Changes
On April 29, 2024 at 9:40:16 AM UTC, kennedysenagi:
-
Added resource Main metadata sheet to A Convolutional Neural Network with Image and Numerical Data to Improve Farming of Edible Crickets as a Source of Food - A Decision Support System
f | 1 | { | f | 1 | { |
2 | "acknowledgement": "The authors gratefully acknowledge the financial | 2 | "acknowledgement": "The authors gratefully acknowledge the financial | ||
3 | support for this research by the following organizations and agencies: | 3 | support for this research by the following organizations and agencies: | ||
4 | Kenya Education Network Trust (KENET) - Grant Number AGMT1170; | 4 | Kenya Education Network Trust (KENET) - Grant Number AGMT1170; | ||
5 | Foreign, Commonwealth & Development Office (FCDO) [IMC-Grant 21108]; | 5 | Foreign, Commonwealth & Development Office (FCDO) [IMC-Grant 21108]; | ||
6 | Australian Centre for International Agricultural Research (ACIAR) | 6 | Australian Centre for International Agricultural Research (ACIAR) | ||
7 | (ProteinAfrica \u2013 Grant No: LS/2020/154), the Rockefeller | 7 | (ProteinAfrica \u2013 Grant No: LS/2020/154), the Rockefeller | ||
8 | Foundation (WAVE-IN\u2014 Grant No: 2021 FOD 030); Bill & Melinda | 8 | Foundation (WAVE-IN\u2014 Grant No: 2021 FOD 030); Bill & Melinda | ||
9 | Gates Foundation (INV-032416); IKEA Foundation (G-2204-02144), Horizon | 9 | Gates Foundation (INV-032416); IKEA Foundation (G-2204-02144), Horizon | ||
10 | Europe (NESTLER - Project: 101060762 - HORIZON-CL6-2021- | 10 | Europe (NESTLER - Project: 101060762 - HORIZON-CL6-2021- | ||
11 | FARM2FORK-01), the Curt Bergfors Foundation Food Planet Prize Award; | 11 | FARM2FORK-01), the Curt Bergfors Foundation Food Planet Prize Award; | ||
12 | Norwegian Agency for Development Cooperation, the Section for | 12 | Norwegian Agency for Development Cooperation, the Section for | ||
13 | Research, Innovation and Higher Education grant number RAF\u20133058 | 13 | Research, Innovation and Higher Education grant number RAF\u20133058 | ||
14 | KEN\u201318/0005 (CAP\u2013Africa); the Swedish International | 14 | KEN\u201318/0005 (CAP\u2013Africa); the Swedish International | ||
15 | Development Cooperation Agency (SIDA); the Swiss Agency for | 15 | Development Cooperation Agency (SIDA); the Swiss Agency for | ||
16 | Development and Cooperation (SDC); the Australian Centre for | 16 | Development and Cooperation (SDC); the Australian Centre for | ||
17 | International Agricultural Research (ACIAR); the Norwegian Agency for | 17 | International Agricultural Research (ACIAR); the Norwegian Agency for | ||
18 | Development Cooperation (NORAD); the German Federal Ministry for | 18 | Development Cooperation (NORAD); the German Federal Ministry for | ||
19 | Economic Cooperation and Development (BMZ); the Federal Democratic | 19 | Economic Cooperation and Development (BMZ); the Federal Democratic | ||
20 | Republic of Ethiopia; and the Government of the Republic of Kenya. The | 20 | Republic of Ethiopia; and the Government of the Republic of Kenya. The | ||
21 | funders had no role in study design, data collection and analysis, | 21 | funders had no role in study design, data collection and analysis, | ||
22 | decision to publish, or preparation of the manuscript. ", | 22 | decision to publish, or preparation of the manuscript. ", | ||
23 | "administrative_areas": "Nairobi", | 23 | "administrative_areas": "Nairobi", | ||
24 | "author": null, | 24 | "author": null, | ||
25 | "author_email": null, | 25 | "author_email": null, | ||
