis in Main Wheat-Producing Regions of China with Deep Learning Approaches(wang et al.2020) No-data
Others Updating, Please make contributions

Table 2. Datasets for Crop Yield Prediction

Name Description Crop Range
faostat(CSV) Basic yield according to country in format of CSV All range World,1961-2016
EarthStat Harvested Area and Yield for 4 Crops of wheat, maize, rice, and soybean. wheat, maize, rice, and soybean World(1995,2000,2005)
GDHY The Global Dataset of Historical Yield (GDHYv1.2+v1.3) offers annual time series data of 0.5-degree grid-cell yield estimates of major crops worldwide for the period 1981-2016. maize, rice, wheat and soybean World,1981-2016
Others Updating, Please make contributions

Contest of Crop Yield Prediction

  • 2020 Syngenta Crop Challenge (Syngenta, 2020) Using real-world crop data to develop models that can predict the performance of potential corn products.
  • 2018 Syngenta Crop Challenge (Syngenta, 2018) In the 2018 Syngenta Crop Challenge, Syngenta released several large datasets that recorded the genotype and yield performances of 2,267 maize hybrids planted in 2,247 locations between 2008 and 2016 and asked participants to predict the yield performance in 2017.

Topic 2. Crop Counting/ Detection

  • DL for crop detection
  • traditional ML for crop detection
  • Datasets, contests
  • Overview of Different crops
  • Difference of counting and detection

Table1.DL Methods for Crop Counting

Year Method Keywords Title MAE Re-Implement
2017 mTASSEL Land-based,Maize tassels,CNN TasselNet: counting maize tassels in the wild via local counts regression network(Lu et al.2017) 6.6 Pytorch(Code)
2018 Fast-RCNN Land-based,Spike Count # Detection and analysis of wheat spikes using Convolutional Neural Networks(Hasan et al.2019) 5.4 Caffe(Code)
2019 TasselNetv2 Land-based,Maize tassels,CNN TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks(Xiong et al.2019) 5.4 Pytorch(Code)
2019 Deep Counting Land-based,superpixels,crop yield DeepCount: In-Field Automatic Quantification of Wheat Spikes Using Simple Linear Iterative Clustering and Deep Convolutional Neural Networks(Sadeghi-Tehran et al., 2019) XXX Keras(Code)
2020 Comparison Automatic wheat ear counting using machine learning based on RGB UAV imagery(https://onlinelibrary.wiley.com/doi/full/10.1111/tpj.14799))
Others Updating, Please make contributions

Table2. ML Methods for Crop Counting

Method Author Height Accuracy Limitation
RGB image texture histogram analysis (Cointault et al., 2008) 1-2meter 73%~85% Low accuracy
Laplacian frequency filter, median spatial filter and local peak segmentation (Fernandez-Gallego et al., 2018) 1meter 90% Failed at different stages of plant development. Due to changes in canopy color (yellowing)
Use Gabor filter, principal component analysis and k-means clustering (Alharbi et al., 2018) 2 meter 80~90% Different stages. Complex environment.
Use color, texture and histogram, kernel principal component analysis (KPCA) and dual support vector machine (TWSVM) model for multi-feature extraction Chengquan Zhou 2018 5.0meter 82% The initial steps require minimal human intervention to generate patches from the original image for different developmental stages
Artificial Nerual Network Hao Lu 2017 Pouria Sadeghi-Tehran 2019 2.5meter 85%~95% Vehicle-mounted visible camera
Others Updating, Please make contributions

Table3. Datasets for Crop Counting

Source Name Size Description
Land-based Global Wheat Detection Kaggle The data is images of wheat fields, with bounding boxes for each identified wheat head. Not all images include wheat heads / bounding boxes. The images were recorded in many locations around the world. 3,000+1000
Land-based SPIKE The SPIKE dataset contains images with manually annotated ground truth labels for training and testing convolutional neural networks for spike detection of wheat in the field. 500+
Land-based Maize Tassels Counting Dataset - 361 field images collected from 4 experimental fields across China: Zhengzhou, Henan Province, China, Taian, Shandong Province, China, Gucheng, Hebei Province, China, and Jalaid, Sinkiang Autonomous Region, China. 361

Contest for Crop Counting

  • Global Wheat Detection (kaggle, 2020) In this competition, players detect wheat heads from outdoor images of wheat plants, including wheat datasets from around the globe.

