Fast and precise detection of litchi fruits for yield estimation based on the improved YOLOv5 model

Abstract

The fast and precise detection of dense litchi fruits and the determination of their maturity is of great practical significance for yield estimation in litchi orchards and robot harvesting. Factors such as complex growth environment, dense distribution, and random occlusion by leaves, branches, and other litchi fruits easily cause the predicted output based on computer vision deviate from the actual value. This study proposed a fast and precise litchi fruit detection method and application software based on an improved You Only Look Once version 5 (YOLOv5) model, which can be used for the detection and yield estimation of litchi in orchards. First, a dataset of litchi with different maturity levels was established. Second, the YOLOv5s model was chosen as a base version of the improved model. ShuffleNet v2 was used as the improved backbone network, and then the backbone network was fine-tuned to simplify the model structure. In the feature fusion stage, the CBAM module was introduced to further refine litchi’s effective feature information. Considering the characteristics of the small size of dense litchi fruits, the 1,280 × 1,280 was used as the improved model input size while we optimized the network structure. To evaluate the performance of the proposed method, we performed ablation experiments and compared it with other models on the test set. The results showed that the improved model’s mean average precision (mAP) presented a 3.5% improvement and 62.77% compression in model size compared with the original model. The improved model size is 5.1 MB, and the frame per second (FPS) is 78.13 frames/s at a confidence of 0.5. The model performs well in precision and robustness in different scenarios. In addition, we developed an Android application for litchi counting and yield estimation based on the improved model. It is known from the experiment that the correlation coefficient R2 between the application test and the actual results was 0.9879. In summary, our improved method achieves high precision, lightweight, and fast detection performance at large scales. The method can provide technical means for portable yield estimation and visual recognition of litchi harvesting robots.

Integrating environmental and satellite data to estimate county-level cotton yield in Xinjiang Province

“…Support vector regression (SVR), random forest regression (RFR), convolution neural networks (CNNs), and long short-term memory networks (LSTM) have successfully estimated the yield of various crop types, considering the effects of climate change at the pixel or county scale ( Gopal and Bhargavi, 2019 ; Sun et al., 2019 ; Khaki et al., 2020 ). To enhance ML and DL methods, the ensemble Bayesian model averaging (EBMA) and You Look Only Once version 5 (YOLOv5) which are improved models also applied ( Wang et al., 2022 ; Fei et al., 2023 ). Furthermore, deep learning adaptive crop model (DACM) is proposed considering the spatial heterogeneity of large areas for yield estimation ( Zhu et al., 2022 ).…”

Immature Green Apple Detection and Sizing in Commercial Orchards Using YOLOv8 and Shape Fitting Techniques

“…Furthermore, the potential of the YOLOv5-based models has been assessed in detecting litchi fruits for yield estimation [47],spikelet detection in grapes [48] and green pepper detection [49].…”

Design of field real-time target spraying system based on improved YOLOv5

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