Bacteriosis is one of the most common and devastating diseases for peach crops all over the world. Timely identification of bacteriosis disease is necessary for reducing the usage of pesticides and minimize loss of crops. In this proposed work, convolutional neural network (CNN) models using deep learning and an imaging method is developed for bacteriosis detection from the peach leaf images. In the imaging method, disease affected area is quantified and an adaptive operation is applied to a selected suitable channel of the color image. Gray level slicing is done on pre-processed leaf images for segmentation and automatic identification of bacterial spot disease in peach crops. The datasets are augmented to make the algorithm more robust to different illumination conditions. The proposed work compares the result of imaging method and CNN method. Model architectures generated with different deep learning algorithms, had the best performance reaching an accuracy of 98.75%% identifying the corresponding peach leaf [bacterial and healthy] in 0.185 s per image. The test dataset is consist of images from real cultivation field and also from the laboratory conditions. The significantly high identification rate makes the model diagnostic or early warning tool, and an approach that could be further integrated with the unmanned aerial vehicle to operate in real farming conditions

细菌病是全世界桃类作物最常见和最具破坏性的疾病之一。及时查明细菌病是减少农药使用和减少农作物损失的必要条件。在这项拟议的工作中，开发了使用深度学习和成像方法的卷积神经网络（CNN）模型，用于从桃叶图像中检测细菌病。在成像方法中，疾病受影响的区域被量化，并且自适应操作被应用于彩色图像的选择的合适通道。对经过预处理的叶图像进行灰度切片，以对桃类作物中的细菌斑病进行分割和自动识别。增强了数据集，以使算法对不同的照明条件更加稳健。拟议的工作比较了成像方法和CNN方法的结果。使用不同的深度学习算法生成的模型体系结构，在每张图像0.185 s内识别出相应的桃叶（细菌和健康的）时，其最佳性能达到98.75 %%的精度。测试数据集包括来自实际耕种场以及实验室条件的图像。很高的识别率使模型诊断或预警工具成为可能，并且可以与无人飞行器进一步集成以在实际耕种条件下运行的方法