Precise and timely detection of a crop’s nutrient requirement will play a crucial role in assuring optimum plant growth and crop yield. The present study introduces a reliable deep learning platform called “Deep Learning-Crop Platform” (DL-CRoP) for the identification of some commercially grown plants and their nutrient requirements using leaf, stem, and root images using a convolutional neural network (CNN). It extracts intrinsic feature patterns through hierarchical mapping and provides remarkable outcomes in identification tasks. The DL-CRoP platform is trained on the plant image dataset, namely, Jammu University-Botany Image Database (JU-BID), available at https://github.com/urfanbutt. The findings demonstrate implementation of DL-CRoP—cases A (uses shoot images) and B (uses leaf images) for species identification for Solanum lycopersicum (tomato), Vigna radiata (Vigna), and Zea mays (maize), and cases C (uses leaf images) and D (uses root images) for diagnosis of nitrogen deficiency in maize. The platform achieved a higher rate of accuracy at 80–20, 70–30, and 60–40 splits for all the case studies, compared with established algorithms such as random forest, K-nearest neighbor, support vector machine, AdaBoost, and naïve Bayes. It provides a higher accuracy rate in classification parameters like recall, precision, and F1 score for cases A (90.45%), B (100%), and C (93.21), while a medium-level accuracy of 68.54% for case D. To further improve the accuracy of the platform in case study C, the CNN was modified including a multi-head attention (MHA) block. It resulted in the enhancement of the accuracy of classifying the nitrogen deficiency above 95%. The platform could play an important role in evaluating the health status of crop plants along with a role in precise identification of species. It may be used as a better module for precision crop cultivation under limited nutrient conditions.
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机译：准确及时地检测作物的营养需求，对于确保植物的最佳生长和作物产量至关重要。本研究引入了一种可靠的深度学习平台，称为“深度学习-作物平台”（DL-CRoP），用于使用卷积神经网络 （CNN） 使用叶、茎和根图像识别一些商业种植的植物及其营养需求。它通过分层映射提取内在特征模式，并在识别任务中提供显着的成果。DL-CRoP 平台是在植物图像数据集上训练的，即查谟大学植物学图像数据库 （JU-BID），可在 https://github.com/urfanbutt 获得。研究结果证明了 DL-CRoP——案例 A（使用芽图像）和 B（使用叶子图像）用于 Solanum lycopersicum（番茄）、Vigna radiata（Vigna）和 Zea mays（玉米）的物种鉴定，以及案例 C（使用叶子图像）和 D（使用根图像）用于诊断玉米缺氮。与随机森林、K 最近邻、支持向量机、AdaBoost 和朴素贝叶斯等成熟算法相比，该平台在所有案例研究中实现了 80-20、70-30 和 60-40 次拆分的更高准确率。它为案例 A （90.45%）、B （100%） 和 C （93.21） 的召回率、精度和 F1 分数等分类参数提供了更高的准确率，而案例 D 的中等准确率为 68.54%。为了进一步提高案例研究 C 中平台的准确性，对 CNN 进行了修改，包括一个多头注意力 （MHA） 块。它导致氮缺乏分类的准确性提高到 95% 以上。该平台可以在评估农作物的健康状况以及精确识别物种方面发挥重要作用。它可以用作在有限营养条件下进行精确作物种植的更好模块。
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