Identify and classify pests in the agricultural sector using metaheuristics deep learning approach
The most significant challenges impacting agricultural production and preventing the sustainable expansion of the agricultural sector are insect pests and crop diseases. It is inefficient to put surveillance cameras in areas densely populated by the pests you are trying to catch, and it is typically...
| Main Authors: | , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2023-06-01
|
| Series: | Franklin Open |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S277318632300018X |
| _version_ | 1797753876744503296 |
|---|---|
| author | Atul B. Kathole Jayashree Katti Savita Lonare Gulbakshee Dharmale |
| author_facet | Atul B. Kathole Jayashree Katti Savita Lonare Gulbakshee Dharmale |
| author_sort | Atul B. Kathole |
| collection | DOAJ |
| description | The most significant challenges impacting agricultural production and preventing the sustainable expansion of the agricultural sector are insect pests and crop diseases. It is inefficient to put surveillance cameras in areas densely populated by the pests you are trying to catch, and it is typically not enough to check in on the photos generated by your Internet of Things monitoring devices from a single location. Although the Internet of Things (IoT) is a specialised technology and analytics system, it has found many applications, including in agricultural contexts. To aid in identifying and naming the insects seen in the pictures, this study seeks to establish a model for pest identification and categorisation. In the beginning, data is gathered by Internet of Things devices using the IoT platform. The collected images are then subjected to an object detection analysis using Yolov3. The Adaptive Honey Badger Algorithm is used to fine-tune the classifier’s parameters. (AHBA). These findings demonstrate that the proposed technique is preferable due to its ability to speed up the collection of agricultural data and guarantee technical support. |
| first_indexed | 2024-03-12T17:24:12Z |
| format | Article |
| id | doaj.art-2e6ea79a96454d73a93b50893fdb70cc |
| institution | Directory Open Access Journal |
| issn | 2773-1863 |
| language | English |
| last_indexed | 2024-03-12T17:24:12Z |
| publishDate | 2023-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Franklin Open |
| spelling | doaj.art-2e6ea79a96454d73a93b50893fdb70cc2023-08-05T05:18:32ZengElsevierFranklin Open2773-18632023-06-013100024Identify and classify pests in the agricultural sector using metaheuristics deep learning approachAtul B. Kathole0Jayashree Katti1Savita Lonare2Gulbakshee Dharmale3Computer Engineering, Dr. D. Y. Patil Institute of Technology, Pune, India; Corresponding author.IT Department, Pimpri Chinchwad College of Engineering, Pune 411044, IndiaComputer Engineering, Dr. D. Y. Patil Institute of Technology, Pune, IndiaIT Department, Pimpri Chinchwad College of Engineering, Pune 411044, IndiaThe most significant challenges impacting agricultural production and preventing the sustainable expansion of the agricultural sector are insect pests and crop diseases. It is inefficient to put surveillance cameras in areas densely populated by the pests you are trying to catch, and it is typically not enough to check in on the photos generated by your Internet of Things monitoring devices from a single location. Although the Internet of Things (IoT) is a specialised technology and analytics system, it has found many applications, including in agricultural contexts. To aid in identifying and naming the insects seen in the pictures, this study seeks to establish a model for pest identification and categorisation. In the beginning, data is gathered by Internet of Things devices using the IoT platform. The collected images are then subjected to an object detection analysis using Yolov3. The Adaptive Honey Badger Algorithm is used to fine-tune the classifier’s parameters. (AHBA). These findings demonstrate that the proposed technique is preferable due to its ability to speed up the collection of agricultural data and guarantee technical support.http://www.sciencedirect.com/science/article/pii/S277318632300018XSmart agriculture systemCNNInternet of ThingsPest classification |
| spellingShingle | Atul B. Kathole Jayashree Katti Savita Lonare Gulbakshee Dharmale Identify and classify pests in the agricultural sector using metaheuristics deep learning approach Franklin Open Smart agriculture system CNN Internet of Things Pest classification |
| title | Identify and classify pests in the agricultural sector using metaheuristics deep learning approach |
| title_full | Identify and classify pests in the agricultural sector using metaheuristics deep learning approach |
| title_fullStr | Identify and classify pests in the agricultural sector using metaheuristics deep learning approach |
| title_full_unstemmed | Identify and classify pests in the agricultural sector using metaheuristics deep learning approach |
| title_short | Identify and classify pests in the agricultural sector using metaheuristics deep learning approach |
| title_sort | identify and classify pests in the agricultural sector using metaheuristics deep learning approach |
| topic | Smart agriculture system CNN Internet of Things Pest classification |
| url | http://www.sciencedirect.com/science/article/pii/S277318632300018X |
| work_keys_str_mv | AT atulbkathole identifyandclassifypestsintheagriculturalsectorusingmetaheuristicsdeeplearningapproach AT jayashreekatti identifyandclassifypestsintheagriculturalsectorusingmetaheuristicsdeeplearningapproach AT savitalonare identifyandclassifypestsintheagriculturalsectorusingmetaheuristicsdeeplearningapproach AT gulbaksheedharmale identifyandclassifypestsintheagriculturalsectorusingmetaheuristicsdeeplearningapproach |