Artificial intelligence for detection and quantification of rust and leaf miner in coffee crop
This work addresses pest detection for coffee farmers, offering a practical mobile tool, but it is incremental as it applies existing methods to a specific agricultural problem.
The researchers developed an algorithm to detect rust and leaf miner pests in coffee leaves and quantify disease severity, achieving an average precision of 81.5% in detection and finding no significant difference between quantification methods, proposing a less computationally costly approach.
Pest and disease control plays a key role in agriculture since the damage caused by these agents are responsible for a huge economic loss every year. Based on this assumption, we create an algorithm capable of detecting rust (Hemileia vastatrix) and leaf miner (Leucoptera coffeella) in coffee leaves (Coffea arabica) and quantify disease severity using a mobile application as a high-level interface for the model inferences. We used different convolutional neural network architectures to create the object detector, besides the OpenCV library, k-means, and three treatments: the RGB and value to quantification, and the AFSoft software, in addition to the analysis of variance, where we compare the three methods. The results show an average precision of 81,5% in the detection and that there was no significant statistical difference between treatments to quantify the severity of coffee leaves, proposing a computationally less costly method. The application, together with the trained model, can detect the pest and disease over different image conditions and infection stages and also estimate the disease infection stage.