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Early identification and prevention of various plant diseases in commercialfarms and orchards is a key feature of precision agriculture technology. Thispaper presents a high-performance real-time fine-grain object detectionframework that addresses several obstacles in plant disease detection thathinder the performance of traditional methods, such as, dense distribution,irregular morphology, multi-scale object classes, textural similarity, etc. Theproposed model is built on an improved version of the You Only Look Once(YOLOv4) algorithm. The modified network architecture maximizes both detectionaccuracy and speed by including the DenseNet in the back-bone to optimizefeature transfer and reuse, two new residual blocks in the backbone and neckenhance feature extraction and reduce computing cost; the Spatial PyramidPooling (SPP) enhances receptive field, and a modified Path Aggregation Network(PANet) preserves fine-grain localized information and improve feature fusion.Additionally, the use of the Hard-Swish function as the primary activationimproved the model's accuracy due to better nonlinear feature extraction. Theproposed model is tested in detecting four different diseases in tomato plantsunder various challenging environments. The model outperforms the existingstate-of-the-art detection models in detection accuracy and speed. At adetection rate of 70.19 FPS, the proposed model obtained a precision value of$90.33 \%$, F1-score of $93.64 \%$, and a mean average precision ($mAP$) valueof $96.29 \%$. Current work provides an effective and efficient method fordetecting different plant diseases in complex scenarios that can be extended todifferent fruit and crop detection, generic disease detection, and variousautomated agricultural detection processes.

A fast accurate fine-grain object detection model based on YOLOv4 deep neural network