AgrIntel: Spatio-temporal profiling of nationwide plant-protection problems using helpline data. (January 2023) - Godara, Samarth; Toshniwal, Durga; Bana, Ram Swaroop; Singh, Deepak; Bedi, Jatin; Parsad, Rajender; Dabas, Jai Prakash Singh; Jhajhria, Abimanyu; Godara, Shruti; Kumar, Raju; Marwaha, Sudeep
Abstract: 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. Highlights: Extracting nationwide disease/pest hot spots using helpline data. Obtaining weather parameters responsible for the outbreak of disease/pests. Locating regions with increasing/disease agricultural problems in India. Deep Learning-based models for forecasting farmers' demand for assistance. Four AI-based pipelines for nationwide profiling of plant protection-related problems.