Crop disease detection using Unmanned Aerial Vehicle system

Pakistan is an agricultural country,and agricultural industry is the backbone of our economy.As we boost our agriculture industry it will boost our economy directly. Wheat is the Major crop of the Pakistan and Pakistan is 8th largest producer of wheat in the world there are lots of diseases

Project Title

Crop disease detection using Unmanned Aerial Vehicle system

Project Area of Specialization

Robotics

Project Summary

Pakistan is an agricultural country,and agricultural industry is the backbone of our economy.As we boost our agriculture industry it will boost our economy directly.

Wheat is the Major crop of the Pakistan and Pakistan is 8th largest producer of wheat in the world there are lots of diseases effect the production of wheat in Pakistan,yellow rust is the one of the major disease.Detection and preventation from these diseases is an other long process for the formers.there is need of implemention of modern technology to make robust systems for the number of applications in agriculture.Remote sensing in the precision agriculture is taking Milestones from last decades unmanned aerial vehicles are used in lot of application of agriculture, forestry and surveillance purposes.

we are designing and implementing a  unmanned aerial vehicle system which monitors the yellow rust in wheat.There are number of sensors and camera's are used for imaging with their specific accuracy in specific applications.Hyper Spectral imaging like other Spectral imaging collects and processes information from across the electromagnetic spectrum.The goal of hyper spectral imaging is to obtain the spectrum for each pixel in the image of seen,with the purpose of finding objects, identifying materials or detecting process.Where as the human eye sees color of light in mostly three bands,Spectral imaging divides the Spectrum into many more bands this technique of dividing images into bands can be extended beyond the visible.

The use of a low-cost five-band multi spectral camera (RedEdge, MicaSense, USA) and a low-altitude airborne platform is investigated for the detection of plant stress caused by yellow rust disease in winter wheat for sustainable agriculture. Mainly focused on: (i) determining whether or not healthy and yellow rust infected wheat plants can be discriminated; (ii) selecting spectral band and Spectral Vegetation Index (SVI) with a strong discriminating capability; (iii) developing a low-cost yellow rust monitoring system for use at farmland scales. The learnt system was also applied to the whole farmland of interest with a promising monitoring result. It is anticipated that this study by seamlessly integrating low-cost multi spectral camera, low-altitude UAV platform and machine learning techniques paves the way for yellow rust monitoring at farmland scales.

Project Objectives

The aim of this research is to explore the combination of UAV based spectral data and data analysis method to detect plant disease on wheat
crop.

The objectives are as under:

• To study UAV remote sensing system for healthy and diseased crop i-e yellow rust infected wheat plants.
• To select spectral Vegetation Index that best differentiate healthy and diseased wheat plant.
• To develop a low-cost and easily-deployed UAV remote sensing system for yellow rust monitoring at farmland scales.
• To analyze the data obtained from UAV remote sensing system for healthy and diseased crop.

Project Implementation Method

Systems and Methods

Wheat plants under various levels of yellow rust infection generally have different spectral reflectance values. In this work, the problem of discriminating various wheat plants is formulated as a classification problem. Therefore, different elements for supervised classification are to be introduced such as data ground truthing, feature extraction, classifier selection and performance metric. In addition, to select spectral band and SVI that best differentiate healthy and yellow rust infected wheat plants, feature ranking algorithm is also adopted in this work.Aerial images for wheat field are first preprocessed by Pix4D software where features can be extracted including spectral bands and SVIs. Ground truth data is also collected from wheat field to label the image so that a labelled dataset is available for classifier training. Then random forest is adopted as the classifier, where its hyperparameters are fine tuned to guarantee satisfying performance by using Bayesian optimization. In model application to the FOI, the pre-processed aerial image by Pix4D can be classified by the trained classifier so that a yellow rust disease severity map can be generated.