26 | "citation_narrative": "DOI: 10.3389/frai.2024.1403593", | 26 | "citation_narrative": "DOI: 10.3389/frai.2024.1403593", | ||
27 | "collaborators": "[{\"collaborator\": \"Henry Kyalo\"}, | 27 | "collaborators": "[{\"collaborator\": \"Henry Kyalo\"}, | ||
28 | {\"collaborator\": \"Henri E. Z. Tonnang\"}, {\"collaborator\": | 28 | {\"collaborator\": \"Henri E. Z. Tonnang\"}, {\"collaborator\": | ||
29 | \"James Egonyu\"}, {\"collaborator\": \"John Olukuru\"}, | 29 | \"James Egonyu\"}, {\"collaborator\": \"John Olukuru\"}, | ||
30 | {\"collaborator\": \"Chrysantus M. Tanga\"}]", | 30 | {\"collaborator\": \"Chrysantus M. Tanga\"}]", | ||
31 | "contact_person": "Kennedy Senagi", | 31 | "contact_person": "Kennedy Senagi", | ||
32 | "contact_person_email": "ksenagi@icipe.org", | 32 | "contact_person_email": "ksenagi@icipe.org", | ||
33 | "country": "[{\"country\": \"KE\"}]", | 33 | "country": "[{\"country\": \"KE\"}]", | ||
34 | "creator_user_id": "f09ec764-fe3c-4069-850b-f968ff0c20bb", | 34 | "creator_user_id": "f09ec764-fe3c-4069-850b-f968ff0c20bb", | ||
35 | "date_uploaded": "2024-04-26", | 35 | "date_uploaded": "2024-04-26", | ||
36 | "donor": "Kenya Education Network Trust (KENET)", | 36 | "donor": "Kenya Education Network Trust (KENET)", | ||
37 | "end_date": "2024-04-26", | 37 | "end_date": "2024-04-26", | ||
38 | "groups": [], | 38 | "groups": [], | ||
39 | "id": "59311393-3b0b-4f79-92b3-62a9b30a89ff", | 39 | "id": "59311393-3b0b-4f79-92b3-62a9b30a89ff", | ||
40 | "isopen": false, | 40 | "isopen": false, | ||
41 | "license_id": "cc-nc", | 41 | "license_id": "cc-nc", | ||
42 | "license_title": "Creative Commons Non-Commercial (Any)", | 42 | "license_title": "Creative Commons Non-Commercial (Any)", | ||
43 | "license_url": "http://creativecommons.org/licenses/by-nc/2.0/", | 43 | "license_url": "http://creativecommons.org/licenses/by-nc/2.0/", | ||
44 | "maintainer": "Kennedy Senagi", | 44 | "maintainer": "Kennedy Senagi", | ||
45 | "maintainer_email": "ksenagi@icipe.org", | 45 | "maintainer_email": "ksenagi@icipe.org", | ||
46 | "metadata_created": "2024-04-29T09:38:54.556778", | 46 | "metadata_created": "2024-04-29T09:38:54.556778", | ||
n | 47 | "metadata_modified": "2024-04-29T09:38:54.556787", | n | 47 | "metadata_modified": "2024-04-29T09:40:16.585528", |
48 | "name": | 48 | "name": | ||
49 | p-learning-algorithms-in-farming-edible-crickets-as-a-source-of-food", | 49 | p-learning-algorithms-in-farming-edible-crickets-as-a-source-of-food", | ||
50 | "notes": "Crickets (Gryllus bimaculatus) produce sounds as a natural | 50 | "notes": "Crickets (Gryllus bimaculatus) produce sounds as a natural | ||
51 | means to communicate and convey various behaviors and activities, | 51 | means to communicate and convey various behaviors and activities, | ||
52 | including mating, feeding, aggression, distress, and more. These | 52 | including mating, feeding, aggression, distress, and more. These | ||
53 | vocalizations are intricately linked to prevailing environmental | 53 | vocalizations are intricately linked to prevailing environmental | ||
54 | conditions such as temperature and humidity. By accurately monitoring, | 54 | conditions such as temperature and humidity. By accurately monitoring, | ||
55 | identifying, and appropriately addressing these behaviors and | 55 | identifying, and appropriately addressing these behaviors and | ||
56 | activities, the farming and production of crickets can be enhanced. | 56 | activities, the farming and production of crickets can be enhanced. | ||
57 | This research implemented a decision support system that leverages | 57 | This research implemented a decision support system that leverages | ||
