Automating yellow rust disease identification in wheat using artificial intelligence

Abstract

Plant disease has long been one of the major threats to world food security due to reduction in the crop yield and quality. Accurate and precise diagnosis of plant diseases has been a significant challenge. Cost-effective automated computational systems for disease diagnosis would facilitate advancements in agriculture. The objective of this paper is to explore computer vision based Artificial Intelligence method for automating the identification of yellow rust disease and improve the accuracy of plant disease identification. The dataset of 2000 images of wheat leaf were collected in the real life experimental conditions of ICAR-Indian Agricultural Research Institute, New Delhi in the crop season during January-April, 2019. Based on our experiment, we propose a deep learning-based approach to detect healthy leaves and yellow rust infected leaves in the wheat crop. The experiments are implemented in python with PyCharm IDE, utilizing the Keras deep learning library backend with TensorFlow. The proposed model achieves 97.3% testing accuracy and 98.42% as the training accuracy. The accuracy of the developed model can be improved further by training it with larger size of the dataset in future. In future, accuracy of computer vision based AI models can be improved by using the larger size training datasets. Also, these models can be used for providing automatic advisory services to the farmers, thereby, adding much needed assistance to the overloaded extension experts.

Cropland suitability assessment using multi criteria evaluation techniques and geo-spatial technology: A review

“…Further, Jain et al (2021c) have reported that suitability identification should be followed by optimum area under allocation of suitable crops failing which may lead to over-exploitation of natural resources and unsustainability of the crop for the region. With advent of cheaper computational power and storage, latest machine learning based methods should also be explored for suitability analysis based on computer vision (Nigam et al 2021). The analysis clearly reveals that soil, climatic and topography parameters are essential parameters for crop suitability analysis.…”