Awesome Advanced Precision Agriculture

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

Table of Contents

• Overview of PA
• Recent Advanced Plant Phenotyping
  • Topic 1. Crop Yield Prediction
  • Topic 2. Crop Counting/ Detection
  • Topic 3. Classification
  • Topic 4. Tree Health
  • Topic 5. Water Stress
  • Topic 6. Weed Detection
• General Auxiliary Datasets for PA
• Tools, Library
• Community
  • Learning Materials
  • Discussion
• Software programs
  • Commercial
  • Open Source
  • Project
• Note

Overview of PA

• Definition (WIKI, ScienceDirect)
• Overview of Precision Agriculture (Cisternas et al.2020)
• Remote sensing in PA( Mulla et al.2013)
• GeoStatistical Applications for PA(Oliver, Margaret A)
• UAV images for PA(Radoglou-Grammatikisa et al.2020)
• Machine Learning for PA(Chlingaryan et al. 2018)
• Big Data in PA(Wolfert et al.2017)
• GPS in PA(Neményi et al.2013)
• IoT for PA(Khanna et al.2018)
• Robot System in PA(P.Vasconez et al.2019)

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

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.