# Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
###### Abstract
They classified weeds with an accuracy of more than 95% using DCNN models. [PERSON] et al. [12] described an extraordinary organization accuracy of 99% when using CNNs to distinguish between weeds and sugar beets. From linear regression to CNNs, the dataset using which a machine learning model can be presented is constrained. The literature contains many grasses and plant life image datasets. The Annual Life CLEF Plant Identification Challenge 2015 Dataset 25 features 113,205 pictures with 41,794 explanations of 41,794 trees. This vast dataset is unique compared with most other work sites that offer particular datasets of scientific interest. All of these approaches provide high-ranking accuracy for target datasets. However, most datasets capture plant life to the best of their ability under perfect lab conditions. Although perfect lab conditions allow strong theoretical classification results, determining a classification model on a grass control robot requires an image dataset that captures plants in realistic environmental conditions [13]. Agriculture productivity is predicted to increase significantly with robotic grass control. The main benefits of an autonomous weed control system are lower labor costs and the potential to use fewer herbs, while using weeds more effectively. Improving the efficacy of weed control will have a huge economic impact. Farmers are thought to lose US 2.5 billion each year due to adversely impacted agricultural productivity, and to spend USD 1.5 billion on grass management efforts in Australia alone. Agricultural robot technology offers the opportunity to eliminate these losses and boost output [14]. In comparison to variable rate application (VRA), uniform application of agrochemicals (UA) causes over-application of harmful chemicals, greater agricultural input costs, and environmental deterioration (VA). The design, implementation, and testing of a smart variable ratio sprinkler (SVRS) for VA pesticide application uses deep learning (DL). The SVRS is used to quickly identify healthy potato plants and those affected by early blight, lambs' quarter's weed, and other pests. Approximately 24,000 images of farmlands in Prince Edward Island and New Brunswick were shot in a range of bright, overcast, and partly cloudy situations to present and train the YOLOV3 and mini YOLOV3 models. The tiny YOLOV3 was chosen for SVRS improvements because of its increased performance. The two spraying methods (UA and VA) and three weather conditions (cloudy, partly cloudy, and sunny) functioned as the two independent factors in a factorial laboratory experiment, with spray volume consumption acting as the response variable [15]. The development of optical techniques and artificial intelligence to distinguish crop plants and weeds is a critical step toward the automation of nonchemical herbal control systems in agriculture and decreasing chemical use through spot spraying. Large-scale bold spraying of chemical herbicides is not only a waste of herbal medicines and labor, but also a cause of environmental pollution and food quality problems. Traditional methods are concerned with the need for high light and sample quality. Therefore, proper identification of weeds and precise sprays are important strategies for promoting sustainable agricultural development. To avoid the effect of different light on the pictures, the color model and then the gray picture component are suggested. The method of vertical projection and the method of linear scanning have been developed to identify the main line of crop rows quickly. To reduce the complexity of the math, the Classical view efficiency rate (WIR) has been modified, and a better method of horizontal scanning has been adopted to make calculations within the cells. Finally, the revised broad inflation rate (MWIR) is typically used to capture real-time decisions through the minimum error ratio of the basin decision under distribution [16].