Food security is an important factor in maintaining the livelihood of people around the world. Plant biosecurity mainly deals with analyzing and managing the health of the plant. The biosecurity measures help in reducing the transmission of disease in plants. Environmental factors have a direct influence on determining the growth, stability, and resistance over a variety of diseases. Plants are highly vulnerable to seasonal diseases and the progression increases over time under different environmental conditions. So, it is indeed important to address the problem of protecting the plants from heterogeneous diseases. Many computational techniques have been proposed to early detect the plant disease to protect the crops from devastation. But, the performance of the existing system needs improvement to enhance the predictive ability of the model in challenging situations. In this paper, an effective loss-fused convolutional neural network model is proposed to identify the plants affected with disease of its own type. This system combines the advantages of two different loss functions thereby makes better prediction. The diseases were classified based on the features extracted from the plant leaves in the final layer of the model. The dataset used to perform this experiment is accessed from Plant Village Database. This system attained 98.93% accuracy on discriminating the affected samples over the unaffected one. The result obtained through this model proves its efficacy on the classification of disease affected leaf samples over other existing methodologies.

粮食安全是维持世界人民生计的重要因素。植物生物安全性主要涉及分析和管理植物健康。生物安全措施有助于减少植物中疾病的传播。环境因素直接影响各种疾病的生长，稳定性和抵抗力。植物极易受到季节性疾病的侵害，在不同的环境条件下，其进程会随着时间的推移而增加。因此，解决保护植物免受异质性疾病侵害的问题确实很重要。已经提出了许多计算技术来及早发现植物病害以保护农作物免受破坏。但，现有系统的性能需要改进，以增强在具有挑战性的情况下模型的预测能力。本文提出了一种有效的损失融合卷积神经网络模型，以识别受其自身类型疾病侵害的植物。该系统结合了两个不同损失函数的优点，从而可以进行更好的预测。根据模型最后一层中从植物叶片中提取的特征对疾病进行分类。可从植物村数据库访问用于执行此实验的数据集。该系统在区分受影响的样品和未受影响的样品方面达到了98.93％的准确度。通过该模型获得的结果证明，与其他现有方法相比，该方法可有效地对病害叶片样本进行分类。本文提出了一种有效的损失融合卷积神经网络模型，以识别受其自身类型疾病侵害的植物。该系统结合了两个不同损失函数的优点，从而可以进行更好的预测。根据模型最后一层中从植物叶片提取的特征对疾病进行分类。可从植物村数据库访问用于执行此实验的数据集。该系统在区分受影响的样品和未受影响的样品方面达到了98.93％的准确度。通过该模型获得的结果证明，与其他现有方法相比，该方法可有效地对病害叶片样本进行分类。本文提出了一种有效的损失融合卷积神经网络模型，以识别受其自身类型疾病侵害的植物。该系统结合了两个不同损失函数的优点，从而可以进行更好的预测。根据模型最后一层中从植物叶片提取的特征对疾病进行分类。可从植物村数据库访问用于执行此实验的数据集。该系统在区分受影响的样品和未受影响的样品方面达到了98.93％的准确度。通过该模型获得的结果证明，与其他现有方法相比，该方法可有效地对病害叶片样本进行分类。该系统结合了两个不同损失函数的优点，从而可以进行更好的预测。根据模型最后一层中从植物叶片提取的特征对疾病进行分类。可从植物村数据库访问用于执行此实验的数据集。该系统在区分受影响的样品和未受影响的样品方面达到了98.93％的准确度。通过该模型获得的结果证明，与其他现有方法相比，该方法可有效地对病害叶片样本进行分类。该系统结合了两个不同损失函数的优点，从而可以进行更好的预测。根据模型最后一层中从植物叶片提取的特征对疾病进行分类。可从植物村数据库访问用于执行此实验的数据集。该系统在区分受影响的样品和未受影响的样品方面达到了98.93％的准确度。通过该模型获得的结果证明，与其他现有方法相比，该方法可有效地对病害叶片样本进行分类。与未受影响的样本相比，区分受影响的样本的准确性为93％。通过该模型获得的结果证明，与其他现有方法相比，该方法可有效地对病害叶片样本进行分类。与未受影响的样本相比，区分受影响的样本的准确性为93％。通过该模型获得的结果证明，与其他现有方法相比，该方法可有效地对病害叶片样本进行分类。