Harnessing deep learning to analyze climate change impacts on crop production

Abstract

Agricultural practices in Africa uplift the economy and sustain livelihoods. However, in recent years numerous issues like climate change, reduced productivity, narrowing resources, and increased prevalence have emerged. In order to combat these obstacles, we devised a plan that integrates IoT, AI ML, and geospatial technology. By analyzing 45 industrial reports and peer-reviewed journals we found out that the use of advanced technology makes resource optimization, irrigation management, and disease detection significantly easier. Our findings reveal some astonishing facts like the efficiency of CNN neural networks in terms of disease detection which stood at an impressive 92 %, then there are neural networks that achieve an accuracy of 88.9 % in predicting the yield of crops. Lastly, RL has managed to attain a water-saving efficiency of an impressive 25.4 %. Despite these advancements, the adoption rate in Africa is still low, this can be attributed to poor infrastructure, lack of funds, and absence of professional knowledge. In order to counter these shortcomings, we suggest political term initiatives, initiatives aimed at enhancing expertise and knowledge, and affordable IOT implementation. In addition to identifying the socio-economic and infrastructural barriers to technology adoption, this essay also offers suggestions that advocate for facilitating sustainable agricultural practices in Africa. So that the identified gaps are bridged, the research aids in enhancing climate change resilience for sustainable growth in the agriculture industry of the continent.

Keywords

• Climate Change
• Agriculture
• Artificial Intelligence (AI)
• Machine Learning (ML)
• Internet of Things (IoT)
• Geospatial Technologies