Computer vision and artificial intelligence in precision agriculture for grain crops: A systematic review

By Diego Inácio Patrício, Rafael Rieder. Computers and Electronics in Agriculture, 2018

790 citations recorded by the source corpus.

Abstract Grain production plays an important role in the global economy. In this sense, the demand for efficient and safe methods of food production is increasing. Information Technology is one of the tools to that end. Among the available tools, we highlight computer vision solutions combined with artificial intelligence algorithms that achieved important results in the detection of patterns in images. In this context, this work presents a systematic review that aims to identify the applicability of computer vision in precision agriculture for the production of the five most produced grains in the world: maize, rice, wheat, soybean, and barley. In this sense, we present 25 papers selected in the last five years with different approaches to treat aspects related to disease detection, grain quality, and phenotyping. From the results of the systematic review, it is possible to identify great opportunities, such as the exploitation of GPU (Graphics Processing Unit) and advanced artificial intelligence techniques, such as DBN (Deep Belief Networks) in the construction of robust methods of computer vision applied to precision agriculture.

Subfields: Analytical Chemistry, Ecology, Plant Science

View Paper: https://www.semanticscholar.org/paper/24696daab4cf7795fea37000108051ea6d9cab23

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Analytical Chemistry

1. Athanasios Balafoutis
2. Hao Luo
3. Okyay Kaynak
4. Dionysis D. Bochtis

In Ecology

1. Athanasios Balafoutis
2. Spyros Fountas
3. Dionysis D. Bochtis
4. Shuanglin Dong

In Plant Science

1. Athanasios Balafoutis
2. Dionysis D. Bochtis
3. Aristotelis C. Tagarakis

Citations in the corpus

• Introducing digital twins to agriculture (2021, background): “Today, technologies like artificial intelligence (Patricio and Rieder, 2018), big data (Wolfert et al., 2017) and Internet of Things (Elijah et al., 2018) find their way in practice, and start to converge.”
• Smart Farming Technology Trends: Economic and Environmental Effects, Labor Impact, and Adoption Readiness (2020, background): “Automation technologies in agriculture found high research interest with machine learning being thoroughly used for agricultural purposes [50–52], as well as computer vision and artificial intelligence [53,54], three‐dimensional (3D) imagery [55], and navigation systems for off‐road ”
• Deep learning for smart fish farming: applications, opportunities and challenges (2020, background): “Although data augmentation technology can be used to add some labeled samples to an existing dataset, when dealing with complex problems (e.g., multiclass problems with high precision requirements), more diversified training data are needed to improve accuracy (Patrício & Rieder, 2018).”
• Quo vadis artificial intelligence? (2022, background): “The quality and the scale of the datasets are the determinants of the robust performance of prediction/classification [39, 40].”
• Machine Learning in Agriculture: A Comprehensive Updated Review (2021, background): “Furthermore, other studies were concerned with the implementation of ML methods regarding the main grain crops by investigating different aspects including quality and disease detection [29].”