计算机科学
卷积神经网络
人工智能
公制(单位)
分割
深度学习
目标检测
模式识别(心理学)
人口
特征提取
植物病害
特征(语言学)
生物技术
生物
语言学
运营管理
哲学
人口学
社会学
经济
作者
Prabhjot Kaur,Shilpi Harnal,Vinay Gautam,Mukund Pratap Singh,Santar Pal Singh
标识
DOI:10.1016/j.engappai.2022.105210
摘要
Tomato leaf infections are a common threat to long-term tomato production that affects many farmers worldwide. Early detection, treatment, and solution of tomato leaf specificity are critical for promoting healthy tomato plant growth and ensuring ample supply and health security for the world’s geometric growth (population). The detection of plant leaf disease using computer-assisted technologies is prevalent these days. In this work, use the 1610 tomato leaf images of different classes from PlantVillage standard repository for the localization of objects. An effective Deep Learning (DL) modified Mask Region Convolutional Neural Network (Mask R-CNN) is proposed for the autonomous segmentation and detection of tomato plant leaf disease in this research. Intending to conserve memory space and computational expense, the suggested model adds a light head “Region Convolutional Neural Network (R-CNN)”. By varying the proportions of anchor in the RPN network and also changing the feature extraction topology, which improves the detection accuracy and computing the metric performance. The proposed technique is compared to existing state-of-the-art models to check if it is viable and robust. The outcomes of the suggested model achieved the results in terms of Mean Average Precision (mAP), F1-score, and accuracy of 0.88, 0.912, and 0.98, respectively. Furthermore, as the model’s ability increases with some parameters, the detection time for lesion detection is reduced by two times than the existing models.
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