Agricultural price prediction is a hot research topic in the field of agriculture, and accurate prediction of agricultural prices is crucial to realize the sustainable and healthy development of agriculture. It explores traditional forecasting methods, intelligent forecasting methods, and combination model forecasting methods, and discusses the challenges faced in the current research landscape of agricultural commodity price prediction. The results of the study show that: (1) The use of combined models for agricultural product price forecasting is a future development trend, and exploring the combination principle of the models is a key to realize accurate forecasting; (2) the integration of the combination of structured data and unstructured variable data into the models for price forecasting is a future development trend; and (3) in the prediction of agricultural product prices, both the accuracy of the values and the precision of the trends should be ensured. This paper reviews and analyzes the methods of agricultural product price prediction and expects to provide some help for the development of research in this field.

Since the mature green tomatoes have color similar to branches and leaves, some are shaded by branches and leaves, and overlapped by other tomatoes, the accurate detection and location of these tomatoes is rather difficult. This paper proposes to use the Mask R-CNN algorithm for the detection and segmentation of mature green tomatoes. A mobile robot is designed to collect images round-the-clock and with different conditions in the whole greenhouse, thus, to make sure the captured dataset are not only objects with the interest of users. After the training process, RestNet50-FPN is selected as the backbone network. Then, the feature map is trained through the region proposal network to generate the region of interest (ROI), and the ROIAlign bilinear interpolation is used to calculate the target region, such that the corresponding region in the feature map is pooled to a fixed size based on the position coordinates of the preselection box. Finally, the detection and segmentation of mature green tomatoes is realized by the parallel actions of ROI target categories, bounding box regression and mask. When the Intersection over Union is equal to 0.5, the performance of the trained model is the best. The experimental results show that the F1-Score of bounding box and mask region all achieve 92.0%. The image acquisition processes are fully unobservable, without any user preselection, which are a highly heterogenic mix, the selected Mask R-CNN algorithm could also accurately detect mature green tomatoes. The performance of this proposed model in a real greenhouse harvesting environment is also evaluated, thus facilitating the direct application in a tomato harvesting robot.

The frequent and sharp fluctuations in garlic prices seriously affect the sustainable development of the garlic industry. Accurate prediction of garlic prices can facilitate correct evaluation and scientific decision making by garlic practitioners, thereby avoiding market risks and promoting the healthy development of the garlic industry. To improve the prediction accuracy of garlic prices, this paper proposes a garlic-price-prediction method based on a combination of long short-term memory (LSTM) and multiple generalized autoregressive conditional heteroskedasticity (GARCH)-family models for the nonstationary and nonlinear characteristics of garlic-price series. Firstly, we obtain volatility characteristic information such as the volatility aggregation of garlic-price series by constructing GARCH-family models. Then, we leverage the LSTM model to learn the complex nonlinear relationships between the garlic-price series and the volatility characteristic information of the series, and predict the garlic price. We applied the proposed model to a real-world garlic dataset. The experimental results show that the prediction performance of the combined LSTM and GARCH-family model containing volatility characteristic information of garlic price is generally better than those of the separate models. The combined LSTM model incorporating GARCH and PGARCH models (LSTM-GP) had the best performance in predicting garlic price in terms of evaluation indexes, such as mean absolute error, root mean-square error, and mean absolute percentage error. The combined model of LSTM-GARCH provides the best results in garlic price prediction and can provide support for garlic price prediction.

In order to accomplish accurate mushroom classification and measurement, it is necessary to optimize the existing classification algorithm and measurement devices, as well as to design a specific robot to improve classification accuracy and measurement efficiency. In order to achieve the above objectives, a research-level verification of mushroom grading using Yolov5 + OpenCV and a mushroom measuring system using a resistance strain gauge sensor was carried out. In the aspect of mushroom grading, a method based on the OpenCV visual library was used to identify the minimum quadrilateral outside the mushroom contour, allowing the size of the mushroom to be measured. The experiment’s results show that the method can assess target objects that are occluded with each other under different illumination conditions with 96% accuracy. In the measurement of mushrooms, the strain of the resistance is converted into an analog signal, and the weight of different grades of mushrooms is converted according to the linear relationship after processing by the detection circuit module. Through this method, the error range is successfully controlled within ±0.02 kg, which meets the requirements of accurate measurement of mushrooms. The results of field experiments show that the proposed accurate grading and measurement method of Lentinula edodes is effective and feasible, and provides technical support for the intelligent grading and measurement of Lentinula edodes in production units.

This Research proposes an intelligent pruning method based on the improved Mask R-CNN (Mask Region-based Convolutional Neural Network) model to address the shortcomings of intelligent pruning technology for Sichuan pepper trees. Utilizing ResNeXt-50 as the backbone network, the algorithm optimizes the anchor boxes in the RPN (Region Proposal Network) layer to adapt to the complex morphology of pepper tree branches, thereby enhancing target detection and segmentation performance. Further reducing the quantization error of the RoI (Region of Interest) Align layer through bilinear interpolation, the algorithm innovatively introduces edge loss (L_edge) into the loss function to address the issue of blurred edge features caused by the overlap between retained and pruned branches. Experimental results demonstrate the outstanding performance of the improved Mask R-CNN model in segmenting and identifying pepper tree branches, achieving recognition accuracies of 92.2%, 96.3%, and 85.6% for Upright branches, Centripetal branches, and Competitive branches, respectively, while elevating the recognition accuracy of retained branches to 94.4%. Compared to the original Mask R-CNN, the enhanced model exhibits a 6.7% increase in the recognition rate of retained branches and a decrease of 0.12 in loss value, significantly enhancing recognition effectiveness. The research findings not only provide an effective tool for the precise pruning of pepper trees but also offer valuable insights for implementing intelligent pruning strategies for other fruit trees.

The supply chain of Agaricus bisporus constitutes a complex, multi-layered production and distribution network, encompassing various stages. Challenges in information transmission and process integration between these stages often lead to reduced query efficiency, subsequently compromising the credibility of traceability results and the flexibility of the system. In traditional traceability systems, data for multiple service recipients may be stored on a single blockchain, resulting in complex data structures and making it challenging for users to conduct effective queries and analyses. To address this issue, this paper proposes a solution: a multi-chain tracing model tailored for stakeholders in the traceability process of Agaricus bisporus. This model establishes public chains, enterprise chains, and supervision chains for consumers, enterprises, and regulatory authorities, respectively, to enhance system storage and query efficiency. Additionally, leveraging the modular design of Hyperledger Fabric, the paper optimizes and upgrades the supply chain of Agaricus bisporus. The proposed multi-chain tracing model, designed for traceability for stakeholders, undergoes rigorous validation. Analysis and application results demonstrate that this approach significantly improves the query efficiency and credibility of traceability information while markedly enhancing system flexibility.