# Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends
## 5 Benefits, Challenges and Trends (RQ3)
In reviews of the literature, [5, 23] used machine learning techniques for crop yield prediction, [22] used digital twins for crop management, [8] studied big data for smart farming, and [6] used deep learning and computer vision for disease and pest recognition. In the SRL analysis, we identified terms that used various AI technologies to optimize agricultural processes; the main terms identified were precision agriculture, agriculture 4.0, and smart farming. Precision agriculture is an approach in farm management that uses information technology (IT) to optimize resource usage and reduce environmental impacts [25, 49]. Precision agriculture uses remote sensing approaches in the aerial monitoring of agricultural fields and provides real-time images collected from satellites, UAVs, or manned aircraft [50]. Agriculture 4.0 or Digital Agriculture is a term referring to Industry 4.0. It represents a more efficient industry that makes full use of Big Data and new technologies to benefit the entire supply chain and produce a greater and better quantity, with less, in search of increasing food supplies and reducing waste [10, 51, 52, 53, 54]. Described as precision farming evolution, agriculture 4.0 uses automated collection, integration, and data analysis [9]. Next-generation agriculture 5.0 and 6.0 uses a deep training data set and technological advancements through robots that can target achieving both production and environmental goals [24]. Already the term \"Smart Farming\" is to the application of intelligent systems and communication technologies such as sensors, IoT, cloud-based processes, artificial intelligence, and networking in the farming system to boost farm produce [55]. Finding 3: Artificial intelligence techniques applied in the main fields of agriculture were identified, with the main benefits being the optimization of agricultural management systems, irrigation, and the identification of diseases and pests. It was observed that the increase in intelligence in agriculture could be related to the digitization and manipulation of large volumes of data, enabling the use of intelligent techniques in system optimization and planning. Computer vision was used in conjunction with robotics and Unmanned Aerial Vehicles (UAVs) for classifying crops and identifying diseases and pests. The present section provides insights into the technologies most researched. Based on the technologies identified in the 176 articles analyzed, Figure 6 shows the Top 10 most frequent technologies and terms identified. There is a relationship between the identified technologies: machine learning is the most used technique, and this technique, like deep learning and computer vision, needs data to obtain good results. We can see in the top 10: the Internet of Things, which is capable of collecting and transmitting data; big data, a knowledge area that studies how to treat, analyze and obtain information from large data sets; and cloud computing, which is a data center that makes data available over the Internet. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Benefits** & **Challenges** & **References** \\ \hline Using the CNN model for image augmentation and the accuracy rate. & As challenges, increase date fruit varieties, and adapt the model for a mobile application. & [26] \\ \hline Analysis of artificial intelligence techniques applied for agricultural disease image recognition. & Limitations like the training process being prone to over-fitting, and for each new dataset and task, the models need to be re-trained. & [6] \\ \hline \hline \end{tabular}
\end{table} 
Table 15: _Cont._Drones and unmanned aerial vehicles (UAVs) can collect a huge and complex amount of data, and using big data analytics tools and cloud computing could be utilized to increase data processing efficiency, provide data security and scalability, and reduce costs [25]. Machine learning, ANN-based, and deep learning techniques hold a promising future in crop prediction due to the amount of data from varied sources [23].