The smart farm is currently a hot topic in the agricultural industry. Due to the complex field environment, the intelligent monitoring model applicable to this environment requires high hardware performance, and there are difficulties in realizing real-time detection of ripe strawberries on a small automatic picking robot, etc. This research proposes a real-time multistage strawberry detection algorithm YOLOv5-ASFF based on improved YOLOv5. Through the introduction of the ASFF (adaptive spatial feature fusion) module into YOLOv5, the network can adaptively learn the fused spatial weights of strawberry feature maps at each scale as a way to fully obtain the image feature information of strawberries. To verify the superiority and availability of YOLOv5-ASFF, a strawberry dataset containing a variety of complex scenarios, including leaf shading, overlapping fruit, and dense fruit, was constructed in this experiment. The method achieved 91.86% and 88.03% for mAP and F1, respectively, and 98.77% for AP of mature-stage strawberries, showing strong robustness and generalization ability, better than SSD, YOLOv3, YOLOv4, and YOLOv5s. The YOLOv5-ASFF algorithm can overcome the influence of complex field environments and improve the detection of strawberries under dense distribution and shading conditions, and the method can provide technical support for monitoring yield estimation and harvest planning in intelligent strawberry field management.

A fresh-cut cauliflower surface defect detection and classification model based on a convolutional neural network with transfer learning is proposed to address the low efficiency of the traditional manual detection of fresh-cut cauliflower surface defects. Four thousand, seven hundred and ninety images of fresh-cut cauliflower were collected in four categories including healthy, diseased, browning, and mildewed. In this study, the pre-trained MobileNet model was fine-tuned to improve training speed and accuracy. The model optimization was achieved by selecting the optimal combination of training hyper-parameters and adjusting the different number of frozen layers; the parameters downloaded from ImageNet were optimally integrated with the parameters trained on our own model. A comparison of test results was presented by combining VGG19, InceptionV3, and NASNetMobile. Experimental results showed that the MobileNet model’s loss value was 0.033, its accuracy was 99.27%, and the F1 score was 99.24% on the test set when the learning rate was set as 0.001, dropout was set as 0.5, and the frozen layer was set as 80. This model had better capability and stronger robustness and was more suitable for the surface defect detection of fresh-cut cauliflower when compared with other models, and the experiment’s results demonstrated the method’s feasibility.

This study revealed the effect of the use of different temperature and calcium chloride (CaCl₂) treatments on the storage quality of fresh-cut “Xuebai” cauliflowers. Fresh-cut “Xuebai” cauliflowers were soaked with 2% CaCl₂ solution at different temperatures. The change in the firmness, color, and ascorbic acid (ASA), total glucosinolates (TGLS), polygalacturonase (PG), and lipoxygenase (LOX) content of fresh-cut “Xuebai” cauliflowers during the cold storage period was assessed. In addition, the sensory quality was also evaluated. The results show that the combined treatments with CaCl₂ at different temperatures could effectively maintain the storage quality of fresh-cut “Xuebai” cauliflowers. Then, a method based on factor analysis with comprehensive quality evaluation was proposed. A factor analysis with a principal component analysis (PCA) was conducted on nine indicators of cauliflowers. Two principal components were extracted with a cumulative contribution rate of 97.513%. The results demonstrated that the treatment with the best fresh-keeping effect of cauliflowers in storage was the combination treatment at 40 °C with 2% CaCl₂ solution, while the optimal storage period was 15 days.

The efficient identification of the field flat jujube is the first condition to realize its automated picking. Consequently, a lightweight algorithm of target identification based on improved YOLOv5 (you only look once) is proposed to meet the requirements of high-accuracy and low-complexity. At first, the proposed method solves the imbalance of data distribution by improving the methods of data enhancement. Then, to improve the accuracy of the model, we adjust the structure and the number of the Concentrated-Comprehensive Convolution Block modules in the backbone network, and introduce the attention mechanisms of Efficient Channel Attention and Coordinate Attention. On this basis, this paper makes lightweight operations by using the Deep Separable Convolution to reduce the complexity of the model. Ultimately, the Complete Intersection over Union loss function and the non-maximum suppression of Distance Intersection over Union are used to optimize the loss function and the post-processing process, respectively. The experimental results show that the mean average precision of improved network reaches 97.4%, which increases by 1.7% compared with the original YOLOv5s network; and, the parameters, floating point of operations, and model size are compressed to 35.39%, 51.27%, and 37.5% of the original network, respectively. The comparison experiments are conducted around the proposed method and the common You Only Look Once target detection algorithms. The experimental results show that the mean average precision of the proposed method is 97.4%, which is higher than the 90.7%, 91.7%, and 88.4% of the YOLOv3, YOLOv4, and YOLOx-s algorithms, and the model size decreased to 2.3%, 2.2%, and 15.7%, respectively. The improved algorithm realizes a reduction of complexity and an increase in accuracy, it can be suitable for lightweight deployment to a mobile terminal at a later stage, and it provides a certain reference for the visual detection of picking robots.

The efficient detection of strawberries has great significance in the realization of strawberry production estimation and automatic picking in the greenhouse. Factors such as the complex growing environment in the field and fruit aggregation shading, especially for immature strawberries at the turning stage and mature strawberries with high similarity in shape, size, and even color, resulted in low accuracy of detection. This research aims at establishing an improved lightweight model based on YOLOv5 which is for strawberry ripeness detection in natural environments, in response to the problems of some current detection models whose structures are complex and difficult to deploy on low-cost devices. The method first adds a smaller target detection layer to the original network, then removes part of the deep structure of the network to reduce the complexity of the model, by adjusting the depth-multiple and width-multiple partially to achieve a lighter model while ensuring accuracy. After that, the performance of the model was further improved by introducing the BiFPN structure and SimAM attention module. The experimental results show that the improved model provides an effective method for detecting strawberries in natural environments. Compared with the YOLOv5s model, the improved model has a 60.35% reduction in parameters and a 55.47% reduction in the model size, and the mAP, mAP0.5:0.95, and F1 are improved to 91.86%, 79.04%, and 90.9%, respectively. The model proposed in this research proves an excellent generalization performance in three strawberry test sets and provides a reference for subsequent deployment in small/low-cost picking robots.