Artificial Intelligence for Smart Cities and Villages: Advanced Technologies, Development, and Challenges

Smart Farming Solution for Crop Disease Prediction and Protection

Author(s): Poonam Chakravarty*, Jigar Pandya, Yagnesh Rathod and Mohan Raj

Pp: 282-298 (17)

DOI: 10.2174/9789815049251122010019

* (Excluding Mailing and Handling)

Abstract

Agriculture is the main source of income for Indian citizens with about 60% of the population depending upon agriculture which influences India’s economy. Crop selection and disease management plays a crucial role in the farmer’s economy. Smart farming systems help farmers to increase crop production through automated systems. Crop diseases can be predicted by a comprehensive analysis system. Smart farming system with Artificial Intelligence (AI) observes, and manages Internet of Things (IoT) devices to detect crop diseases by visual symptoms. Smartphone-based AI apps guide farmers for disease diagnosis, thus preventing yield loss. This system will detect plant diseases present on the leaves and provide preventive measures for the detected diseases. The plant leaf images are collected which show symptoms of diseased and healthy plants under maintained conditions to check for fungal, bacterial and viral diseases. Machine and Deep Learning can help identify crop diseases based on collected images and datasets pertaining to the crops segmented into Healthy and Diseased Crop. The IoT technology implements specific systems at different levels to predict crop diseases effectively. The different data is accessed easily from the centralized cloud system. The crop diseases are managed with the use of high fungicides due to this soil toxicity increase but this activity system will provide the best recommendations for proper disease management. AI, Image processing, IoT, machine learning, robotics, satellites, cloud computing technologies are improving farmers' crop management practices.



Keywords: Crop diseases, Crop management, Deep learning, Image processing, Internet of things, Machine learning, Smart farming.

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