Center for Innovation Management Research of Xinjiang, School of Economics and Management, Xinjiang University, Urumqi, Xinjiang, China.
PLoS One. 2022 Feb 1;17(2):e0262629. doi: 10.1371/journal.pone.0262629. eCollection 2022.
Apple tree diseases have perplexed orchard farmers for several years. At present, numerous studies have investigated deep learning for fruit and vegetable crop disease detection. Because of the complexity and variety of apple leaf veins and the difficulty in judging similar diseases, a new target detection model of apple leaf diseases DF-Tiny-YOLO, based on deep learning, is proposed to realize faster and more effective automatic detection of apple leaf diseases. Four common apple leaf diseases, including 1,404 images, were selected for data modeling and method evaluation, and made three main improvements. Feature reuse was combined with the DenseNet densely connected network and further realized to reduce the disappearance of the deep gradient, thus strengthening feature propagation and improving detection accuracy. We introduced Resize and Re-organization (Reorg) and conducted convolution kernel compression to reduce the calculation parameters of the model, improve the operating detection speed, and allow feature stacking to achieve feature fusion. The network terminal uses convolution kernels of 1 × 1, 1 × 1, and 3 × 3, in turn, to realize the dimensionality reduction of features and increase network depth without increasing computational complexity, thus further improving the detection accuracy. The results showed that the mean average precision (mAP) and average intersection over union (IoU) of the DF-Tiny-YOLO model were 99.99% and 90.88%, respectively, and the detection speed reached 280 FPS. Compared with the Tiny-YOLO and YOLOv2 network models, the new method proposed in this paper significantly improves the detection performance. It can also detect apple leaf diseases quickly and effectively.
苹果树病害多年来一直困扰着果园种植户。目前，已有大量研究针对果蔬作物病害检测展开了深度学习研究。由于苹果叶片脉络复杂多样，相似病害难以判断，因此提出了一种新的基于深度学习的苹果叶片病害目标检测模型 DF-Tiny-YOLO，以实现对苹果叶片病害的快速、高效自动检测。选取了包括 1404 张图像在内的四种常见的苹果叶片病害进行数据建模和方法评估，并进行了三方面的主要改进。特征复用与 DenseNet 密集连接网络相结合，进一步实现了减少深层梯度的消失，从而增强特征传播，提高检测精度。引入了 Resize 和 Re-organization (Reorg)，对卷积核进行压缩，减少模型的计算参数，提高了模型的运行检测速度，并允许特征堆叠，实现特征融合。网络末端依次使用 1×1、1×1 和 3×3 的卷积核，实现特征的降维，增加网络深度，而不增加计算复杂度，从而进一步提高检测精度。结果表明，DF-Tiny-YOLO 模型的平均精度（mAP）和平均交并比（IoU）分别为 99.99%和 90.88%，检测速度达到 280 FPS。与 Tiny-YOLO 和 YOLOv2 网络模型相比，本文提出的新方法显著提高了检测性能，能够快速有效地检测苹果叶片病害。
