This study aims to contribute to the efficient use of natural resources in corn cultivation; this includes the precise control of fertilizers and, in general, the monitoring of a crop’s variables to ensure an improvement in production. We introduce a technological and integral solution based on Wireless Sensor Networks (WSNs), Machine Learning (ML), and Edge Computing (EC) to monitor the behavior of a corn crop. At present, the statistics of the entities that monitor agricultural production indicators and the information obtained in the field are inconsistent. The farmer applies eight (8) times less fertilizer than the national average, resulting in twice the production of a non-technified crop and 1.5 times more production if there is some type of technification. Using the proposed platform, which comprises WSN, ML, and EC, contributes to food security by strengthening sustainable agriculture; this aligns with the goal of positively impacting agricultural productivity and the income of small producers. We developed a supervised learning model using Naive Bayes, which was found to have 87% sensitivity and 73% specificity, although the accuracy is 60%.