Keyword Spotter Model for Crop Pest and Disease Monitoring from Community Radio Data
In societies with well developed internet infrastructure, social media is the leading medium of communication for various social issues especially for breaking news situations. In rural Uganda however, public community radio is still a dominant means for news dissemination. Community radio gives audience to the general public especially to individuals living in rural areas, and thus plays an important role in giving a voice to those living in the broadcast area. It is an avenue for participatory communication and a tool relevant in both economic and social development.This is supported by the rise to ubiquity of mobile phones providing access to phone-in or text-in talk shows. In this paper, we describe an approach to analysing the readily available community radio data with machine learning-based speech keyword spotting techniques. We identify the keywords of interest related to agriculture and build models to automatically identify these keywords from audio streams. Our contribution through these techniques is a cost-efficient and effective way to monitor food security concerns particularly in rural areas. Through keyword spotting and radio talk show analysis, issues such as crop diseases, pests, drought and famine can be captured and fed into an early warning system for stakeholders and policy makers.
This paper tries to design an effective and economical model which spots keywords about pests and disease from community radio data in Luganda and English. The author collected keywords from both radio and online newspapers, set up a Siamese convolutional neural network, which includes input 1 and input 2, Resnet 50, Flatten, five-layer dense and L1 distance, trained the model on a total of 18426 samples and validated on 4607 and tested on 5759 samples via a K80 GPU on Google Colab. The paper should be rejected because: (1) The generalizability to different use cases needs to be shown. The approach is too applied. (2) The fit to ICLR is not perfect. I would expect a stronger focus on representation learning. (3) Model The paper lacks necessary explanation and clarification for the model, which is the core of the paper. For example, the part “Dense” in Siamese convolutional model is not well explained. The number of layers is up to 5, if there is no dropout, the model may have overfitting and gradient vanishing problems. (4) Result As can be seen from both model accuracy and model loss curves in Figure 1, the part of 21st -30th epochs shows an increasing accuracy of training data while that of validation data does not increase anymore and a declining loss of training data while that of validation does not decrease. This is a tendency of overfitting, which means the model should adopt 20 epochs rather than 30 epochs. (5) Incompleteness Compared with the detailed depiction of the first three parts, part 4, part 5 and part 6 are insufficient. It is necessary to complement these three parts with interpretation, explanation and discussion. Things to improve the paper that did not impact the score: (1) Language problems such as in page 3 paragraph 2: “To compliment the keywords captured from radio talkshows, we also scrapped an online local newspaper.” Here “compliment” should be “complement”, and “scrap” is semantically incorrect. (2) Page 4 part 4.1: “…according to a hierarchical feature extraction given by equation 3.” It should be equation 2. (3) Format in references: fonts are not uniformed.
You are a member of the scientific community tasked with peer review. Review the following paper title and abstract.
### Title + Abstract
Keyword Spotter Model for Crop Pest and Disease Monitoring from Community Radio Data
In societies with well developed internet infrastructure, social media is the leading medium of communication for various social issues especially for breaking news situations. In rural Uganda however, public community radio is still a dominant means for news dissemination. Community radio gives audience to the general public especially to individuals living in rural areas, and thus plays an important role in giving a voice to those living in the broadcast area. It is an avenue for participatory communication and a tool relevant in both economic and social development.This is supported by the rise to ubiquity of mobile phones providing access to phone-in or text-in talk shows. In this paper, we describe an approach to analysing the readily available community radio data with machine learning-based speech keyword spotting techniques. We identify the keywords of interest related to agriculture and build models to automatically identify these keywords from audio streams. Our contribution through these techniques is a cost-efficient and effective way to monitor food security concerns particularly in rural areas. Through keyword spotting and radio talk show analysis, issues such as crop diseases, pests, drought and famine can be captured and fed into an early warning system for stakeholders and policy makers.
