# Assessment of Smart Mechatronics Applications in Agriculture: A Review
## 4 Results and Discussion
### Smart Mechatronics Developments Worldwide
The many papers retrieved were related to one another by key words, as shown in Table 1. The variance was calculated as the square of deviation from the mean among the papers from which we extracted data. The variability between or within different databases was measured using sum of squares (SS). Degrees of freedom (DOF) in this context is the number of databases minus one, which gives us the number of databases between the journals. Mean square (MS) is the average variation in journals or data sources. Mathematically:  
\[\sum_{i=1}^{n}\left(Y_{i}-\overset{-}{Y}\right) \tag{1}\]  
where \(n\) is the number of observations, \(Y_{i}\) value in a sample, and \(\overset{-}{Y}\) mean value of a sample. \[MS=\frac{SS}{DOF} \tag{2}\]  
\[F=\frac{MS\text{ within data base group}}{\text{MS between data base group}} \tag{3}\]  
where the \(F\) statistic shows one-way analysis of variation. The \(p\)-value was greater than 5% in all cases, indicating that there was no significant difference in terms of key words yielding hits in the different databases as demonstrated in the Table 2.  
Table 3 provides illustrations for the probability values. This was discovered by a pairwise comparison of the variation of publications found across various publishing companies (\(p<0.05\)). \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Publisher** & **Count** & **Sum** & **Average** & **Variance** \\ \hline Elsevier & 6 & 30 & 5 & 25.2 \\ Science Director & 6 & 26 & 4.33 & 18.67 \\ Springer & 6 & 30 & 5 & 26.4 \\ Taylor \& Francis & 6 & 24 & 4 & 16.4 \\ Academic Journals & 6 & 16 & 2.67 & 7.47 \\ MDPI & 6 & 20 & 3.33 & 11.07 \\ Wiley Online Library & 6 & 12 & 2 & 4.4 \\ Natural Publishing Group & 6 & 18 & 3 & 9.2 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Summary of results of analysis of variance on single factors. \begin{table}
\begin{tabular}{c c c} \hline \hline
**Source of Variation** & **Between Groups** & **Within Groups** \\ \hline SS & 50.67 & 594 \\ DF & 7 & 40 \\ MS & 7.24 & 14.85 \\ F & 0.49 & - \\ \(p\)-value & 0.84 & - \\ F-Critical & 2.25 & - \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Results of analysis of variance between and within publishing groups. Table 4 shows the results of AI in the agricultural sector. For convenience, it was compared. The ultimate goal of data generation and collection is to use it. In agriculture, data analytics can lead to significant productivity gains and cost savings. By combining AI with big data, farmers can receive effective recommendations based on well-classified, real-time crop demand information. This takes the guesswork out of it and enables more precise agricultural practices, such as irrigation, fertilization, crop protection, and harvesting. Farmers can use IoT sensors and other assistive technologies (such as drones, GIS, and other tools) to monitor, measure, and store real-time field data on various metrics. Combining AI farming tools with IoT devices and software will help farmers obtain more accurate information faster. Better data means better decisions and less time and money spent on trial and error. Combining artificial intelligence with autonomous tractors and the IoT can solve one of the most common problems in agriculture. Labor shortage. These technologies are potentially more cost-effective due to their higher accuracy and reduced errors. Taken together, AI, autonomous tractors, and the IoT are key to precision agriculture. Another technology that is less common but is growing rapidly is robotics. Agricultural robots are already being used for manual tasks, such as picking fruits and vegetables and thinning lettuce. The advantages of robots over farm workers are significant. Work longer, be more accurate, and be less error prone.