Peanut leaf diseases that occur throughout the growth process of peanuts seriously affect the peanut yield and quality. The timely and accurate identification and diagnosis of disease with appropriate early treatment measures can effectively avoid the risk of yield and quality losses caused by leaf lesions. Due to the low professional knowledge level of plant growers, the traditional manual diagnosis exhibits low accuracy and causes manpower wastage. Therefore, the present study proposed an online recognition method for peanut leaf diseases based on the data balance algorithm and deep transfer learning. The data balance algorithm was used to solve the problem of data distribution tilt. Furthermore, transfer learning was used to construct a peanut leaf disease recognition model to enhance the generalisation ability based on the lightweight convolutional neural network by removing the original network output layer, re-adding the normalisation and pooling layers, modifying the fully connected layer, and introducing the regularisation constraint strategies. Finally, the deployment and analysis of the model were completed in low-cost embedded devices. This allowed the rapid on-site identification of the healthy state, black spot disease, brown spot disease, net spot disease and mosaic disease for single leaves. Using the self-built peanut leaf disease dataset, three lightweight convolutional neural networks, namely MobileNet V2, Xception and NasNetMobile, were trained and deployed. Comparative experiments showed that the average macro accuracy for peanut leaf disease recognition reached 0.978, 0.990 and 0.974. The average on-site diagnostic accuracy of peanut leaf diseases exceeded 85%. Thus, the present study provides new methods for the development of a portable peanut leaf disease diagnostic device.

花生整个生长过程中出现的花生叶病严重影响花生的产量和品质。及时、准确地识别和诊断病害，采取适当的早期治疗措施，可以有效避免叶片病害造成的产量和品质损失风​​险。由于种植者的专业知识水平不高，传统的人工诊断准确性低，造成人力浪费。因此，本研究提出了一种基于数据平衡算法和深度迁移学习的花生叶片病害在线识别方法。采用数据平衡算法解决数据分布倾斜问题。此外，迁移学习构建花生叶病害识别模型，基于轻量级卷积神经网络，去除原有网络输出层，重新加入归一化层和池化层，修改全连接层，引入正则化约束策略。最后在低成本的嵌入式设备中完成了模型的部署和分析。这样可以快速现场识别单叶的健康状态、黑斑病、褐斑病、网斑病和花叶病。使用自建的花生叶病数据集，训练部署了三个轻量级卷积神经网络MobileNet V2、Xception和NasNetMobile。对比实验表明，花生叶片病害识别的平均宏观准确率分别达到0.978、0.990和0.974。花生叶病现场平均诊断准确率超过85%。因此，本研究为便携式花生叶病诊断设备的开发提供了新的方法。