Machine Learning-Based Classification of Powdery Mildew Severity on Melon Leaves

Abstract

Precision agriculture faces challenges related to plant disease detection. Plant phenotyping assesses the appearance to select the best genotypes that resist to varying environmental conditions via plant variety testing. In this process, official plant variety tests are currently performed in vitro by visual inspection of samples placed in a culture media. In this communication, we demonstrate the potential of a computer vision approach to perform such tests in a much faster and reproducible way. We highlight the benefit of fusing contrasts coming from front and back light. To the best of our knowledge, this is illustrated for the first time on the classification of the severity of the presence of a fungi, powdery mildew, on melon leaves with 95% of accuracy.

An Overview of Machine Learning Applications on Plant Phenotyping, with a Focus on Sunflower

“…ML can also be useful for classifying disease severity and help farmers streamline the distribution of agrochemicals. For example, powdery mildew on melon can be detected and classified due to the combined use of pre-trained CNN and SVM [190].…”

Resources for image-based high-throughput phenotyping in crops and data sharing challenges

“…datasets offer close range annotated images of infected plant organs against a clean background, offering a resource for disease diagnosis and severity scoring in collected leaves ( Mohanty, 2016 ; Arsenovic et al, 2019 ; Chouhan et al, 2019 ; Krohling, 2019 ; Parraga-Alava et al, 2019 ; Rauf et al, 2019 ; Tian et al, 2019 ; Nakatumba-Nabende et al, 2020 ; Singh et al, 2020 ). Machine learning models using support vector machines, CNNs, and self-attention CNNs trained on similar datasets were published recently ( Abdu et al, 2020 ; El Abidine et al, 2020 ; Zeng and Li, 2020 ), some of which report increased efficiency when using segmented regions for pathogen identification ( Esgario et al, 2020 ; Karlekar and Seal, 2020 ). A comprehensive review on machine learning for disease assessment in crops was published by Hasan et al (2020) .…”

Vibrational spectroscopic profiling of biomolecular interactions between oak powdery mildew and oak leaves

“…5 Severe powdery mildew infections result in denser or higher percentage coverage of the leaf, which can be quantitatively assessed via either machine learning-based or pixel analysis-based image processing techniques and typically further requiring a spectroscopic-based method to quantify how much of a given leaf is covered with mildew. [6][7][8][9][10][11][12][13] During mycelial coverage, the net rates of photosynthesis and transpiration of the affected leaves are reduced, 14 leading to a decrease in growth and net carbon uptake during the growing season. 15 The haustoria, specialised fungal cellular structures designed to siphon nutrients away from the leaf, 16 also disrupt the translocation of photosynthates from the leaf into the tree.…”