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Enhancing Agriculture Productivity with IoT-Enabled Predictive Analytics & Machine Learning

EasyChair Preprint no. 11720

11 pagesDate: January 8, 2024


The interdisciplinary approach leverages data from IoT sensors and satellites, applies predictive analytics, and harnesses the power of machine learning algorithms to optimize farming practices. Precision agriculture offers a myriad of benefits. It enables resource optimization by providing real-time data on soil conditions, weather patterns, and crop health. This data-driven approach minimizes waste, enhances yield, and reduces the need for pesticides and fertilizers. Furthermore, machine learning models provide predictive insights, facilitating early disease detection, precise pest management, and accurate yield forecasting. These advancements empower farmers to make informed, data-driven decisions, thereby promoting more efficient and sustainable farming practices. The future of agriculture with IoT, predictive analytics, and machine learning holds great promise. Ongoing research focuses on advanced sensors, edge computing, and data integration. Additionally, ethics, data privacy, and regulatory considerations are essential areas of exploration. As technology continues to advance, it is evident that precision agriculture will play a pivotal role in addressing the food security and sustainability challenges of the 21st century. With the use of experimental data gathered from a maize farm, the suggested strategy is assessed. The findings demonstrate that the suggested method has a 95% accuracy rate for predicting crop yields. By decreasing the negative effects of farming techniques on the environment and boosting crop output, this research may help promote sustainable agriculture.

Keyphrases: Agriculture, Crop health, crop yields, environmental impact, farming practices, Internet of Things (IoT), machine learning, Predictive Analytics, Sustainability

BibTeX entry
BibTeX does not have the right entry for preprints. This is a hack for producing the correct reference:
  author = {Jasmine Beulah Gnanadurai and V Vinothina and V S Prakash and M Sandhya and K Vignesh},
  title = {Enhancing Agriculture Productivity with IoT-Enabled Predictive Analytics & Machine Learning},
  howpublished = {EasyChair Preprint no. 11720},

  year = {EasyChair, 2024}}
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