58 | machine learning (ML) algorithms to decode and classify cricket songs, | 58 | machine learning (ML) algorithms to decode and classify cricket songs, | ||
59 | along with their associated key weather variables (temperature and | 59 | along with their associated key weather variables (temperature and | ||
60 | humidity). Videos capturing cricket behavior and weather variables | 60 | humidity). Videos capturing cricket behavior and weather variables | ||
61 | were recorded. From these videos, sound signals were extracted and | 61 | were recorded. From these videos, sound signals were extracted and | ||
62 | classified such as calling, aggression, and courtship. Numerical and | 62 | classified such as calling, aggression, and courtship. Numerical and | ||
63 | image features were extracted from the sound signals and combined with | 63 | image features were extracted from the sound signals and combined with | ||
64 | the weather variables. The extracted numerical features, i.e., | 64 | the weather variables. The extracted numerical features, i.e., | ||
65 | Mel-Frequency Cepstral Coefficients (MFCC), Linear Frequency Cepstral | 65 | Mel-Frequency Cepstral Coefficients (MFCC), Linear Frequency Cepstral | ||
66 | Coefficients, and chroma, were used to train shallow (support vector | 66 | Coefficients, and chroma, were used to train shallow (support vector | ||
67 | machine, k-nearest neighbors, and random forest (RF)) ML algorithms. | 67 | machine, k-nearest neighbors, and random forest (RF)) ML algorithms. | ||
68 | While image features, i.e., spectrograms, were used to train different | 68 | While image features, i.e., spectrograms, were used to train different | ||
69 | state-of-the-art deep ML models, i,e., convolutional neural network} | 69 | state-of-the-art deep ML models, i,e., convolutional neural network} | ||
70 | architectures (ResNet152V2, VGG16, and EfficientNetB4). In the deep ML | 70 | architectures (ResNet152V2, VGG16, and EfficientNetB4). In the deep ML | ||
71 | category, ResNet152V2 had the best accuracy of 99.42%. The RF | 71 | category, ResNet152V2 had the best accuracy of 99.42%. The RF | ||
72 | algorithm had the best accuracy of 95.63% in the shallow ML category | 72 | algorithm had the best accuracy of 95.63% in the shallow ML category | ||
73 | when trained with a combination of MFCC+chroma and after feature | 73 | when trained with a combination of MFCC+chroma and after feature | ||
74 | selection. In descending order of importance, the top 6 ranked | 74 | selection. In descending order of importance, the top 6 ranked | ||
75 | features in the RF algorithm were, namely humidity, temperature, C#, | 75 | features in the RF algorithm were, namely humidity, temperature, C#, | ||
76 | mfcc11, mfcc10, and D. From the selected features. With this | 76 | mfcc11, mfcc10, and D. From the selected features. With this | ||
77 | information, it is notable that insects require specific temperatures | 77 | information, it is notable that insects require specific temperatures | ||
78 | and humidity for growth and metabolic activities. Moreover, the songs | 78 | and humidity for growth and metabolic activities. Moreover, the songs | ||
79 | produced by certain cricket species naturally align to musical tones | 79 | produced by certain cricket species naturally align to musical tones | ||
80 | such as C# and D as ranked by the algorithm. Using this knowledge, a | 80 | such as C# and D as ranked by the algorithm. Using this knowledge, a | ||
