Optimizing olive disease classification through transfer learning with unmanned aerial vehicle imagery

Early detection of diseases in growing olive trees is essential for reducing costs and increasing productivity in this crucial economic activity. The quality and quantity of olive oil depend on the health of the fruit, making accurate and timely information on olive tree diseases critical to monitor growth and anticipate fruit output. The use of unmanned aerial vehicles (UAVs) and deep learning (DL) has made it possible to quickly monitor olive diseases over a large area indeed of limited sampling methods. Moreover, the limited number of research studies on olive disease detection has motivated us to enrich the literature with this work by introducing new disease classes and classification methods for this tree. In this study, we present a UAV system using convolutional neuronal network (CNN) and transfer learning (TL). We constructed an olive disease dataset of 14K images, processed and trained it with various CNN in addition to the proposed MobileNet-TL for improved classification and generalization. The simulation results confirm that this model allows for efficient diseases classification, with a precision accuracy achieving 99% in validation. In summary, TL has a positive impact on MobileNet architecture by improving its performance and reducing the training time for new tasks

Optimization techniques in deep convolutional neuronal networks applied to olive diseases classification

Plants diseases have a detrimental effect on the quality but also on the quantity of agricultural production. However, the prediction of these diseases is proving the effect on crop quality and on reducing the risk of production losses. Indeed, the detection of plant diseases -either with a naked eye or using traditional methods- is largely a cumbersome process in terms of time, availability and results with a high-risk error. The present work introduces a depth study of various CNN architectures with different optimization algorithms carried out for olive disease detection using classification techniques that recommend the best model for constructing an effective disease detector. This study presents a dataset of 5571 olive leaf images collected manually on real conditions from different regions of Morocco, that also includes healthy class to detect olive diseases. Further, one of the goals of this research was to study the correlation effects between CNN architectures and optimization algorithms evaluated by the accuracy and other performance metrics. The highest rate in trained models was 100 %, while the highest rate in experiments without data augmentation was 92,59 %. Another subject of this study is the influence of the optimization algorithms on neuronal network performance. As a result of the experiments carried out, the MobileNet architecture using Rmsprop algorithms outperformed the others combinations in terms of performance and efficiency of disease detector