An Efficient Approach to Detect Weed in the Maize Field Using Deep Learning

Hasan, Muhammad Tanvir; Lipu, Nahid Hasan; Miah, Md Shajeeb

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dc.contributor.author	Hasan, Muhammad Tanvir
dc.contributor.author	Lipu, Nahid Hasan
dc.contributor.author	Miah, Md Shajeeb
dc.date.accessioned	2020-12-07T11:03:54Z
dc.date.available	2020-12-07T11:03:54Z
dc.date.issued	2020-12-05
dc.identifier.uri	http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/5329
dc.description	Actualized a specialist framework dependent on picture division and this framework doesn't need any preparation it can straightforwardly apply to the extraordinary picture of maize and weed, for discovering picture thresholding this framework utilized Otsu's strategy and this master framework gets the ideal outcome utilizing AES technique (Montalvo M, 2013). Executed a picture preparing calculation to distinguish the presence of weeds in a particular site of harvests, this calculation is finished by two stages, first is wiping out those regions that are too little, second is determined the average value of yield this value is taken as the threshold for yield and weed arrangement, this calculation recognizes 96% of weeds in harvests (Tejeda, 2019). “Built-up a programmed model that utilizes an open-source realistic engine for making information algorithmically, this model gets 91.3% of exactness in yield and weeds recognition utilizing RGB Basic SegNet” (Di Cicco, 2017). Utilized a blend of wavelet features in a neural organization to give a texture-based discriminator to segment the weeds from the principal crop, this ANN model separates the weeds from sugar beet (classification rate of 93.3%) (Bakhshipour, 2017). This model utilized semantic division for exact mapping of weeds, they isolated vegetations from foundation soil, stone, dead plants, and minority class pixels are labeled in the background-segmented image, the model utilized Maximum Likelihood Classification for foundation division and utilized VGG16 and ResNet-50 for feature extraction (Asad, 2019). Built-up an Unmanned Aerial Vehicle for taking a distant picture and they utilized weed planning by object-based image analysis (OBIA) strategy which permitting them to consolidate spectral, contextual, and morphological data, among another element, for weed planning they utilized three consecutive steps, one is a classification of crop rows, second is the separation between crop plants and weeds dependent on their relative positions, and third is weed coverage mapping using a grid structure (Peña, 2013). Nowadays, deep learning algorithms like convolutional neural networks (CNN) have gotten pervasive in the area of artificial intelligence. CNN or Deep CNN (DCNN) can collect information from a picture very efficiently, it is used convolutional and pooling layer for gathering information from a picture. In weed identification, the CNN technique has been applied effectively (dos Santos Ferreira, 2017). CNN relies on the dataset size if the dataset is little there can happen an overfitting issue, in this kind of circumstance transfer learning can be successful. Transfer learning utilized the model which is pre-trained, and only the last couple of layers are prepared according to the category in the new dataset. In this work, we will utilize the notable pre-prepared CNN's model VGG-16 (Simonyan, 2014) for maize and weed image identification.	en_US
dc.description.abstract	In agriculture one of the significant difficulties is to control weed, for controlling weed it is essential to distinguish weed appropriately. The objective of this examination is to assemble a model that can recognize the weed precisely. In picture characterization Convolutional Neural Network (CNN) makes gigantic progress, CNN extract the feature of the picture, and utilizing this feature information it performed classification, CNN brings great classification result when the dataset size is huge if the size dataset is little, there can happen an overfitting issue. So Deep CNN's (DCCN) with transfer learning has engaged, VGG-16 which is pre-trained on ImageNet by transfer learning technique, just the last couple of layers of VGG-16 model were supplanted regarding the picture classes, thus, VGG-16 can accomplish great classification result bring about the little size of the dataset. In this paper we utilized a dataset that contains two sorts of pictures, one is maize and another is weed and the dataset contains more than 24 thousand pictures. In this paper we proposed a model utilizing VGG-16 architecture, this model will take a picture with no hand-create feature extraction then the model will classify the picture by getting picture information and here we change the VGG-16 last three-layer as indicated by our model after the preparation stage this model accomplishes over 99% of validation accuracy.	en_US
dc.language.iso	en	en_US
dc.publisher	Daffodil International University	en_US
dc.subject	Learning	en_US
dc.subject	Maize	en_US
dc.title	An Efficient Approach to Detect Weed in the Maize Field Using Deep Learning	en_US
dc.type	Other	en_US