CROP PROTECTION AGAINST BIRDS USING DEEP LEARNING AND IOT
Abstract: Air as an empty space is in many ways a commercial use. These make it free for all animals to use. Butsometimesthesetermsareusedbyahandfulofbirdsto hurtfarmers,sowork inthisareatosave crops.Visualand spatial perception is one of the most important applications of computer vision. This is a comparison of deep learning in the state. Bird detection is an important issue for many applications such as aviation safety, bird protection and the ecological science of migratory birds. In this study, a system has been developed to detect birds in high-definition video. Requirements to consider are Convolutional Neural Networks (CNN), background visualization, contour detection and classification confusion matrix. Findings include, but are not limited to, the following, using PCA in deep features not only reduces size and thus reduces training/testing time, but also improves recognition accuracy, especially when using neural network classifiers.
Keywords: authentication, identification, image detection, biometrics, image recognition.
Keywords: Authentication, Recognition, Image detection,Biometrics,Imagerecognisation.
1. INTRODUCTION
1.1 Bird detection
Bird detection is an important issue in a variety of applications, such as aviation safety, the ecological science of birds and migratory birds. Due to the increasingnumberofflyingvehicles,birddetectionplays animportantrolein protectionfromall kindsof dangers and threats. Thousands of bird strikes are reported each year, many of which result in takeoffs, engine stalls, and other negative consequences. According to the International Civil Aviation Organization (ICAO),there were more than 25,000 bird strikes reported by civil aviation between 1988 and 1992. Bird shooting is also a big problem for soldiers. In 2006, the US Air Force reportedmorethan5,000birdstrikes.
2. THE LITERATURE SURVEY
One of the newest technologies is computerized automatic bird detection. According to Dominique Chatbot, bird studies are organized using aerial photographs and video rather than audiences. Even a shortexaminationoftheimagetakesalongtime.Thanks to advances in digital cameras and image recognition
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systems,itis nowpossibletoperformcomputerassisted bird detection in the highest quality images. The visuals of the research methods were given and the data collectedonthissubjectwereevaluated.
Birdsfocusingonagraybackgroundareoftenaffectedby thisperception,whichrequiresalargevisualfield.
Some of the methods used to measure prey can be used forbirds,butlowresolutionbirddetectionusingthermal infrared imaging is generally somewhat used for large mammals.Withcontinuedadvancesincameraanddrone technology, birdwatchers can reduce the time and resources spent watching birds by using automatic bird trackers.
Inothermethods,accordingtoJeongjinJo,theproblemof collision between airplane and bird has been studied in different ways. Deep learning techniques are currently usedinimagerecognitionresearch.
Thisarticledescribeshowtoprocessimagesandcapture birds in multiple dynamic environments using a convolutional neural network (CNN). Dynamic background is removed from body movement by prioritization and disease movement is isolated from it. The learning model was created based on the input data of the bird images in the background before processing. The authors hope to improve the accuracy of small objects using the Inception-v3 neural network model, history subtraction is a method for moving objects for viewing.
Picardie examines and categorizes various ways to perform extraction based on speed, need and accuracy. Among the methods, a combination of Gaussian (MOG) andKernelDensityEstimation(KDE)worksbetter.
Model precision. Due to KDE's memory requirements, MOG is suitable for low memory devices. An improved backgroundsubtraction,whichisabettermodelforboth simplestaticandcomplexdynamicscenes.
To identify background birds, true bird detection based on background subtraction is recommended. They capture the motion of the Gaussian Mixture Model (GMM). As mentioned earlier, MOG-based background subtraction is often used for motion detection. However, it shows some shortcomings when applied to dynamic backgrounds.Therefore,ithasbeenproposedtousedeep learningforbirddetectionforclassification.
Various object detection methods have been used to identify objects in video frames. This technique combats high computational costs, as CNNs typically have hundredsofthousandsofpotentialcandidates.Therefore, theRegion-BasedConvolutionalNeuralNetwork(R-CNN) has been proposed. To reduce computational cost, a selective searchis used to extract recommendations that should include the product. However, the computational cost is still high because the CNN is executed approximately2000timesforeachregionseparately.
Therefore, Fast R-CNN and Faster R-CNN are recommended for better detection. Product search performance. One of them is Google's Inception V3, a feature extraction module. Inception V3 achieves high performance by using 1×1 convolutions to reduce mapping and processing. Another is the machine learning-builtNASNet.
