## 3 Information and Communication Technology Solutions for Precision Agriculture
### Data Analysis and Artificial Intelligence
#### 3.2.3 Computer Vision  
Computer vision is the main constituent element of many ICT solutions for PA. The main idea behind computer vision is to enable computers to understand diverse environments based on visual information. Image acquisition and image processing/analysis are the main components of computer vision systems. Image acquisition refers to devices (e.g., cameras) transforming electronic signals from a sensor to numerical representations. Image processing and analysis revolve around manipulating images or videos (e.g., to improve their quality and facilitate subsequent analysis) and extracting useful knowledge from them [41; 42]. After rice transplantation has taken place, rice plant numbers and density are key factors for measuring yield and quality of rice grains. [PERSON] et al. propose an image processing technique based on morphological operation for automatically determining the number of rice plants. For this purpose, images collected by UAVs using satellite navigation are analyzed. The results of the research indicated high efficiency of the proposed image processing technique [43]. [PERSON] et al. analyze how the use of convolutional neural networks and genetic algorithms in satellite image processing can facilitate PA. The results of their research showed that these technologies are beneficial for maximizing the economic factor as well as for minimizing CO\({}_{2}\) emissions and land degradation [44]. [PERSON] et al. analyze a computer vision method for fruit detection, which can be utilized by harvesting robots. The method is based on mask region convolutional neural network (mask RCNN) technologies. The specific approach enabled the accurate categorization of fruits (ripe or unripe), marked regions with bounding boxes as well as indicated the visual location of the fruit picking points, by analyzing shape and edge features of the mask images, that were generated by the method [45]. Measuring the surface soil moisture content is vital for assessing soil-plant-water interactions, studying the climate change as well as for water budget closures. [PERSON] et al. propose a new approach for estimating the soil moisture content, based on multispectral satellite imagery. The proposed moisture index uses raw image digital count (DC) data in the thermal infrared, red, and near-infrared bands [46]. [PERSON] and [PERSON] demonstrate a big data environment for soil analysis based on computed tomography (CT) images, which can contribute to the better understanding of problems of agricultural lands as well as to improving productivity and sustainability of crops. In the proposed solution, 3D reconstruction and statistical analysis algorithms are also employed [47]. [PERSON] et al. demonstrate a deep learning-based model for detecting weeds, which uses images taken by UAVs. This model uses convolutional neural networks with unsupervised training datasets. The proposed solution was found to be flexible and provided similar results with other methods using supervised datasets while not being dependent on the time-consuming task of the creation of large agricultural training datasets with annotations at a pixel level by experts [48]. Data fusion and evaluation/benchmarking techniques are frequently used in computer vision-related technologies for PA. The term data fusion refers to merging two or more representations of the same object into a single, clear, and consistent representation [49]. [PERSON] et al. present a new data fusion methodology which can be used for describing the canopy status variability. This methodology exploits information from multiple sources including: 2D multispectral imagery from UAVs, 3D point cloud crop models, and thermal imagery from UAVs. The proposed methodology was found to have higher discriminant power than methodologies utilizing a single source of data [50]. [PERSON] et al. propose a model for rapid prediction of soil classes based on the outer product analysis data fusion algorithm. Data from visible near infrared and mid-infrared spectra were fused.