Minahil Shahzad


In Pakistan, agriculture and food invention classifications are facing accumulative pressures from climate change affecting biotic stresses on field crops specifically and in general on soil health and irrigation water. Pakistan has its GDP based on agriculture with major crops including Wheat, Sugarcane, Paddy and Cotton. However, data of the crops is taken manually, which is mostly labor intensive, relatively ineffective and non-scientific. Therefore, technological innovations in artificial intelligence are the most feasible and economically viable and proven options than ever to sheltered adequate food for the fast-growing population of the country. Crop maps are frequently designed using foliage indices and field data. With the recent advances in remote sensing and Artificial Intelligence (AI) such as high resolution satellite imagery analysis, deep learning and computer vision, automation and improvement in precision of crop mapping can be achieved. Now-e-days we can enumerate field scale phenotypic data accurately and assimilate the big data into analytical and prescriptive management tools. The integration of AI with geographic information systems (GIS) provides a powerful tool for real-time monitoring of accurate crop classification, plant health (Biotic Stress), crop growth, water (quality and quantity) and harvest monitoring. These models also have the capacity to do image analysis for disease diagnostics and associated management recommendations on farmers phones. It will also help to develop future training methodologies and modules according to running requirements in response to the existing biotic stresses of major field crops in Pakistan.


GIS; TensorFlow; Vegetation Index; Cross-Validation; Model Optimization.

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Pakistan Journal of Phytopathology
ISSN: 1019-763X (Print), 2305-0284 (Online).
© 2013 Pak. J. Phytopathol. All rights reserved.