.png){kind=icon}
返回
Insect Predation Estimate Using Binary Leaf Models and Image-Matching Shapes
.png){kind=icon}
OA
AI
DOI：10.3390/agronomy12112769.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
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%.
Keyword:
leaf area measurement
defoliation
insect predating
smart farming
precision agriculture
期刊
A
IF:
3.4
论文数:
1.7W
被引数:
5.0W
机构
引用论文
Estimating soybean leaf defoliation using convolutional neural networks and synthetic images使用卷积神经网络和合成图像估计大豆叶片落叶
PREAI
分享收藏分享收藏
GrapeNet: A Lightweight Convolutional Neural Network Model for Identification of Grape Leaf Diseases.png)
AGRICULTURE-BASELOAAI
AGRICULTURE-BASELIF3.6
OAAI分享收藏Insect Pest Image Recognition: A Few-Shot Machine Learning Approach including Maturity Stages ClassificationAGRONOMY-BASELOAAI
AGRONOMY-BASELIF3.4
OAAI分享收藏
查看更多内容