Agricultural production is on most countries’ national agenda because climate change affects crops, fruits, vegetables, and insect infestation. Therefore, achieving maximum production results is a challenge faced by professional growers, who have seen greenhouses as a very good option to guarantee these results. By using new technologies inside greenhouses, farmers can reduce the damaging effect of insects on plants and improve indoor cultivation through climate control. However, to efficiently manage agricultural fields and greenhouses today, farmers have to apply technologies in line with Industry 4.0, such as: robots, Internet of Things devices, machine learning applications, and so on. In this context, deploying sensors plays a key role in collecting data and finding information supporting the farmer’s decision-making. As a feasible solution for small farms, this paper presents an autonomous robot that moves through greenhouse crop paths with previously-planned routes and can collect environmental data provided by a wireless sensor network, where the farmer does not have previous information about the crop. Here, an unsupervised learning algorithm is implemented to cluster the optimal, standard, and deficient sectors of a greenhouse to determine inappropriate growth patterns in crops. Finally, a user interface is designed to help farmers plan both the route and distance to be traveled by the robot while collecting information from the sensors to observe crop conditions.
- smart agriculture