## 5 Conclusions  
In this paper, we evaluated SAgric-IoT, an IoT platform, in terms of packet delivery ratio and energy consumption in a small-scale network under a controlled environment (laboratory) and test bed conditions in a field, including its embedded system, the communication protocol, and its algorithms. SAgric-IoT reduces the intensive human involvement required in current agricultural information collection systems and provides information in a faster way.  
Results proved that SAgric-IoT is superior in energy saving and reliability of transmission. On the other hand, the CNN model for identifying diseases in the crop has high levels of accuracy, with average values greater than 95%. This value has an acceptable reliability level for detecting early disease problems affecting the yield. We conclude that SAgric-IoT is a viable solution to improve precision agriculture because it is a wireless platform that uses low-power algorithms for gathering data.  
Figure 14: Sample predicted images using the proposed model.  
Figure 15: Performance comparison of the proposed model and existing models.  
Our future work focuses on the platform's security to avoid integrity problems with the collected data that cause a reduction in the quality or quantity of production.  
Another significant challenge to solve in the next phase is to go from a model trained with images taken in the laboratory to images generated directly in the sowing field. Due to the lack of context and diversity in plant appearance, deep learning models trained on individual leaf photographs may need help identifying plant diseases in full plant images. However, several studies have shown that the use of a combination of leaf and full plant images, and the use of deep learning techniques, can improve the accuracy of plant disease detection.  
For example, a study [97] to detect tomato plant diseases used a convolutional neural network (CNN) trained with laboratory-taken leaf images and real-world leaf images. The real-world leaf includes other elements, such as stems and other leaves. With this combination of training, they achieved an accuracy rate of 99.53%. Another recent study [98] used various images from a leaf dataset and manually taken photos, along with deep learning techniques, to detect diseases in grapevine plants and achieved over 80% accuracy in this mixed scenario.  
In summary, a deep learning model trained only with images of a single leaf may have difficulties recognizing diseases in plants in pictures of a whole plant. Still, the combination of leaf and entire plant images, as well as deep learning techniques, can significantly improve accuracy in disease detection in plants.  
In future work, the model will be trained with complete plant images and implemented in the real-time plant monitoring scenario. This way, it will be possible to check the crops' conditions by leaf samples and directly monitor the plants from the images captured by the camera system, fully integrating it into the SAgric-IoT platform.  
Conceptualization, J.A.G.-I. and J.C.-C.; methodology, P.C.S.-M. and L.A.-R.; formal analysis, J.A.G.-I., J.C.-C. and P.C.S.-M.; data curation, L.A.-R.; writing--original draft preparation, J.A.G.-I. and J.C.-C.; writing--review and editing, P.C.S.-M. and L.A.-R. All authors have read and agreed to the published version of the manuscript.  
This research received no external funding.  
Not applicable.  
The data presented in this study are available on request from the corresponding author.  
The authors declare no conflict of interest.