NASNetreducesthesearch spacebysearchingthe entire network location and interconnecting the units to complete the network. Along with these attempts to reduce the computational cost, lower cost object detection methods have since been proposed. YOLO and SSD can directly guess the class list using only one pipeline.Intermsofaccuracy,SSDshowsupmoreclearly than YOLO because of its multi-layer configuration and similar strategy. The concept of ResNets and MobileNets has the same purpose, which is to reduce the computationalcost.
ResNets provide the best performance in many applications through cross-linking, where the output of one layer is added directly to the output of some subsequent layers. MobileNets embed convolutional neural networks into mobile and visual applications at low cost. Based on distributed network architecture, MobileNetscreateslightanddeepneuralnetworks.Many studies use classification for bird studies. These two studiesuse backgroundsubtractionanddeeplearning to searchforbirdssimilartoours.
Ontheotherhand,theydifferinthewaytheyreducethe negative. To reduce false detection, analysis of beautiful images is used to filter out pre-candidates and different tools use convolutional neural networks (CNN), fully convolutional networks (FCNS) and super resolution depending on size. article. Both of these articles use background inference before deep learning. However, in the history of agriculture, the results of the extraction date are murkier, due to the strong attack on non-avian cropssuchasleavesandgrass.
3. METHODOLOGY.
3.1 Basic Working
Working of Bird Detection System:
Take the video input from the embedded camera in the field. Convert the video footage in the frames. Apply Image Processing and enhance the picture quality. The image pre-processing involves background subtraction phases:
Background Subtraction
The background subtraction is done with the help of calculating the Foreground mask. The foreground mask is calculated according to the [56], and the background subtraction between the current moving frame and the inputframeisperformed.
Backgroundsubtractioninvolves4essentialsteps:
Pre-processing, background modelling, detection of foreground,anddatavalidationprocess.
pre-processingisprocesswherethevideoframerawdata from the input video arrangement can be willing for the nextstage.
The background modelling consists of the regular frame where the touching object is eliminated by revising the new video frames and calculating the background model with statistical illustration. Data validation investigates the mask and eliminates the unneeded pixels from movingobjectsandsuppliestheforegroundmaskoutput.
We prefer the MOG2 method because of its soft memory consumption and soft complexity rate. For the MOG2 the backgroundisconsideredasaparametricframeandeach pixel is represented as the particular number for Gaussian function. The equation is given as :
ClassificationwithConfusionMatrix
The error Matrix is another name for the Confusion Matrix used to determine the performance of the classifiers for binary classification tasks. The confusion matrix is a square matrix that is situated with columns and rows which store a record of the actual values and predicted values. The prediction error, accuracy, precision, and recall are calculated which helps to improvethepredictionpowerofourmodel.
ConfusionMatrix
Size
Evaluation
Criteria
BodyColor
Description
Themaincriteriafortheevaluations of the birds are the size of the bird. As we have seen many of the times thatthe small birds are less harmful to the environment but these scenarioschangesmanytimessowe willbeaddingmorecriteriato.
BodyColorofthebirdplaysthevital role in the detection of the birds. This helps in the detection of harmfulspeciesbecausetheysharea similarcolorpatterninthebody.
Flyingskills
Flockofbirds
It will be able to detect the bird by the flying skills because few species differfromtheirflyingskills.
Manybirdswhicharesometimesnot harmful do cause destruction becauseoftheirflock.
When using the Box method, thinning pictures are split intoequalnumbersofboxes.Thedistancefromthebox's leftcornermaybeusedtocompareanytwoinputphotos, and the orientation can be utilised as a feature in each box. The average distances between squares are then saved as a feature vector. It is possible to compare two feature vectors by calculating their Euclidean distance fromeachother.
Therearetwokindsofimpersonators:thosewhoarereal and those who aren't. As can be seen in Figure 5, the system'shistogramandassociatedgraphicsillustratethe findings.
4. Algorithm Development
A Support Vector Machine is a machine learning algorithmthatcanbeusedforthefollowingpurposes:
•NaturalLanguageProcessing
•Classification
As mentioned earlier, it attempts to use supersegmentationforlabelvectorplanes..class.Findingthe true hyperplane is finding the saddle point of the Lagrangian function. It is equivalent to a bivariate quadraticprogram.