81 | decision support system was built to guide farmers about the optimal | 81 | decision support system was built to guide farmers about the optimal | ||
82 | temperature and humidity ranges and interpret the songs (calling, | 82 | temperature and humidity ranges and interpret the songs (calling, | ||
83 | aggression, and courtship) in relation to weather variables. With this | 83 | aggression, and courtship) in relation to weather variables. With this | ||
84 | information, farmers can put in place suitable measures such as | 84 | information, farmers can put in place suitable measures such as | ||
85 | temperature regulation, humidity control, addressing aggressors, and | 85 | temperature regulation, humidity control, addressing aggressors, and | ||
86 | other relevant interventions to minimize or eliminate losses and | 86 | other relevant interventions to minimize or eliminate losses and | ||
87 | enhance cricket production.", | 87 | enhance cricket production.", | ||
n | 88 | "num_resources": 0, | n | 88 | "num_resources": 1, |
89 | "num_tags": 6, | 89 | "num_tags": 6, | ||
90 | "organization": { | 90 | "organization": { | ||
91 | "approval_status": "approved", | 91 | "approval_status": "approved", | ||
92 | "created": "2022-05-17T13:53:24.098004", | 92 | "created": "2022-05-17T13:53:24.098004", | ||
93 | "description": "", | 93 | "description": "", | ||
94 | "id": "d16ea320-c49c-4a2e-b419-49c90c384c7d", | 94 | "id": "d16ea320-c49c-4a2e-b419-49c90c384c7d", | ||
95 | "image_url": "2022-05-17-135324.083479bees.jpeg", | 95 | "image_url": "2022-05-17-135324.083479bees.jpeg", | ||
96 | "is_organization": true, | 96 | "is_organization": true, | ||
97 | "name": "environmental-health", | 97 | "name": "environmental-health", | ||
98 | "state": "active", | 98 | "state": "active", | ||
99 | "title": "Environmental Health", | 99 | "title": "Environmental Health", | ||
100 | "type": "organization" | 100 | "type": "organization" | ||
101 | }, | 101 | }, | ||
102 | "owner_org": "d16ea320-c49c-4a2e-b419-49c90c384c7d", | 102 | "owner_org": "d16ea320-c49c-4a2e-b419-49c90c384c7d", | ||
103 | "principal_investigator": "Kennedy Senagi", | 103 | "principal_investigator": "Kennedy Senagi", | ||
104 | "principal_investigator_email": "ksenagi@icipe.org", | 104 | "principal_investigator_email": "ksenagi@icipe.org", | ||
105 | "private": false, | 105 | "private": false, | ||
106 | "region": "Nairobi", | 106 | "region": "Nairobi", | ||
107 | "relationships_as_object": [], | 107 | "relationships_as_object": [], | ||
108 | "relationships_as_subject": [], | 108 | "relationships_as_subject": [], | ||
t | 109 | "resources": [], | t | 109 | "resources": [ |
110 | { | ||||
111 | "cache_last_updated": null, | ||||
112 | "cache_url": null, | ||||
113 | "created": "2024-04-29T09:40:16.621911", | ||||
114 | "date_last_updated": "2024-04-26", | ||||
115 | "date_uploaded_resource": "2024-04-26", | ||||
116 | "description": "", | ||||
117 | "format": "CSV", | ||||
118 | "hash": "", | ||||
119 | "id": "91644e4e-7951-4ed4-8533-c52c7451e557", | ||||
120 | "last_modified": "2024-04-29T09:40:16.569668", | ||||
121 | "metadata_modified": "2024-04-29T09:40:16.591494", | ||||
122 | "mimetype": "text/csv", | ||||
123 | "mimetype_inner": null, | ||||
124 | "name": "Main metadata sheet", | ||||
125 | "package_id": "59311393-3b0b-4f79-92b3-62a9b30a89ff", | ||||
126 | "position": 0, | ||||
127 | "resource_type": null, | ||||
128 | "restricted": "{\"allowed_users\": \"\", \"level\": | ||||
129 | \"public\"}", | ||||
130 | "size": 58048, | ||||
131 | "state": "active", | ||||
132 | "url": | ||||
