## 7 Conclusions and Future Work  
In this paper, we raised the interest of DL model compression in the application to plant disease detection in agriculture. We discussed the DL inference on low-resources devices, then we \begin{table}
\begin{tabular}{c c c c c c} \hline
**AlexNet+GAP** & **Original** & **75\% Pruning** & **80\% Pruning** & **85\% Pruning** & **90\% Pruning** \\ \hline \multicolumn{5}{c}{**Filter-level pruning**} \\ \hline Size & 2.93 Mb & 0.23 Mb & 0.17 Mb & 0.11 Mb & 0.08 Mb \\ \hline FLOPs & 1,509,781 & 96,525 & 64,365 & 37,017 & 17,391 \\ \hline Accuracy & **88.50** & 86.52 & 85.80 & 77.72 & 72.33 \\ \hline \multicolumn{5}{c}{**Pruning + postquantization**} \\ \hline Size & 0.73 Mb & 0.06 Mb & 0.04 Mb & 0.03 Mb & 0.02 Mb \\ \hline Accuracy & **88.31** & 86.16 & 86.25 & 77.62 & 72.32 \\ \hline \end{tabular}
\end{table} 
Table 10: Comparison in terms of size and Flops of filter-level pruning on AlexNet+GAP with the plant seedlings dataset. \begin{table}
\begin{tabular}{c c c c} \hline
**VGGNet16 Input Size** & **Size** & **Accuracy** \\ \hline \(224\,\times\,224\,\times\,3\) & 512.91 Mb & 93.19 \\ \hline \(100\,\times\,100\,\times\,3\) & 248.91 Mb & **94.24** \\ \hline \(50\,\times\,50\,\times\,3\) & 152.91 Mb & 93.97 \\ \hline \end{tabular}
\end{table} 
Table 12: Size of VGGNet16 with different sizes of the input image from the plant seedlings dataset. \begin{table}
\begin{tabular}{c c c c} \hline
**LeNet5+GAP** & **Original** & **75\% Pruning** & **80\% Pruning** & **85\% Pruning** & **90\% Pruning** \\ \hline \multicolumn{5}{c}{**Filter-level pruning**} \\ \hline Size & 349.97 Kb & 63.55 Kb & 52.8 Kb & 43.8 Kb & 36.8 Kb \\ \hline FLOPs & 167,367 & 20,403 & 15,787 & 10,863 & 6997 \\ \hline Accuracy & **83.47** & 71.07 & 67.65 & 70.08 & 57.95 \\ \hline \multicolumn{5}{c}{**Pruning + postquantization**} \\ \hline Size & 86.05 Kb & 15.42 Kb & 12.95 Kb & 12.66 Kb & 9.23 Kb \\ \hline Accuracy & **83.55** & 71.06 & 68.10 & 69.36 & 54.80 \\ \hline \end{tabular}
\end{table} 
Table 11: Comparison in terms of size and Flops of filter-level pruning on LeNet5+GAP with th plant plant seedlings dataset. \begin{table}
\begin{tabular}{c c c c} \hline
**VGGNet16 Input Size** & **Size** & **Accuracy** \\ \hline \(224\,\times\,224\,\times\,3\) & 512.91 Mb & 93.19 \\ \hline \(100\,\times\,100\,\times\,3\) & 248.91 Mb & **94.24** \\ \hline \(50\,\times\,50\,\times\,3\) & 152.91 Mb & 93.97 \\ \hline \end{tabular}
\end{table} 
Table 12: Size of VGGNet16 with different sizes of the input image from the plant seedlings dataset. \begin{table}
\begin{tabular}{c c c c c} \hline
**VGGNet+GAP** & **Original** & **75\% Pruning** & **80\% Pruning** & **85\% Pruning** & **90\% Pruning** \\ \hline \multicolumn{5}{c}{**Filter-level pruning**} \\ \hline Size & 56.34 Mb & 3.56 Mb & 2.33 Mb & 1.34 Mb & 0.64 Mb \\ \hline FLOPs & 29.485,986 & 1,820,340 & 1,179,450 & 658,006 & 296,298 \\ \hline Accuracy & 88.68 & **90.39** & 88.59 & 87.24 & 89.58 \\ \hline \multicolumn{5}{c}{**Pruning + postquantization**} \\ \hline Size & 14.09 Mb & 0.89 Mb & 0.58 Mb & 0.33 Mb & 0.16 Mb \\ \hline Accuracy & 88.85 & **90.38** & 88.40 & 87.33 & 89.84 \\ \hline \end{tabular}
\end{table} 
Table 9: Comparison in terms of size and FLOPs of filter-level pruning on VGGNet+GAP with the plant seedlings dataset. summarized important techniques for size compression and consequently times speed-up which have been applied to allow DL on devices with limited resources. To evaluate the performance of model compression, we applied two pruning methods, combined with quantization to compress LeNet5, VGG16, and AlexNet on two databases used in agriculture. The results show on one hand that it is possible with fine-grained pruning to compress the size of these models by an average factor of 38 by pruning 95% of the model. Additionally, that in some cases only 1% of the weight of the fully connected layers can be used. However, deployment requires the use of specialized hardware.