# Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
## 2 Literature Review  
In [13], a useful technology for efficient farming-related implementations is outlined: UAV (unmanned aerial vehicles). Aerial surveillance of UAV farms in agriculture allows for crucial decision-making on the monitoring of crops. Developments have further improved the accuracy and reliability of aerial imagery-based research in deep learning models. Remote crop assessment applications such as plant categorization and aggregation, crop counting, yield estimate and comparison, weed identification, disease detection, crop mapping, and nutrient deficiencies, among others, may accommodate various types of sensors (spectral cameras, RGB) on UAVs. UAVs are abundantly used to practice farming activities, and are cited by several researchers. An analysis of research studies adapting deep learning to UAV imagery for deficient farming is provided in this report. Depending on the requirement, these studies were categorized into five main classes, which include identification of vegetation, classification and segmentation, crop estimation and yield expectations, crop mapping, crop disease and detection of weeds, and detection of inadequate intake. A comprehensive analysis of each area of research is presented. In [17], the use of fungicides and herbicides in agriculture can be substantially reduced or even eliminated using precision weeding. The specific selection of weeds, low-cost plant identification, and high speed are necessary to achieve high precision. Using combined red, green, and near-infrared reflectance, the current system is combined with a size differentiation system to classify weeds and crops in lettuce fields. LED arrays are presented at 525, 650, and 850 nm, and pictures are captured in a single shot using a modern RGB camera. A kinematic stereo system is used to compensate for parallax errors in pictures, and the precise position data of plants are presented. The scheme was tested within three lettuce fields ranging from 0.5 to 10 km/h at various growth stages in field trials around fields. The results of in-field trials showed values of 69% and 56%, respectively, in crop and weed identification. Post-trial processing led to average crop and weed processing values of 88% and 81%, respectively. The demand for wheat is also growing as the world's population grows [18]. It is essential to monitor weeds in wheat crops and the barren/wasteland areas in order to decrease the production of weeds so that wheat productivity can be enhanced. The important variable evaluated was the detection of weeds. An unmanned aerial vehicle (UAV) is utilized in various stages of gathering wheat crop data to capture high-quality RGB pictures. The recommended backdrop subtraction approach speeds up the processing of weeds, wheat, and barren land inside the wheat crop region. The results show that background subtraction is a good method for detecting weeds, barren land, and wheat. In [14], pictures were taken, and the study describes a machine vision system for weed detection in the field of vegetable crops, while avoiding illumination and sharpness difficulties throughout the acquisition phase. This design will serve as a foundation for a mobile weed removal robot with a camera obscura (Latin for \"dark room\") in light-controlled areas. This research study aims to build a successful weed discrimination algorithm by using image filtering to remove color and area characteristics, then implementing a method to mark each item in the scene, and finally recommending a region-based classification involving specificity, sensitivity, and positive and negative estimated parameters to evaluate the algorithm. The authors of [19] stated that weed control in modern agriculture usually consists of pouring herbicides all around the agricultural field. Substantial waste and herbicide rates for farmers and environmental degradation are involved in this activity.