user
<think>Okay, to do a decent peer review, I should start with summarizing what the work is about. This includes novelty, contributions, clarity, and impact. I might include suggestions to issues that I find critical to the usefulness of the work. Starting with novelty, there are different types of novelty. The first aspect of novelty is scope and scale. Is the work covering a broader perspective than previous works? The second aspect is methodology. Is the proposed methodology novel compared to previous methods? Do they provide any significant advantage over previous methodologies? The third aspect is results. Are the results different compared previous work? Does this lead to any novel theories or an extension of current theories? Next is clarity, I will be reading the work in depth and analyze if any sentences can be potentially ambiguous or misleading. If any sentences are ambiguous, I will state them and give a suggestion on how to rephrase such sentence or how to make more clear to the audience. Lastly, impact is important. I will attempt to predict how such work will impact the scientific community. This might include the applications of such work and how the work might transform the way we study a certain topic. Finally, I will might only mention issues that are critical. This is to keep the review as concise and helpful as possible.</think>



This paper tries to design an effective and economical model which spots keywords about pests and disease from community radio data in Luganda and English. The author collected keywords from both radio and online newspapers, set up a Siamese convolutional neural network, which includes input 1 and input 2, Resnet 50, Flatten, five-layer dense and L1 distance, trained the model on a total of 18426 samples and validated on 4607 and tested on 5759 samples via a K80 GPU on Google Colab. The paper should be rejected because: (1) The generalizability to different use cases needs to be shown. The approach is too applied. (2) The fit to ICLR is not perfect. I would expect a stronger focus on representation learning. (3) Model The paper lacks necessary explanation and clarification for the model, which is the core of the paper. For example, the part “Dense” in Siamese convolutional model is not well explained. The number of layers is up to 5, if there is no dropout, the model may have overfitting and gradient vanishing problems. (4) Result As can be seen from both model accuracy and model loss curves in Figure 1, the part of 21st -30th epochs shows an increasing accuracy of training data while that of validation data does not increase anymore and a declining loss of training data while that of validation does not decrease. This is a tendency of overfitting, which means the model should adopt 20 epochs rather than 30 epochs. (5) Incompleteness Compared with the detailed depiction of the first three parts, part 4, part 5 and part 6 are insufficient. It is necessary to complement these three parts with interpretation, explanation and discussion. Things to improve the paper that did not impact the score: (1) Language problems such as in page 3 paragraph 2: “To compliment the keywords captured from radio talkshows, we also scrapped an online local newspaper.” Here “compliment” should be “complement”, and “scrap” is semantically incorrect. (2) Page 4 part 4.1: “…according to a hierarchical feature extraction given by equation 3.” It should be equation 2. (3) Format in references: fonts are not uniformed.
assistant
This paper tries to design an effective and economical model which spots keywords about pests and disease from community radio data in Luganda and English. The author collected keywords from both radio and online newspapers, set up a Siamese convolutional neural network, which includes input 1 and input 2, Resnet 50, Flatten, five-layer dense and L1 distance, trained the model on a total of 18426 samples and validated on 4607 and tested on 5759 samples via a K80 GPU on Google Colab. The paper should be rejected because: (1) The generalizability to different use cases needs to be shown. The approach is too applied. (2) The fit to ICLR is not perfect. I would expect a stronger focus on representation learning. (3) Model The paper lacks necessary explanation and clarification for the model, which is the core of the paper. For example, the part “Dense” in Siamese convolutional model is not well explained. The number of layers is up to 5, if there is no dropout, the model may have overfitting and gradient vanishing problems. (4) Result As can be seen from both model accuracy and model loss curves in Figure 1, the part of 21st -30th epochs shows an increasing accuracy of training data while that of validation data does not increase anymore and a declining loss of training data while that of validation does not decrease. This is a tendency of overfitting, which means the model should adopt 20 epochs rather than 30 epochs. (5) Incompleteness Compared with the detailed depiction of the first three parts, part 4, part 5 and part 6 are insufficient. It is necessary to complement these three parts with interpretation, explanation and discussion. Things to improve the paper that did not impact the score: (1) Language problems such as in page 3 paragraph 2: “To compliment the keywords captured from radio talkshows, we also scrapped an online local newspaper.” Here “compliment” should be “complement”, and “scrap” is semantically incorrect. (2) Page 4 part 4.1: “…according to a hierarchical feature extraction given by equation 3.” It should be equation 2. (3) Format in references: fonts are not uniformed.