# Detecting Banana Plantations in the Wet Tropics, Australia, Using Aerial Photography and U-Net
## 4 Discussion  
In this paper we have presented an automated approach to mapping banana plantations using the U-Net CNN architecture [33] to assist the biosecurity response to Foc TR4. The U-Net has been successfully applied to other land uses around the world, but not at an operational level [40; 54; 55]. Until this study, there were no existing automated classifications to detect banana plantations using high-resolution aerial photography in Queensland, Australia or globally. One perceived benefit of manually mapping is that humans can draw on undefined experiences or obscure learnings, such as past, present, or regional knowledge about land uses [15]. Despite this, when comparing the CNN approach to existing methods, we found the new classification technique is more accurate (>0.94). This is consistent with other similar studies mapping land uses using deep learning [34; 40; 54] except in this study we have applied our model over a broad geographical area. We have also found the CNN method to be more rapid, allowing the automatic classification of banana plantations within hours when compared with existing methods requiring weeks of manually digitizing features. There were some misclassifications associated with blurred sections of aerial imagery due to third party post-processing, which caused some sections of banana plantations to be missed. Additionally, some papaya plantations and small areas of sugarcane crops were misclassified \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Classification** & **Total Accuracy** & **User’s Accuracy** & **Producer’s Accuracy** & **Jaccard Index** \\ \hline QLUMP Banana & & & & \\ Plantations 1  & 0.996 & 0.862 & 0.921 & 0.341 \\ U-Net Banana & & & & \\ Plantations & 0.999 & 0.983 & 0.959 & 0.943 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Accuracy assessment results showing the Jaccard Index, User’s, Producer’s, and total accuracies, assessed on independent validation data. The Total, User’s, and Producer’s accuracies were based on a stratified random sample of 9805 points, 701 of which were banana plantation points. The Jaccard Index used all the validation data for the Tully River catchment. as banana plantations. To address these issues and to create a more robust model, we suggest additional training data be generated in these problem areas to allow the CNN to better learn these features. It is important for a model classifying land use features within high resolution aerial photography to have the ability to account for these types of artefacts as past and future imagery captures are likely to contain similar issues. Stratifying the random generation of training data by land uses or targeting similar land uses (such as papaya plantations) would also ensure the CNN has enough examples of non-banana plantations, and will improve the classification result. While the U-Net has enabled greater accuracy and a more rapid land use product, it is limited by the availability of extensive training data which is a prerequisite. Initially this work was made possible using the data produced by the QLUMP methodology which was used to map the initial extent of banana plantations in 2015. This will also be the case for any future land use classes mapped using the U-Net. Therefore, there are implications for both the QLUMP and U-Net methodologies going forward. A logical option would be to iteratively update and improve the original QLUMP data, using the proposed U-Net methodology. Future work will focus on updating the location and extent of all banana plantations within the Wet Tropics to 2018 which was only partially available for this work (Figure 1). We will also be expanding this method to other land uses and commodities, developing methods for monitoring land use change and investigating if these methods can be used to detect damaged plantations either from wilt associated with Foc TR4 or as a result of wind damage from natural disaster such as Tropical Cyclones.