# Weed Detection in Wheat Crops Using Image Analysis and Artificial Intelligence (AI)
## 2 Literature Review
Allocating the appropriate herbicide dosages in the correct location and at the right time is one approach for reducing costs and environmental consequences (precision agriculture). Unmanned aerial vehicles (UAV) are becoming an attractive acquisition approach for weed optimization and control due to their capacity to capture pictures of the whole agricultural field area with extremely fine spectral resolution and at a reasonable cost. Despite the substantial advancements in UAV acquisition systems, automated weed detection remains difficult because of weeds' strong similarity to crops. The latest deep learning technique has shown promising results in various complex classification problems. This method, however, requires a certain amount of time in a training phase; it is highly time-consuming to create large agricultural datasets with expert pixel-level annotations. This paper presents a new automated learning approach for weed detection from UAV images, utilizing convolutional neuronal networks (CNNs) and unmonitored training dataset selection. The planned system is divided into three major stages. We begin by automatically detecting crop lines and using them to define interline weeds. The second phase uses interline weeds to create the training dataset. Finally, to create a model capable of detecting the crops and weeds in the photos, we performed CNNs on this dataset. The obtained results are close to conventional supervised labeling of training data. In the field of spinach, the precision gaps are 1.5%, and are 6% in the bean field. The work [20] seeks to thoroughly map the latest technology of weed mapping in crop fields from aerial agricultural pictures. Four digital repositories were checked: Science Direct, Springer. IEEExplore, ArXiv, and Link. We were capable of identifying, with this research, what kind of approaches and techniques have been utilized in this particular field in the last 10 years. Four main points are explored in the reviewed research articles: the form of segmentation, method of classification, space of features, and culture addressed. The paper [21] provides a detailed research survey on the implementation of artificial intelligence approaches in agriculture farming. There are many problems in the world of agriculture, including infestation of diseases and pests, insufficient soil management, in adequate irrigation and drainage, and much more. All these lead to substantial crop loss, including environmental challenges resulting from the severe and unnecessary usage of chemicals. There have been some academic reports carried out to solve these problems. The artificial intelligence field, with its robust learning abilities intelligence, has become a primary technique for tackling challenges related to agriculture. In order to assist agricultural specialists, technologies for effective applications are being built throughout the world. This research survey assesses 100 major contributions, in which artificial intelligence strategies were used to tackle the issues associated with agriculture farming. This paper deals with the introduction of artificial intelligent approaches to the vast agricultural subdomain, so that readers can capture the multi-dimensional advancement of agro-intelligent systems over 34 years, from 1983 to 2017. The paper [9] discusses that one of the most significant ways to assess apple growth phases and calculate output in orchards is the real-time detection of apples. Apples alter their size, color, cluster density, and other growth features as they mature. Conventional detection methods can only identify apple plants at a specific stage of development, and they cannot be modified to various phases of development using the same model. In orchards with changing light, complicated backdrops, overlapping apples, and branches and leaves, we present an enhanced YOLO-V3 model for identifying apples throughout different growth phases. Images of immature, growing, and ripe apples were initially gathered.