CropAI-SOS: AI-Powered Crop Health Monitoring, Weather Alert, Geo-Mapping, and Emergency Support System for Farmers

The proposed CropAI-SOS system offers an integrated solu-tion to assist farmers in monitoring crop health and managing agricultural activities more efficiently. The system integrates artificial intelligence, weather monitoring, geo-mapping and emergency support features all in one platform. The system, incorporating these technologies, enables farmers to identify crop diseases in the early phase, get timely alerts on weather and access location-specific agricultural information. In practice, the proposed system shows how the use of mod-ern technologies such as machine learning and image processing can improve crop monitoring and help farmers to make better decisions. The crop disease detection module detects unhealthy crops based on image analysis. The weather alert module provides early warnings on environmental conditions that can affect crop growth. Furthermore, geo-mapping improves the system by allowing farmers to see farm locations and receive updates specific to their region. Another important contribution of the system is the emer-gency support feature, which allows farmers to report urgent agricultural issues and seek assistance when needed. This transforms the platform from a simple monitoring tool into a more comprehensive agricultural support system.

 [1]     S. P. Mohanty, D. P. Hughes, and M. Salathe, “Using Deep Learning for Image-Based Plant Disease Detection,” Frontiers in Plant Science, vol. 7, 2016. [2]     K. P. Ferentinos, “Deep Learning Models for Plant Disease Detection and Diagnosis,” Computers and Electronics in Agriculture, vol. 145, 2018, pp. 311–318. [3]     A. Kamilaris and F. X. Prenafeta-Boldu, “Deep Learning in Agriculture: A Survey,” Computers and Electronics in Agriculture, vol. 147, 2018, pp. 70–90. [4]     K. G. Liakos et al., “Machine Learning in Agriculture: A Review,” Sensors, vol. 18, no. 8, 2018. [5]     A. Picon et al., “Deep Convolutional Neural Networks for Mobile Capture Device-Based Crop Disease Classification,” Computers and Electronics in Agriculture, vol. 161, 2019, pp. 280–290. [6]     S. Sladojevic et al., “Deep Neural Networks Based Recognition of Plant Diseases by Leaf Image Classification,” Computational Intelligence and Neuroscience, 2016. [7]     M. Brahimi et al., “Deep Learning for Plant Diseases: Detection and Saliency Map Visualization,” Human and Machine Learning, Springer, 2017. [8]     A. Chlingaryan, S. Sukkarieh, and B. Whelan, “Machine Learning Approaches for Crop Yield Prediction and Smart Farming,” Computers and Electronics in Agriculture, vol. 151, 2018. [9]     S. Wolfert et al., “Big Data in Smart Farming – A Review,” Agricultural Systems, vol. 153, 2017, pp. 69–80. [10]    P. P. Ray, “Internet of Things for Smart Agriculture: Technologies, Practices and Future Direction,” Journal of Ambient Intelligence and Smart Environments, 2017.