Venue: ESPC 2022 Competition

Reviews:

final_score: rating: 3: Accept. This work can be presented during the event, but it is not a contender to win the competition.
review_content: summary: The authors present a system for improving the process of irrigating the fields. 
strengths: - The problem the authors attempt to address is increasingly becoming important making their solution timely. 
- The solution involves solar powered sensors which enhances the deployability. 
- The system is well thought and considers various use cases such as animal and bird detection. 
- The authors have a working prototype and the design is detailed. 
weaknesses: - Some acronyms need to be elaborated. What is an NPK?
- The video could be improved and the code documentation as well. It is not clear how the device shown in the video is related to the device in the report. 
- The authors do not describe why the accuracy is low (50%). What are the factors that affect the accuracy? 
- It is also not clear which public datasets the authors have explored before falling to back to the images they capture. Specifically, why cant you use existing models or data sets for training? 
- The details of the protocols used and the various components through which the data traverses, from the collection to storage, is largely missing.
review_title: null

final_score: rating: 3: Accept. This work can be presented during the event, but it is not a contender to win the competition.
review_content: summary: The authors aim at providing a smart irrigation system to reduce the damage caused by improper irrigation.
strengths: 1. The research topic is appropriate. 
2. The prototype and the paper are presented in a good manner.
weaknesses: 1. Typos exist in the reports.
2. It does not mention there is temperature and humidity sensors in the hardware part, while in section 5 pcb gives those measurements. 
3. It is not so clear for the SVM model building part, what are the input features and what is the output. Details are missing in the model building part, which are very important. The inputs probably are the image, temperature, and humidity and the output is the moisture level (based on soil moisture). The criteria of how to divided into three different moisture level is not given.
review_title: null

final_score: rating: 4: Accept + Potential Winner. This work can be presented and it is a strong contender to win this competition.
review_content: summary: This project is based on implementation of Smart irrigation system to increase the crop yield. 
strengths: 1. Project report as been written in well manner.
2. Demonstrated the field demo.

weaknesses: 1. There are typos in the report.
2. IoT protocols details are missing
review_title: null

final_score: rating: 3: Accept. This work can be presented during the event, but it is not a contender to win the competition.
review_content: summary: Better smart irrigation systems are needed for climate change resilience. This applies to agribusiness, small-scale agriculture and household gardens and parks.  The application of energy harvesting solar panels is an attractive proposition to power environmental monitoring sensors. Smart irrigation system provides a research contribution to need. The prototype is attractive and Machine Learning is used to predict more water-efficient irrigation. However, soil-moisture monitoring systems are well established in the research literature and there are many affordable small IoT sensors available in the market. The current solution description does not address the networking of sensor measurements and data visualisation needed for large areas such as agricultural fields. A discussion on how the solution addresses the spatial and temporal variability of soil moisture for real-life agricultural settings is needed.  
strengths: Smart irrigation system appears to be ongoing research with a history applying IoT and cloud services to monitoring soil health. This is important research area because soil moisture-based irrigation controllers help agricultural businesses achieve water-efficient irrigation scheduling. Normally, by detecting the amount of moisture beneath the landscape, Soil Moisture System s override scheduled irrigation when plants don’t need water, helping reduce water waste and promote plant health.   

The solution appears to be at research level. 

An attractive  prototype with a credible the system architecture and hardware design. It appears to be a stand alone product that can be deployed in an agricultural plot.  It is a good idea to use Solar Panel to power up the environmental sensors. 

An idea for patent application would be using Machine Learning SVM to predict Soil Moisture and predict scheduling of irrigation activities based on the historical sensor data.   
weaknesses: Smart irrigation system authors should review what is available on the market. Water saving is an important climate change research area and there are many soil moisture sensors that are affordable and easy to install. What appears to be missing are low cost soil moisture sensor networks.    

The solution appears to be at research level and similar to other products on the market. 

The prototype is not networked. Data collection is based on sd card which is impractical for real-life application. Soil moisture is very heterogeneous and there would need to be a network of sensors to adequately monitored and predict water-efficient scheduling. 
 
More work is needed to represent the spatial and temporal distribution of soil moisture variability, for example creating a heat map taking readings for 4 smart irrigation monitors.
review_title: null

Decision: Accept