DVM is supposed to solve the following optimization problem. This is a tracking algorithm, so a training dataset must be used to train a classifier that needs to betestedusingatestdataset.The
SVM uses a custom kernel function to create a hyperplaneforclassification.Thesekernelsarethebasis for finding the true hyperplane among many possible hyperplanes split into a vector. The 4 basic kernels are asfollows:
•LinearKernel
•PolynomialKernel
•RadialBasisFunctionKernel
•SigmoidKernel
One of these kernel functions is used to create a separateobjectasthesubjectrequires.
Thesekernelshavethefollowingadvantages:
•Kernelfunctiontype
•Kernelfunctionparametervalues
•Editparametervalues
These values must be calculated carefully regardless of the kernel selected, because the effects are kernel. classifierandaccuracy.Anyerrorsormiscalculationsin theseresultsmayaffecttheresultsandthefinalresult.
4.2 LIBSVM
LIBSVM is a library for support vector machines. This packhasbeenproducedsince2000.
This package is designed to easily use SVMs in their applications.Thislibraryisusedinmachinelearningas wellasinmanyfields.Usedforthefollowingpurposes
•SupportVectorClassification
•SupportVectorRegression
•SingleClassSupportVectorMachines
LIBSVM implementation has two steps: first, training dataisusedtoobtainthemodel,andsecond.,themodel is used to predict data. For SVC and SVR, LIBSVM can also give the estimated result. The structure of the LIBSVMpackageisasfollows.
• Home directory: core C/C++ programs and sample files.Inparticular,thesvm.cppfileusesthetrainingand testingalgorithmsdetailedinthisdocument.
• Tools subdirectory: This subdirectory contains tools forcheckinginputdataandselectingSVMparameters.
• Other subdirectories contain prebuilt binaries and interfacesforotherlanguages/software.
CNN
A Convolutional Neural Network or CNN is a deep learning neural network designed to process data sequences such as portraits. CNNs are very interesting at extracting patterns like lines, gradients, circles, and even eyes and faces from their input images. This technology makes neural networks very powerful for computer vision. CNNs can be run directly on unprocessed images without preprocessing. Convolutional Neural Network is a type of less than 20 feedforwardneuralnetwork.
The power of convolutional neural networks comes from a special type of layer called the convolutional layer. A CNN has many layers stacked on top of each other, each capable of recognizing more images. Alphabets can be recognized using three or four layers and faces can be recognized using 25 layers. The reactionprocessistomakemachinesseetheworldlike
humans, see the world the same way, and even use the information for various tasks such as image and video recognition, image analysis and classification, media entertainment, recognition, natural language processing.Andmore.
Convolutional Neural Network Design:
The construction of a convolutional neural network is a multi-layeredfeed-forwardneural network,made by assemblingmanyunseenlayersontopofeachotherin a particular order. It is the sequential design that give permission to CNN to learn hierarchical attributes. In CNN, some of them followed by grouping layers and hidden layers are typically convolutional layers followedbyactivationlayers.
The pre-processing needed in a ConvNet is kindred to that of the related pattern of neurons in the human brain and was motivated by the organization of theVisualCortex.
Quality assessment of images of the palm vein:
If the palm vein picture supplied by the user does not satisfytheidentificationcriteria,thequalityevaluationof the image may be utilized to determine if hardware acquisition performance is adequate. It's still difficult to makeanaccurateassessmentofthequalityofapalmvein picture.
5. IMPLEMENTATION
Launch the program using the app, navigate to the start button, and launch the program file for video bird identification. The program began to function, and using the videoinput,itrecognizedthebirdsandproducedthe proper output for the farmer and system keeps on workinguntilturnedoff.
6. RESULTS:
Fig:5.2Birddetection
Choosethevideoforanalysisanddetectionofbirds.Play the video, and then you can see how many birds and varieties of bird are in your field or video. Two different category of birds are recognised simultaneously. The UI created for detections of birds and species works in the background and gives output on screen. It provides the count and start the buzzer when we have reached the count.
5.3 Video Report
Asavideoisplayed,birdsarepickedup,andwecreateda visual interface to show the birds and species. There are manycategoriesofbirds seenintherepresentation
Here, we present the result of our project where we can have a look at the classification of the birds is in 2 types harmful as well as non-harmful. In the above image one can see that crow (the bird which is black colour and having a long beak) is categorized in to the harmful bird category and pigeon (the bird having a grey colour and smallbeak)iscategorizedinnon-harmfulbirdcategory.
