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Automated disease, weed and crop classification with computer vision will beinvaluable in the future of agriculture. However, existing model architectureslike ResNet, EfficientNet and ConvNeXt often underperform on smaller,specialised datasets typical of such projects. We address this gap withinformed data collection and the development of a new CNN architecture,PhytNet. Utilising a novel dataset of infrared cocoa tree images, wedemonstrate PhytNet's development and compare its performance with existingarchitectures. Data collection was informed by analysis of spectroscopy data,which provided useful insights into the spectral characteristics of cocoatrees. Such information could inform future data collection and modeldevelopment. Cocoa was chosen as a focal species due to the diverse pathologyof its diseases, which pose significant challenges for detection. ResNet18showed some signs of overfitting, while EfficientNet variants showed distinctsigns of overfitting. By contrast, PhytNet displayed excellent attention torelevant features, no overfitting, and an exceptionally low computation cost(1.19 GFLOPS). As such PhytNet is a promising candidate for rapid disease orplant classification, or precise localisation of disease symptoms forautonomous systems.

PhytNet -- Tailored Convolutional Neural Networks for Custom Botanical Data