Weed Detection and Control Using Mask-R-CNN

RECENT TRANSDISCIPLINARY DEEP LEARNING RESEARCH HAS INCREASED INTEREST IN USING THE AGRICULTURAL INDUSTRY. WEED MANAGEMENT AND CONTROL ARE SOME OF MOST CRUCIAL TASKS AGRICULTURE TO MAINTAIN OPTIMUM CROP PRODUCTIVITY. CORRECTLY IDENTIFYING UNDESIRABLE PLANTS MUST COME BEFORE PRESENTING A PRACTICAL PLAN ORDER MANAGE WEEDS. IMAGES COMPLEX CONTAIN ELEMENTS LIKE SIMILAR COLOR TEXTURE, THEREFORE WE NEED APPLY NEURAL NETWORK THAT MAKES USE PIXEL-WISE GROUPING IDENTIFY PLANT TYPE. THIS THESIS, FIELD PICTURES AERIAL IMAGES, ASSESSED PERFORMANCE MASK R-CNN, ONE USED NETWORKS, FOR RECOGNITION (DETECTION CLASSIFICATION). R-CNN WAS CREATED ADDRESS ISSUES WITH INSTANCE SEGMENTATION (PIXEL-WISE ANALYSIS). EMPLOYED CROP/WEED IMAGE DATASET (CWFID) DISTINGUISH BETWEEN DURING ANALYSIS. CWFID'S LIMITATIONS, HOWEVER, IT GROUPS ALL INTO SINGLE CLASS REQUIRES FROM ORGANIC CARROT FIELD. SOLVE PROBLEM, SYNTHETIC 80 DIFFERENT SPECIES R-CNN. Index Terms— Mask-R-CNN, Instance Segmentation, Semantic Fully Convolutional Network (FCN).