Convolutional Neural Network Model for the Detection of Diseases and Pests in Coffee Crops

The early detection of diseases and pests in coffee crops by means of artificial vision and pattern recognition brings with it the ease of inspection and the reduction of crop losses in coffee plantations. This work proposes a model based on neural networks that is capable of detecting coffee leaves in an image and also classifies them into the most common diseases in the Panamanian tropics. The coffee leaf disease classification model is able to classify the following diseases: Cercospora leaf spot, leaf rust, leaf miner and phoma. This model obtained an accuracy of 100% and a loss of 1.6 × 10
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during the training phase. Subsequent to the training phase, a validation of the model was performed; during this phase, an overall accuracy of 90% was obtained for each of the diseases and pests. In future work, it is desired to implement this architecture in the vision system of the agricultural robot. The vision system will allow farmers to effectively inspect and manage their crops.