A tomato harvesting robot was developed in this study, which consisted of a four-wheel independent steering system, a 5-DOF harvesting system, a navigation system, and a binocular stereo vision system. The four-wheel independent steering system was capable of providing a low-speed steering control of the robot based on Ackerman steering geometry. The proportional-integral-derivative (PID) algorithm was used in the laser navigation control system. The Otsu algorithm and the elliptic template method were used for the automatic recognition of ripe tomatoes, and obstacle avoidance strategies were proposed based on the C-space method. The maximum average absolute error between the set angle and the actual angle was about 0.14°, and the maximum standard deviation was about 0.04°. The laser navigation system was able to rapidly and accurately track the path, with the deviation being less than 8 cm. The load bearing capacity of the mechanical arm was about 1.5 kg. The success rate of the binocular vision system in the recognition of ripe tomatoes was 99.3%. When the distance was less than 600 mm, the positioning error was less than 10 mm. The time needed for recognition of ripe tomatoes and pitching was about 15 s per tomato, with a success rate of about 86%. This study provides some insights into the development and application of tomato harvesting robot used in the greenhouse.

Perillae has attracted an increasing interest of study due to its wide usage for medicine and food. Estimating quality and maturity of a perillae requires the information with respect to its size. At present, measuring and sorting the size of perillae mainly depend on manual work, which is limited by low efficiency and unsatisfied accuracy. To address this issue, in this study, we develop an approach based on the machine vision (MV) technique for online measuring and size sorting. The geometrical model and the corresponding mathematical model are built for perillae and imaging, respectively. Based on the built models, the measuring and size sorting method is proposed, including image binarization, key point determination, information matching, and parameter estimation. Experimental results demonstrate that the average time consumption for a captured image, the average measuring error, the variance of measuring error, and the overall sorting accuracy are 204.175 ms, 1.48 mm, 0.07 mm, and 93%, respectively, implying the feasibility and satisfied accuracy of the proposed approach.

Judging the efficiency of agricultural machinery operations is the basis for evaluating the utilization rate of agricultural machinery, the driving abilities of operators, and the effectiveness of agricultural machinery management. A range of evaluative factors—including operational efficiency, oil consumption, operation quality, repetitive operation rate, and the proportion of effective operation time—must be considered for a comprehensive evaluation of the quality of a given operation, an analysis of the causes of impact, the improvement of agricultural machinery management and an increase in operational efficiency. In this study, the main factors affecting the evaluation of agricultural machinery operations are extracted, and information about the daily operations of particular items of agricultural machinery is taken as a data source. As regards modeling, a subset of data can be scored manually, and the remaining data is predicted after the training of the relevant model. With a large quantity of data, manual scoring is not only time-consuming and labor-intensive, but also produces sample errors due to subjective factors. However, a small number of samples cannot support an accurate evaluation model, and so in this study a semi-supervised learning method was used to increase the number of training samples and improve the accuracy of the least-squares support vector machine (LSSVM) training model. The experiment used 33,000 deep subsoiling operation data, 500 of which were used as training samples and 500 as test samples. The accuracy rate of the model obtained using 500 training samples was 94.43%, and the accuracy rate achieved with this method with an increased number of training samples was 96.83%. An optimal combination of agricultural machinery and tools is recommended owing to their operational benefits in terms of reduced costs and improved operating capacity.

Aiming at the problem of insufficient research on the importance evaluation of agricultural machinery spare parts in the process of cross-region operation of combine harvester, Based on CRITIC and TOPSIS, an evaluation model of the importance of spare parts for cross region combine harvesters was established. The CRITIC model was used to calculate the weight of each evaluation index, the weighted TOPSIS evaluation model was used to process the data, and the relative closeness between the spare parts of each harvester to be evaluated and the ideal solution was calculated. Finally, the spare parts resource management decision-making system platform is developed to effectively integrate the spare parts resource allocation. The results show that the model can reasonably and effectively evaluate the important demand degree of combine har-vester spare parts, and has a good reference value for the cooperative service of agricultural machinery service vehicles and the priority degree of spare parts loading.