Abstract
Rice holds a major share in India’s agricultural economy. The various areas under rice cultivation in India include the jade green shaded rice cultivated in the eastern regions, dry rice fields in southern regions, etc. The country is one of the world’s massive brown and white rice producers. The total yield in the year 2009 declined almost from 99.18 million tons to a total of just 89.14 million tons which affected the overall decrease in the crop yields as well as the financial outcome of the farmers. So, detecting rice diseases will help in lessening the adverse effects of the natural imbalance. Rice is one of the staple foods in India. Therefore, it becomes the main crop with the largest area under rice cultivation. As India is a tropical country, it benefits crop production as the crop needs hot and humid conditions for its efficient growth. Rice plants are grown in regions that receive heavy rainfall every year. For proper yield the crop requires an overall temperature of around 25 ºC and a steady rainfall of more than 0.1mm. India being a country with extreme climatic conditions and increasing pollution cannot meet the production demand for the crops due to growing diseases and abnormalities. This paper proposes a method to detect whether a rice crop is healthy or unhealthy using Convolutional neural networks, its various architectures, and probabilistic neural networks.
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Limkar, S. et al. (2021). Classification and Prediction of Rice Crop Diseases Using CNN and PNN. In: Satapathy, S., Zhang, YD., Bhateja, V., Majhi, R. (eds) Intelligent Data Engineering and Analytics. Advances in Intelligent Systems and Computing, vol 1177. Springer, Singapore. https://doi.org/10.1007/978-981-15-5679-1_4
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