Topic 3. Classification

Topic 4. Tree Health

  • Method, Indices, and example applications in PA
  • Furthermore

Index: leaf defoliation,leaf chlorosis and other discolouration,dead branches,canopy damage

Methods: close range remote sensing, Lidar, RADAR, Multi-Sensor Approaches, Spectral Laboratory, Plant Phenomics Facilities and Ecotrons, UAV, THermal

Table x. Recent progress of tree health monitoring in PA

Application Platform Keywords Title
polar vegetation,vigorous UAV&Satellite SVM, Hyper-spectrum Unmanned aircraft system advances health mapping of fragile polar vegetation(Malenovsky et al.2017)
urban tree, discoloration and defoliation UAV&Lidar random forest, Hyper-spectrum,HSI,VI Urban Tree Health Classification Across Tree Species by Combining Airborne Laser Scanning and Imaging Spectroscopy(Chi et al.2020)
orchard, defoliation In-situ&UAV&Thermal Hyper-spectrum,HSI,VI Early Detection and Quantification of Almond Red Leaf Blotch Using High-Resolution Hyperspectral and Thermal Imagery(Lopez et al.2016)
Updating Mapping canopy defoliation by herbivorous insects at the individual tree level using bi-temporal airborne imaging spectroscopy and LiDAR measurements(paper)
Updating Using UAV-based photogrammetry and hyperspectral imaging for mapping bark beetle damage at tree level.(paper)
Updating Urban tree health assessment using airborne hyperspectral and LiDAR imagery(paper)
Updating Street tree health from space? An evaluation using WorldView-3 data and the Washington D.C. Street Tree Spatial Database(paper)

Topic 5. Water Stress

  • Plant based measurement summary
  • RS based measurement summary
  • Conceptional diagram of relationship between stress, response, VIs
  • ET model
  • Integration of thermal and narrow-band hyperspectral imagery
  • Community support like meeting, dataset, workflow...

Table XX. Plant based method(Ihuoma et al.2016)

Method Description Adv Disadv Papers
Stomatal conductance Indirect indicator of plant water stress by measuring the stomata opening Good measure of plant water status. Used as benchmark for most research studies Labour intensive and unsuitable for automation and commercial application. Not very accurate for anisohydric crops
Leaf water potential Direct measurement of leaf water content Widely accepted reference technique Slow, destructive, and unsuitable for isohydric crops
Relative water content Direct measurement of leaf water status Good indicator plant water status, requiring less sophisticated equipment Destructive and time consuming
Sap flow measurement Measures the rate of transpiration through heat pulse Sensitive to stomatal closure and water deficits. Adapted for automated recording and control of irrigation systems Needs calibration for each tree and is difficult to replicate. Requires complex instrumentation and expertise
Stem and fruit diameter Measures fluctuation in stem and fruit diameters in response to changes in water content Sensitive measure of plant water stress Not useful for the control of high-frequency irrigation systems

In Remote sensing, the monitoring water stress is based on the relationships between primary plant stresses, the induced plant responses, and the multi-/hyperspectrum(Gerhards et al.2019, Jones et al.2010 ).

Figure XX. Relationship between stresses, response, RS enter image description here

Table X. commonly applied indices and approaches for water-stress detection using the main multi-/hyperspectral remote sensing imaging techniques(Gerhards et al.2019)

Water-Stress Index Plant Response to Water Stress Formula Reference Application
SDD (Stress Degree Day) Rise in plant temperature Tc − Tair updating
CWSI (Crop Water Stress Index) Rise in plant temperature CWSI = (Tc − Twet)/(Tdry − Twet) updating
WDI (Water Deficit Index) Rise in plant temperature Combination of NDVI (or derivate, e.g., SAVI) and Tc updating
Spectral emissivity Alteration due to changes in the compositions of leaf constituents Spectral emissivity (ɛ) updating
PRI (Photochemical Reflectance Index) Changes in xanthophyll content PRI = (R570 − R531)/(R570 + R531) updating
SR (Simple Ratio) Decrease in chlorophyll content SR = R800/R670 updating
NDVI (Normalized Difference Vegetation Index) Decrease in chlorophyll content, canopy structural changes NDVI = (R800 − R670)/(R800 + R670) updating
WI (Water Index) Decrease in leaf water content WI = R900/R970 updating
LWI (Leaf Water Index) Decrease in leaf water content LWI = R1300/R1450 updating
MSI (Moisture Stress Index) Decrease in leaf water content MSI = R1600/R820 updating
NDWI (Normalized Difference Water Index) Decrease in leaf water content NDWI = (R857 − R1241)/(R857 + R1241) updating
SIF Changes in photosynthetic efficiency due to decreased CO2 uptake SIF685, SIF740, or SIF685/SIF740 updating

Topic 6. Weed Detection

A Crop/Weed Field Image Dataset - This dataset comprises field images, vegetation segmentation masks and crop/weed plant type annotations.

