# Artificial Intelligence in Agriculture: Benefits, Challenges, and Trends
## 5 Benefits, Challenges and Trends (RQ3)
\begin{table}
\begin{tabular}{c c c} \hline \hline
**Benefits** & **Challenges** & **References** \\ \hline In an unstructured environment, the algorithm Mask-RCNN & The algorithms built into this work do not extract contour and & \\ accurately recognized the categories of the objects. & shape information accurately. & [13] \\ \hline Robots and drones optimized the use of water and pesticides & Low offer and high cost of cognitive solutions that need to be & [14] \\ and increased productivity and quality. & more affordable for their popularization. & [14] \\ \hline Intelligent system tool for crop yield prediction. & System complexity. & [5] \\ \hline A low-cost system with remote monitoring was portable, & The main challenges identified are related to the dissemination & [15] \\ lightweight, and user-friendly. & and commercialization of the developed technology. & [15] \\ \hline The results showed that an increase in the dataset volume & The challenge is the use of Inception V3 and ResNet-based & [16] \\ achieved better model performance. & CNN models for a much deeper analysis of crop images is & [16] \\ \hline A responsive web application with deep learning that exploited & This work was evaluated only on a small data set about coffee & [17] \\ the collected data. & leaves & [18] \\ \hline Intelligent system for classifiers for early diagnosis of plant & As challenges, they aim to implement an intelligent service for & [18] \\ pests, reducing the consumption of agricultural pesticides, & detecting citrus pests and extend the proposed architectures to & [18] \\ saving costs, and reducing environmental pollution. & detect more classes of pests. & [19] \\ \hline As labor requirements in horticulture become more challenging, & As challenges, measured the damage or effect on kiwi quality & [19] \\ automated solutions, like the ones proposed in this work, are & by the picker and reduce the losses, which currently stand at & [24.5\%. \\ \hline The main benefit is promoting sustainable irrigation and & Identify parameters like the ratios between water and & [20] \\ fertilization management in precision agriculture. & fertilizers, their impact on the crop production function, and & [20] \\ \hline ET\({}_{0}\) was estimated for water management using ANN, ELM, & Dissemination and use of ANN, ELM, and MLR models on a & [21] \\ and MLR models. & large scale in irrigation planning and management. & [21] \\ \hline The benefits can include cost reductions, catastrophe & Agricultural researchers often small and medium farms are & [22] \\ prevention, positive economic impacts, and safer & more risk-averse. & [23] \\ human-machine interactions. & & \\ \hline Hybrid models and deep learning techniques are used for crop & The use of Neutrosophic sets to express indeterminate and & [23] \\ yield prediction. & inconsistent information that can be widely explored. & [24] \\ \hline Intelligent systems use wireless sensor networks (WSNs) that exploit the acquisition, communication, and processing of data. & The acceptance of the precision agriculture solution considering & [24] \\ \hline Intelligent systems use big data applications to predict insights &
\begin{tabular}{c} Challenges need to be addressed: data ownership and privacy; \\ data quality in real-time; intelligent processing and analytics; \\ sustainable integration of big data sources. \\ \end{tabular} & [8] \\ \hline An intelligent system using the cloud was developed to & & \\ accurately and rapidly process, analyze, and visualize data & Popularization and commercialization of the Agroview system. & [25] \\ \hline \hline \end{tabular}
\end{table} 
Table 15: Benefits and challenges identified. Robotics, deep learning, and computer vision were used in the case study paper on kiwifruit harvesting [19]. The case studies showed in [24, 25] developed systems using machine learning and IoT for crop management.