There have been many studies on using machine learning and deep neural networks to help with mushroom farming. These studies focus on identifying different mushroom species, predicting growth, detecting diseases, analyzing cultivation bags, and understanding the market for better business decisions.

Identifying Mushroom SpeciesResearchers used models like AlexNet, ResNet-50, and GoogLeNet to accurately identify edible and poisonous mushrooms in Thailand.

Predicting Growth and Detecting DiseasesA study in Sri Lanka used IoT devices and Mobile Net V2 models to identify diseases in Oyster mushrooms and determine the best harvest times.

Using Smartphones for HarvestingResearchers created a large set of photos showing different growth stages of mushrooms. They used a CNN to predict the best harvest times, optimized for mobile devices.

Classifying Mushroom SpawnTo reduce contamination, researchers developed a method to classify oyster mushroom spawn using deep neural networks and other classifiers, achieving 98.8% accuracy.

Improving Mushroom ProductionThe study highlights the need to provide scientific information to small-scale mushroom producers, helping them become commercial producers and compete in local and international markets.