General Auxiliary Datasets for PA

Table XX. Auxiliary dataset list

Type Name Resolution Description Range
worldclim WorldClim is a database of high spatial resolution global weather and climate data. World, 1960-2018
CRU TS v4 CRU TS is one of the most widely used observed climate datasets and is produced by the UK’s National Centre for Atmospheric Science (NCAS) World, 1901-2019
SRTM Elevation World,1970-Present
Soil SoilGrids A system for digital soil mapping based on global compilation of soil profile data and environmental layers World, Prediction 30-90m
MODIS The many data products derived from MODIS observations describe features of the land, oceans and the atmosphere that can be used for studies of processes and trends on local to global scales World,1970-Present
Weather Daymet The Daymet dataset provides gridded estimates of daily weather parameters. Seven surface weather parameters are available at a daily time North American,1980-Present
Crop CropScape and Cropland Crop Dataset Layer US,2008-Present
Precipitation CHIRPS Climate Hazards Group InfraRed Precipitation with Station data (CHIRPS) is a 30+ year quasi-global rainfall dataset. World,1980-Present 0.05
Weather ECMWF ECMWF produce and disseminate weather forecast data for the National Meteorological and Hydrological Services (NHMSs) of ECMWF Member and Co-operating States and their authorised users Prediction
Meteorology National Australia BOM

Tools, Library

Community

Learning Materials

  • Remote Sensing in Precision Agriculture: An Educational Primer
  • Index Database - An Index-Data-Base (IDB) could be an useful tool to find indices for a required application, adapted to a selected sensor.
  • Awesome GIS - Awesome GIS is a collection of geospatial related sources, including cartographic tools, geoanalysis tools, developer tools, data, conference & communities, news, massive open online course, some amazing map sites, and more.
  • Awesome Agriculture

Discussion

  • Open Plant Pathology - Open Plant Pathology is an supports and promotes the spread of all open, transparent and reproducible practices in the field of plant pathology.
  • Farm Hack - Worldwide community of farmers that build and modify our own tools.
  • ROS Agriculture Community - Open Source community focusing on using Robot Operating System to empower farmers with robotics tools.
  • OpenFarm - A free and open database for farming and gardening knowledge. You can grow anything
  • Harvest_helper - Provides growing, harvesting and recipe information for the 45 plants in the database as well as a json api so that people can hopefully use this data to build other apps.

Software programs

There are currently a large number of software with PA tools, and we have a brief summary of them

Commercial

Name Description Applications
ERDAS
ArcGIS
ENVI

Open Source

Library

Name Description Language
PlantCV PlantCV is composed of modular functions in order to be applicable to a variety of plant types and imaging systems. Python
pyprecag A suite of tools for Precision Agriculture data analysis Python
Crop Monitoring Machine Set of Machine Learning Algorithms developed with the aim of determining health states of different types of crops Python
Open Plant Pathology A community that values open data and computational tools for advancing epidemiology and pathogen population biology and ecology Mix

Software

Name Description Platform
GeoFIS GeoFIS is a free and open source software platform for high spatial resolution data processing with a decision support perspective. Win, Linux,Docker
Sen2Agri-System Sentinel-2 for Agriculture (Sen2Agri) is a software system processing high resolution satellite images for agricultural purposes funded by ESA (European Space Agency). Please register on the Sen2Agri webpage for Sen2Agri system updates and information. Service

Project

Precision Farming - It employs the use of IOT to monitor, store and analyse soil conditions in a green house to enhance crop production. The microcontroller used is the raspberry pi. Plant Phenotyping - A modular software architecture for Automatic Plant Phenotyping Deep Learning for Biologists with Keras - Tutorials for deep learning based analysis (mainly) on biological relavent themes.

Note

This list will be updated in time, and volunteer contributions are welcome. For questions or sharing, please feel free to contact us or make contributions.