AgrIntel: Spatio-temporal profiling of nationwide plant-protection problems using helpline data
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Title |
AgrIntel: Spatio-temporal profiling of nationwide plant-protection problems using helpline data
Not Available |
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Creator |
Samarth Godara
Durga Toshniwal Ram Swaroop Bana Deepak Singh Jatin Bedi Rajender Parsad Jai Prakash Singh Dabas Abimanyu Jhajhria Shruti Godara Raju Kumar Sudeep Marwaha |
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Subject |
Agriculture policy
Deep learning Disease profiling Food security Plant-protection Spatio-temporal analysis Sustainable food production |
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Description |
Not Available
Sustainable development of the national food system must ensure the introduction of adequate food security interventions and policies. However, several high-end technological developments remain unexplored, which can be used to gain explicit information regarding agricultural problems. In this direction, the presented work proposes AgrIntel, a framework consisting of multiple AI-based pipelines to process nationwide farmers’ helpline data and obtain spatiotemporal insights regarding food-production problems on an extensive scale. AgrIntel overcomes several limitations of the existing methods used for similar objectives, including limited scalability, low frequency, and high cost. The call-logs dataset used in the study is obtained from the nationwide network of farmers’ helpline centers, managed by the Ministry of Agriculture & Farmers’ Welfare, Government of India. The article demonstrates the Spatio-temporal profile of one of India’s highest food grain-affecting diseases, i.e., ‘‘blast in rice crop’’, to demonstrate the utility of the AgrIntel pipelines. First, the proposed framework extracts and clusters the precise geographical locations of farmers calling for help corresponding to the target agricultural problem. Next, the temporal modeling of the problem helps extract the critical dates corresponding to the crop disease/pest spread. Furthermore, by incorporating the historical agroclimatological data, the article introduces a new medium to extract the favorable weather conditions corresponding to the targeted disease/pest outbreak. In addition, the study explores the potential of Deep Learning models (based on Artificial Neural Network, Convolutional Neural Network, Gated Recurrent Unit and Long short-term memory unit) to efficiently predict the futuristic demand for assistance regarding target problems (RMSE of ≈1.5 and MAE of ≈0.9 query calls). The obtained results expose unrevealed insights regarding food production problems, significantly boosting the food security policy-designing procedure. Not Available |
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Date |
2022-12-20T07:06:32Z
2022-12-20T07:06:32Z 2022-11-01 |
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Type |
Research Paper
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Identifier |
Samarth Godara, Durga Toshniwal, Ram Swaroop Bana, Deepak Singh, Jatin Bedi, Rajender Parsad, Jai Prakash Singh Dabas, Abimanyu Jhajhria, Shruti Godara, Raju Kumar, Sudeep Marwaha. (2023) AgrIntel: Spatio-temporal profiling of nationwide plant-protection problems using helpline data. Engineering Applications of Artificial Intelligence, 117(A):105555. https://doi.org/10.1016/j.engappai.2022.105555
Not Available http://krishi.icar.gov.in/jspui/handle/123456789/75228 |
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Language |
English
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Relation |
Not Available;
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Publisher |
Elsevier
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