# Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
## 2 Literature Review
Rotation, color stability, luster transformation, and blur processing were then applied to these pictures. Training sets were created using enhanced pictures. In the YOLO-V3 network, the dense net technique is utilized to process feature layers with poor resolution. This increases network performance by improving feature propagation and promoting feature reuse. The trained model's performance is assessed on a test dataset once it has been trained. The test results demonstrate that the suggested YOLOV3-dense model outperforms the original YOLO-V3 model and the state-of-the-art fruit identification model, the Faster R-CNN with VGG16 net model. The model's average detection time per frame at 3000 \(\times\) 3000 resolution is 0.304 s, allowing for real-time detection of apples in orchards. Furthermore, the YOLOV3-dense model can successfully offer apple identification in circumstances of overlapping apples and occlusion, and it may be used in a real-world orchard setting. The paper [22] deals with weeds known as yield reducers, which can be more economically damaging than fungi, insects, and other crop pests in many cases. Crop productivity and financial losses due to weeds (off-type plants) are key components of agricultural research that contribute to the development of effective weed control techniques. Researchers utilized data from 1581 agricultural research studies on weed control in important field crops, carried out by the ALL India Coordinated Research Project in diverse locations of Indian states between 2003 and 2021, to estimate productivity and economic losses due to weeds. The study discovered that in the case of soybeans (50-76%) and groundnuts (45-71%), there was higher variation in prospective outcome decline among the various locations than in the case of direct seeded rice (15-66%) and maize (18-65%). There was higher variation in potential yield losses among different locations in the case of direct seeded rice (15-55%) and maize (15-55%). Three factors highly influenced (p.0001) the variation in real yield losses due to weeds in farmers field, location, crop type, and soil type. There were also significant variations between various locales, crops, and soil types. Rice had the highest economic losses (USD 4420 million), followed by wheat (USD 3376 million), and soybeans (USD 1559 million). Groundnut (35.8%), soyabean (31.4%) green gram (30.8%), pearl millet (27.7%), maize (25.3%), sorghum (25.1%), sesame (23.7%), mustard (21.4%), direct seeded rice (21.4%), wheat (18.6%), and transplanted rice (21.4%) were among the ten major crops in India in which weeds caused a total economic loss of about USD 11 million (13.8%). In [23], one of the most essential components of agricultural yield is identified as weed control; determining the number and position of weeds has been a difficulty for specialists for decades. In this work, we compared three approaches to weed estimation in lettuce fields, based on deep learning image processing compared with visual assessments by experts. As a feature descriptor, one method is to utilize support vector machine (SCM) histograms of oriented gradients (HOG). In the third strategy, Mask R-CNN (a region-based convolutional neural network) was used to produce instance segmentation for each individual, whereas YOLOv3 (You Only Look Once v3) was used in the second strategy to benefit from its robust architecture for object recognition. These techniques were complemented with a backdrop subtractor that removed non-photosynthetic objects using the NDVI index (normalized difference vegetation index). Crop detection F1-scores for machine and deep learning techniques were 88%, 94%, and 94%, respectively, according to specified criteria. The identified crops were then converted to a binary mask and used with the NDVI background subtractor to identify weeds indirectly. After obtaining the marijuana image, the coverage percentage was determined using standard image processing methods.