Title: Intelligence assistant using deep learning: use case in crop disease prediction

Authors: U. Dinesh Kumar; Manaranjan Pradhan; Shailaja Grover; Naveen Kumar Bhansali

Addresses: Indian Institute of Management Bangalore, Bangalore, 560076, India ' Indian Institute of Management Bangalore, Bangalore, 560076, India ' Consultant, Data Centre and Analytics Lab (DCAL), Indian Institute of Management Bangalore, Bangalore, 560076, India ' Indian Institute of Management Bangalore, Bangalore, 560076, India

Abstract: In India, 70% of the Indian population is dependent on agriculture, yet agriculture generates only 13% of the country's gross domestic product. Several factors contribute to high levels of stress among farmers in India, such as increased input costs, draughts, and reduced revenues. The problem lies in the absence of an integrated farm advisory system. A farmer needs help to bridge this information gap, and they need it early in the crop's lifecycle to prevent it from being destroyed by pests or diseases. This research involves developing deep learning algorithms such as ResNet18 and DenseNet121 to help farmers diagnose crop diseases earlier and take corrective actions. By using deep learning techniques to detect these crop diseases with images farmers can scan or click with their smartphones, we can fill in the knowledge gap. To facilitate the use of the models by farmers, they are deployed in Android-based smartphones.

Keywords: agro-analytics; convolutional neural networks; CNNs; crop disease detection; data augmentation; intelligent assistant.

DOI: 10.1504/IJBIDM.2024.140896

International Journal of Business Intelligence and Data Mining, 2024 Vol.25 No.3/4, pp.451 - 469

Received: 10 Dec 2022
Accepted: 18 Oct 2023
Published online: 03 Sep 2024 *

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