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+in+agricultural+fields+is+critical+for+farmers+to+ensure+maximum+productivity.+However%252C+stresses+show+up+in+different+shapes+and+sizes+across+different+crop+types+and+varieties.+Hence%252C+this+is+posed+as+an+anomaly+detection+task+in+agricultural+images.+Accurate+anomaly+detection+in+agricultural+UAV+images+is+vital+for+early+identification+of+field+irregularities.+Traditional+supervised+learning+faces+challenges+in+adapting+to+diverse+anomalies%252C+necessitating+extensive+annotated+data.+In+this+work%252C+we+overcome+this+limitation+with+self-supervised+learning+using+a+masked+image+modeling+approach.+Masked+Autoencoders+(MAE)+extract+meaningful+normal+features+from+unlabeled+image+samples+which+produces+high+reconstruction+error+for+the+abnormal+pixels+during+reconstruction.+To+remove+the+need+of+using+only+%60%60normal+data+while+training%252C+we+use+an+anomaly+suppression+loss+mechanism+that+effectively+minimizes+the+reconstruction+of+anomalous+pixels+and+allows+the+model+to+learn+anomalous+areas+without+explicitly+separating+%60%60normal+images+for+training.+Evaluation+on+the+Agriculture-Vision+data+challenge+shows+a+mIOU+score+improvement+in+comparison+to+prior+state+of+the+art+in+unsupervised+and+self-supervised+methods.+A+single+model+generalizes+across+all+the+anomaly+categories+in+the+Agri-Vision+Challenge+Dataset&cdnurl=https%3a%2f%2fth.bing.com%2fth%2fid%2fR.86687e1da0d0e90b981b3b2113bd3211%3frik%3d8Efllm%252fAocu6lA%26pid%3dImgRaw%26r%3d0&exph=630&expw=1200&q=Free+Anomaly+Flow+Diagrams&simid=608021568949848325&FORM=IRPRST&ck=5606AA256E469F01219A1F48CB103660&selectedIndex=9&itb=0)
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