  Early identification and prevention of various plant diseases in commercial
farms and orchards is a key feature of precision agriculture technology. This
paper presents a high-performance real-time fine-grain object detection
framework that addresses several obstacles in plant disease detection that
hinder the performance of traditional methods, such as, dense distribution,
irregular morphology, multi-scale object classes, textural similarity, etc. The
proposed model is built on an improved version of the You Only Look Once
(YOLOv4) algorithm. The modified network architecture maximizes both detection
accuracy and speed by including the DenseNet in the back-bone to optimize
feature transfer and reuse, two new residual blocks in the backbone and neck
enhance feature extraction and reduce computing cost; the Spatial Pyramid
Pooling (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 activation
improved the model's accuracy due to better nonlinear feature extraction. The
proposed model is tested in detecting four different diseases in tomato plants
under various challenging environments. The model outperforms the existing
state-of-the-art detection models in detection accuracy and speed. At a
detection 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 ($mAP$) value
of $96.29 \%$. Current work provides an effective and efficient method for
detecting different plant diseases in complex scenarios that can be extended to
different fruit and crop detection, generic disease detection, and various
automated agricultural detection processes.
