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

• Published in: Artificial intelligence in agriculture 2022, Vol.6, p.77-89
• Main Authors: Raouhi, El Mehdi, Lachgar, Mohamed, Hrimech, Hamid, Kartit, Ali
• Format: Article
• Language: English
• Subjects: agriculture; artificial intelligence; Classification; Convolutional neuronal networks (CNN); crop quality; data collection; disease detection; Gradient descent; leaves; Morocco; neurons; Olive dataset diseases (ODD); olives; Optimization; Plant diseases; prediction
• ISSN: 2589-7217; 2589-7217
• DOI: 10.1016/j.aiia.2022.06.001

•The olive is exposed to the attacks of different micro-organisms and other factors.•The olive disease dataset contains a variety images of different growth stages.•The extracted oil's quality and quantity depend both on fruits and health process.•The classification approach here lies in fine-tuning based on the CNN architecture.•The fruit texture and the factors that impact its effectiveness and efficiency. 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.