Agriculture

Precision agriculture

Physics

Geology

Horticulture

Pattern recognition (psychology)

Artificial intelligence

Ecology

Acoustics

Degree (music)

Object detection

Orchard

Computer science

Remote sensing

Biology

Computers and Electronics in Agriculture

Real-time detection of agricultural growth stages is one of the key steps of estimating yield and intelligent spraying in commercial orchards. However, due to considerable degree of occultation in surrounding leaves, significant overlapping between neighboring fruits, differences in size, color, cluster density, and other growth characteristics, traditional detection methods have the limitation in the accuracy of detecting different growth phases. The current work proposes a real-time object detection framework Dense-YOLOv4 based on an improved version of the YOLOv4 algorithm by including DenseNet in the backbone to optimize feature transfer and reuse. Furthermore, a modified path aggregation network (PANet) has been implemented to preserve fine-grain localized information. The model has been applied to detect different growth stages of mango with high degree of occultation in a complex orchard scenario. At a detection rate of 44.2 FPS, the mean average precision (mAP) and F1-score of the proposed model have reached up to 96.20% and 93.61%, respectively. The proposed Dense-YOLOv4 has outperformed the state-of-the-art YOLOv4 with 7.94%,13.10%,10.47%, and 4.73% increase in precision, recall, F1-score, and mAP, respectively. The present work provides an effective and efficient framework to detect different growth stages under a complex orchard scenario and can be extended to different fruit and crop detection, disease detection, and different automated agricultural applications.

Real-time growth stage detection model for high degree of occultation using DenseNet-fused YOLOv4