 Ground truth data

Ground truth labelling is indispensable in supervised learning (e.g. classification or regression). Ground truth data for wheat yellow rust severity was collected via visual inspection by experts of yellow rust

Features: spectral bands and SVIs Features,

Describing data characteristics, are of vital importance for machine learning applications, directly affecting algorithm performance. To maximally represent data characteristics, raw band reflectance and SVIs for wheat plants are considered concurrently for feature extraction. SVI is a simple but vital approach for extracting useful information from remotely sensed data.It is noted that different from SVIs for hyperspectral images, the SVIs for multispectral image are limited to the existing wide bands of the RedEdge camera.

Random forest classifier with Bayesian optimization

With feature vector and label being defined, the next step in classification tasks is to build a classifier from labelled data so that class label for new data can be automatically determined.In this work, state-of-the-art random forest classifier is adopted due to its good performance in term of accuracy and robustness and a relatively low computation load, where its hyperparameters are automatically tuned by using Bayesian optimization.

Feature ranking

It is also of great importance to identify the spectral band or SVI that has the strongest discriminating ability for yellow rust detection.Feature scoring algorithms are adopted due to its simplicity and effectiveness, which can generate a score for each feature reflecting its importance.

Benefits of the Project

Detection of disease is a laborious task and late detection of disease will cause the very solid effects on the production of crop. our designed project will reduce the effort of farmer and it will reduce the damage rate of yellow rust in wheat if it is detected earlyer.All the pesticides will control the disease in its starting stage if any decision are taken earlier then pesticide get a good control on the disease. our goal is to detect the disease  in its early stage and use the pesticides  against the disease in its childhood.After all our desired milestones are listed below.

• Detecting automatically and precisely
• Reducing human efforts
• Quality Wheat production
• Economic growth of nation
• Poverty reduction

Technical Details of Final Deliverable

 UAV-Camera system for multispectral imagery

 5-band multispectral camera (RedEdge, MicaSense Inc., Seattle, USA), constitute a platform of low altitude UAV-camera system.The weight, dimensions, image resolution of RedEdge camera are 135 g, 5.9 cm 4.1 cm 3.0 cm × × and 1280 960 × pixels, respectively. RedEdge camera, equipped with GPS, can capture five spectral images simultaneously In each flight, RedEdge camera was fixed on a gimbal, pointing vertically downwards to guarantee aerial image quality. Flight altitude was set at 16–24 meters above ground, providing images with a ground spatial resolution of 1–1.5 cm/pixel. A laptop installed with Ground Control Station was used to plan, monitor and control the UAV flight. The planned flight path and velocity, and camera triggering are designed so that the consecutive images with overlap and sidelap up to 75 % can be obtained for the purpose of accurate orthomosaic generation. An image of a reflectance calibration panel was always taken at about 1 m height immediately before and after each flight to account for camera characteristics, reflectance characteristics and the effects of environmental variations. Each UAV aerial image contains necessary information for camera calibration and image stitching such as camera information (e.g. exposure time, ISO speed, focal length, black level), GPS and IMU (i.e. Latitude, Longitude, Altitude, Yaw, Pitch and Roll)

Pix4D Mapper for image preprocessing

Image pre-processing to generate calibrated and georeferenced spectral reflectance and SVIs was performed by using commercial Pix4Dmapper software which include initial processing (e.g. keypoint computation for image matching), orthomosaic generation and index calculation (with reflectance calibration). Each layer output (e.g. spectral reflectance for each band and various SVIs) is a single high-resolution GeoTIFF image of the whole site. GeoTIFF images were further post-processed in Matlab to define the common Region Of Interest (ROI) for various image layers so that follow-up analysis (e.g. spectral analysis and supervised classification) can be performed.

Final Deliverable of the Project

HW/SW integrated system

Core Industry

Agriculture

Other Industries

IT

Core Technology

Robotics

Other Technologies

Robotics

Sustainable Development Goals

No Poverty, Decent Work and Economic Growth

Required Resources

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