Many strawberry growers in some areas of the United States rely on customers to pick the fruits during the peak harvest months. Unfavorable weather conditions such as high humidity and excessive rainfall can quickly promote fruit rot and diseases. This study establishes an elementary farm information system to demonstrate timely information on the farm and fruit conditions (ripe, unripe) to the growers. The information system processes a video clip or a sequence of images from a camera to provide a map which can be viewed to estimate quantities of strawberries at different stages of ripeness. The farm map is built by state-of-the-art vision-based simultaneous localization and mapping (SLAM) techniques, which can generate the map and track the motion trajectory using image features. In addition, the input images pass through a semantic segmentation process using a learning-based approach to identify the conditions. A set of labeled images first trains an encoder-decoder neural network model. Then, the trained model is used to determine the fruit conditions from the incoming images. Finally, the fruit in different conditions is estimated using the segmentation results and demonstrated in the system. Generating this information can aid the growers’ decision-making process. Specifically, it can help farm labor direct traffic to specific strawberry locations within a farm where fruits need to be picked, or where berries need to be removed. The obtained system can help reduce farm revenue loss and promote sustainable crop production.Published version