Insect Predation Estimate Using Binary Leaf Models and Image-Matching Shapes

Abstract

Estimating foliar damage is essential in agricultural processes to provide proper crop management, such as monitoring the defoliation level to take preventive actions. Furthermore, it is helpful to avoid the reduction of plant energy production, nutrition decrement, and consequently, the reduction of the final production of the crop and economic losses. In this sense, numerous proposals support the defoliation estimate task, ranging from traditional methodologies to computational solutions. However, subjectivity characteristics, reproducibility limitations, and imprecise results persist. Then, these circumstances justify the search for new solutions, especially in defoliation assessments. The main goal of this paper consists of developing an automatic method to estimate the percentage of damaged leaf areas consumed by insects. As a novelty, our method provides high precision in calculating defoliation severity caused by insect predation on the leaves of various plant species and works effectively to estimate leaf loss in leaves with border damage. We describe our method and evaluate its performance concerning 12 different plant species. Our experimental results demonstrate high accuracy in the determination of leaf area loss with a correlation coefficient superior to 0.84 for apple, blueberry, cherry, corn, grape, bell pepper, potato, raspberry, soybean, and strawberry leaves, and mean absolute error (MAE) less than 4% in defoliation levels up to 54% in soybean, strawberry, potato, and corn leaves. In addition, the method maintains a mean error of less than 50%, even for severe defoliation levels up to 99%.

Keywords

• leaf area measurement
• defoliation
• insect predating
• smart farming
• precision agriculture