经济短缺
卷积神经网络
计算机科学
营养缺乏
图像(数学)
人工智能
召回
任务(项目管理)
精确性和召回率
移动设备
水稻
深度学习
F1得分
营养物
机器学习
模式识别(心理学)
农业工程
农学
工程类
有机化学
化学
哲学
语言学
系统工程
政府(语言学)
操作系统
生物
作者
Majji V. Appalanaidu,G. Kumaravelan
标识
DOI:10.1142/s0219467823400107
摘要
The leaves of plants often display signs of nutritional shortages. Therefore, to identify the nutrient shortages in plants, the color as well as the shape of the leaves can be wielded. Image classification is a quick and efficient methodology for this diagnosis task. In image classification, even though Deep Convolutional Neural Networks (DCNNs) are successful, little emphasis has been paid to their use in detecting plant nutrient deficits. Thus, to classify rice plant nutrient deficits, a DenseNet121 model is proposed in this paper. This proposed technique includes inserting additional new layers, early stopping criteria, model checkpoints, and five-fold cross-validation to enhance the model’s accuracy. After that, the model’s efficacy has been assessed utilizing specific performance metrics like accuracy, [Formula: see text]1 score, precision, and recall. The performance of the suggested model is also analogized with the newer deep learning algorithms. From experimental results, the modified DenseNet121 attained 99.98% of accuracy, 99.99% of Precision, 99.98% of Recall, and 99.97% of [Formula: see text]1-score. Lastly, to classify nutrient deficiencies in rice plants automatically on the web and mobile devices, an application was created for the farmers.
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