Inthisimagewecanhavealookatourdashboardwhere thecountaswellasbifurcationisgivenbetweenharmful andnonharmfulbirds.
7. Conclusion:
Pollination, known to playanimportant part in guarding against pests and rodents, can damage crops and lead to cropdecline,accordingtothereport"Acommonproblem in Indian husbandry." Development of Farmer Income (DFI), Inter-ministerial Report- Risk Management in Agriculture Volume 10, published by the Ministry of Agriculture,showsthatcatcallsbegetagrariandamageby damaging seeds, seeds and growth crops, causing profitable losses to the husbandry community. In numerousagro-ecologicalzonesofthenation,catcallsare knowntoseriouslyharmarangeofcropsduringtimesof weakness. raspberry damage to the crop restroom depends on numerous factors, similar as original raspberryvisibility,overallcroparea,plantingpatternsin thearea,seasonandphysicalstrengthofcatcalls.
"Softwarethatcanidentifyandclassifycatcallsandother objects is needed in the advanced world.” Saving crops and fields from colourful raspberry attacks will help us achievelessergrowthinhusbandry.Westrivetoproduce a further cost-effective and effective way for growers to cover their fields. The software helps to identify objects moving in the air near cropland, land and can descry objects veritably directly. In this design, we use background deduction and figure discovery to descry objects in videotape frames. This fashion is used to identifymovingobjects.
8. Future scope
Our project can be improvised by integrating it with more high resolution cameras and the data of more species of birds as well as extinct species. On further scale one can add a robots linked to our system as a future project. This Future projects can be done using artificialintelligenceandmachinelearning.
REFERENCES
[1]ABird Detection System - (Based on Vision), Bachelor Thesis Electrical Engineering June 2021,Preetham Notla Ganta Saaketh Reddy Sandeep Jyothula, Dept. of Mathematics & Natural Sciences Blekinge Institute of TechnologySE–37179Karlskrona.
[2]QBird Detection in Agriculture Environment using Image Processing and Neural Network, coDIT’19 | Paris, France - April 23-26, 2019.2019 6th International Conference on Control, Decision and Information Technologies (CoDIT’19) | Paris, France / April 23-26, 2019, Seolhee Lee , Miran Lee , Hyesun Jeon , Anthony Smith.
[3] Birds Identification System using Deep Learning, (IJACSA) International Journal of Advanced Computer Science and Applications, Vol. 12, No. 4, 2021, Suleyman A. Al-Showarah , Sohyb T. Al-qbailat Faculty of InformationTechnologyMutahUniversity,KarakJordan.
[4]WWww.downtoearth.org.in, https://www.downtoearth.org.in/news/agriculture/bird simpacting-agricultural-crops-a-major-concern-64588.
[5]AShahrizatShaikMohamed,NooritawatiMdTahir,and Ramli Adnan. Background modelling and background subtractionperformanceforobjectdetection.In20106th International Colloquium on Signal Processing & its Applications,pages1–6.IEEE,2010.
[6]ALiquan Zhao and Shuaiyang Li. Object detection algorithm based on improved yolov3. Electronics, 9(3):537,2020.
BIOGRAPHIES
Utkarsh sawant is pursuing the Bachelor degree (B.E.) in Computer Engineering from Smt. Indira Gandhi collegeOfEngineering,NaviMumbai.
Akash prajapati is pursuing the Bachelor degree (B.E.) in Computer Engineering from Smt.Indira Gandhi college Of Engineering ,Navi Mumbai.
Harshal ubhare is pursuing the Bachelor degree (B.E.) in Computer Engineering from Smt. Indira Gandhi collegeOfEngineering,NaviMumbai.
PROF. Rasika Shintre, Obtained the Bachelordegree(B.E.Computer)inthe year 2011 from Ramrao Adik Institute of Technology (RAIT), Nerul and Master Degree (M.E. Computer)From Bharti Vidyapeeth College Of Engineering, Navi Mumbai.She is Asst. Prof in Smt. Indira Gandhi college Of Engg.OfMumbaiUniversityandhaving about 11 years of experience. Her area of interest include Data Mining and InformationRetrieval.
