An automatic method for estimating insect defoliation with visual highlights of consumed leaf tissue regions

As an essential component of the architecture of a plant, leaves are crucial to sustaining decision-making in cultivars and effectively support agricultural processes. When the leaf area is constantly monitored, a plant’s health and productive capacity can be assessed to foment proactive and reactive strategies. Because of that, one of the most critical tasks in agricultural processes is estimating foliar damage. In this sense, we present an automatic method to estimate leaf stress caused by insect herbivory, including damage in border regions. As a novelty, we present a method with well-defined processing steps suitable for numerical analysis and visual inspection of defoliation severity. We describe the proposed method and evaluate its performance concerning 12 different plant species. Experimental results show high assertiveness in estimating leaf area loss with a concordance correlation coefficient of 0.98 for grape, soybean, potato, and strawberry leaves. A classic pattern recognition approach, named template matching, is at the core of the method whose performance is compared to cutting-edge techniques. Results demonstrated that the method achieves foliar damage quantification with precision comparable to deep learning models. The code prepared by the authors is publicly available.