133 | 1644e4e-7951-4ed4-8533-c52c7451e557/download/main-metadata-sheet.csv", | ||||
134 | "url_type": "upload" | ||||
135 | } | ||||
136 | ], | ||||
110 | "start_date": "2024-04-26", | 137 | "start_date": "2024-04-26", | ||
111 | "state": "draft", | 138 | "state": "draft", | ||
112 | "tags": [ | 139 | "tags": [ | ||
113 | { | 140 | { | ||
114 | "display_name": "Insects", | 141 | "display_name": "Insects", | ||
115 | "id": "9c6eae2d-7311-41e5-958f-3b11a4a905d8", | 142 | "id": "9c6eae2d-7311-41e5-958f-3b11a4a905d8", | ||
116 | "name": "Insects", | 143 | "name": "Insects", | ||
117 | "state": "active", | 144 | "state": "active", | ||
118 | "vocabulary_id": null | 145 | "vocabulary_id": null | ||
119 | }, | 146 | }, | ||
120 | { | 147 | { | ||
121 | "display_name": "decision support system", | 148 | "display_name": "decision support system", | ||
122 | "id": "22b7773d-18d8-4b5d-a86d-7058197312a0", | 149 | "id": "22b7773d-18d8-4b5d-a86d-7058197312a0", | ||
123 | "name": "decision support system", | 150 | "name": "decision support system", | ||
124 | "state": "active", | 151 | "state": "active", | ||
125 | "vocabulary_id": null | 152 | "vocabulary_id": null | ||
126 | }, | 153 | }, | ||
127 | { | 154 | { | ||
128 | "display_name": "deep learning", | 155 | "display_name": "deep learning", | ||
129 | "id": "0265fb58-9cd3-4d0d-8215-2f0b571a09f4", | 156 | "id": "0265fb58-9cd3-4d0d-8215-2f0b571a09f4", | ||
130 | "name": "deep learning", | 157 | "name": "deep learning", | ||
131 | "state": "active", | 158 | "state": "active", | ||
132 | "vocabulary_id": null | 159 | "vocabulary_id": null | ||
133 | }, | 160 | }, | ||
134 | { | 161 | { | ||
135 | "display_name": "machine learning", | 162 | "display_name": "machine learning", | ||
136 | "id": "3638359c-f3cb-452c-ac1a-57193f56c880", | 163 | "id": "3638359c-f3cb-452c-ac1a-57193f56c880", | ||
137 | "name": "machine learning", | 164 | "name": "machine learning", | ||
138 | "state": "active", | 165 | "state": "active", | ||
139 | "vocabulary_id": null | 166 | "vocabulary_id": null | ||
140 | }, | 167 | }, | ||
141 | { | 168 | { | ||
142 | "display_name": "sound classification", | 169 | "display_name": "sound classification", | ||
143 | "id": "e5acbf75-416c-4213-abc9-bfd0a0cca7d9", | 170 | "id": "e5acbf75-416c-4213-abc9-bfd0a0cca7d9", | ||
144 | "name": "sound classification", | 171 | "name": "sound classification", | ||
145 | "state": "active", | 172 | "state": "active", | ||
146 | "vocabulary_id": null | 173 | "vocabulary_id": null | ||
147 | }, | 174 | }, | ||
148 | { | 175 | { | ||
149 | "display_name": "transfer learning", | 176 | "display_name": "transfer learning", | ||
150 | "id": "837a5abb-e47d-4c1e-88e1-5c75879de94d", | 177 | "id": "837a5abb-e47d-4c1e-88e1-5c75879de94d", | ||
151 | "name": "transfer learning", | 178 | "name": "transfer learning", | ||
152 | "state": "active", | 179 | "state": "active", | ||
153 | "vocabulary_id": null | 180 | "vocabulary_id": null | ||
154 | } | 181 | } | ||
155 | ], | 182 | ], | ||
156 | "third_party": "no", | 183 | "third_party": "no", | ||
157 | "title": "A Convolutional Neural Network with Image and Numerical | 184 | "title": "A Convolutional Neural Network with Image and Numerical | ||
158 | Data to Improve Farming of Edible Crickets as a Source of Food - A | 185 | Data to Improve Farming of Edible Crickets as a Source of Food - A | ||
159 | Decision Support System", | 186 | Decision Support System", | ||
160 | "type": "dataset", | 187 | "type": "dataset", | ||
161 | "url": null, | 188 | "url": null, | ||
162 | "version": null | 189 | "version": null | ||
163 | } | 190 | } |