RESUMO
The image-based detection and classification of plant diseases has become increasingly important to the development of precision agriculture. We consider the case of tomato, a high-value crop supporting the livelihoods of many farmers around the world. Many biotic and abiotic plant health issues impede the efficient production of this crop, and laboratory-based diagnostics are inaccessible in many remote regions. Early detection of these plant health issues is essential for efficient and accurate response, prompting exploration of alternatives for field detection. Considering the availability of low-cost smartphones, artificial intelligence-based classification facilitated by mobile phone imagery can be a practical option. This study introduces a smartphone-attachable 30x microscopic lens, used to produce the novel tomato microimaging dataset of 8500 images representing 34 tomato plant conditions on the upper and lower sides of leaves as well as on the surface of tomato fruits. We introduce TOMMicroNet, a 14-layer convolutional neural network (CNN) trained to classify amongst biotic and abiotic plant health issues, and we compare it against six existing pre-trained CNN models. We compared two separate pipelines of grouping data for training TOMMicroNet, either presenting all data at once or separating into subsets based on the three parts of the plant. Comparing configurations based on cross-validation and F1 scores, we determined that TOMMicroNet attained the highest performance when trained on the complete dataset, with 95% classification accuracy on both training and external datasets. Given TOMMicroNet's capabilities when presented with unfamiliar data, this approach has the potential for the identification of plant health issues.