# Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
## 2 Literature Review
Finally, we used a Blan-Altman plot, infraclass correlation coefficients (ICCs), and Dunn's test to give statistical measures among each estimation and those of a group of weed specialists (human machines). We discovered that these methods improve weed coverage assessment accuracy and reduce subjectivity in human-estimated data. Authors of [8] conducted a field study, they found that outcome losses caused by six normally occurring and most plentiful weeds in wheat fields, namely _Phalaris minor_ [PERSON], _Rumex dentatus_ L., _Coronopus didymus_ (L.) [PERSON], _Medicago donticulata_ [PERSON], _Chenopodium album_ L., and _Poa annua_ L., were investigated. These weeds were cultivated in a 1:1 weed-crop ratio with two commercially armed wheat types, Inqalab 91 and Punjab 96. _P. annua_ caused the highest yield losses of 76 percent in Inqalab 91, followed by _C. Didymus_ at 75 percent, while other weeds caused yield losses of 60-70 percent. _R. dentatus_ produced the greatest yield decrease of 55 percent in Punjab 96, followed by _P. minor_ (28 percent), _M. denticulate_, _C. album_ (23 percent), _C. Didymus_ (10 percent), and _P. annua_ (10 percent). In comparison to Inqalab 91, Punjab 96 proven to be more resistant to weeds. In [15], the new technology of deep learning convolutional neural networks (CNNs) was shown to help farmers boost their efficiency by using remote sensing and automated field condition inference. This study investigated how CNNs were used to identify two weeds in pictures of wild blueberry fields: hair rescue and sheep sorrel. To control patches of these weeds, commercial herbicide sprayers apply agrochemicals uniformly. Three objects were identified, and three images were classified. Using pictures from 58 wild blueberry fields, CNNs were taught to recognize hair rescue and sheep sorrel. The CNNs were trained on pictures with a resolution of 1280 x 720 and tested at four different internal resolutions. The CNNs were retrained with progressively reduced training datasets ranging from 3780-472 images to explore the influence of dataset size on accuracy. The best object detection CNN was YOLOv3 small; it detected at least one target weed per image at 1280 \(\times\) 736 resolution, and had F-1 scores of 0.97 for hair rescue and 0.90 for sheep sorrel. The reference of the darknet for the image classification CNN was the most accurate, with F1-scores of 0.96 and 0.95 for pictures containing hair rescue and sheep sorrel, respectively, at 1280 \(\times\) 736. At the lowest resolution, 864 \(\times\) 480, MobileNetV2 produced comparable findings, with F1-scores of 0.95 for both weeds. Except for the darknet reference, the quantity of the training dataset has no influence on accuracy. This technique may be used in smart sprayer to manage individual spray treatments for specific targets, which would reduce herbicide use. The CNN will be put to the test on a smart sprayer, and an app will be developed to provide producers with field-specific data. For wild clue berry growers, using a CNN to improve agricultural efficiency will result in significant cost savings. In the paper [20], in military navigation, environmental monitoring, and civic applications, accurate and quick identification in remote sensing pictures is critical. Due to the difficulty of recognizing tiny items in remote sensing pictures, object detection technology faces greater demands and obstacles. One-stage object detectors and the two-stage object detectors based on convolutional neural networks have made considerable progress in the field of image classification and detection in recent years. The one-stage object detector outperforms the two-stage object detector in terms of detection speed, while the two-stage target detector outperforms the other in terms of detection accuracy. YOLOv3 is used as a one-stage target detector in this article to identify remote sensing images from small aircraft. Using dimension clusters, we selected appropriate anchors to cover the size distribution in our experimental data.