Wood moisture content (WMC) is an important technical index used in the wood drying process, and assessing its change accurately and reliably is the key to improving wood drying quality. In order to improve the accuracy and reliability of WMC forecasting, a modeling method is proposed that uses a modified ant colony algorithm (MACA) to optimize the least square support vector machine (LSSVM). The MACA combines the large-step size global search with the small-step size local fine search to obtain the optimal parameter combination automatically and are tested by five standard functions. Then the MACA-LSSVM model is proposed to predict the WMC and compared with back propagation neural network (BP-NN), LSSVM model, and ant colony optimization LSSVM (ACO-LSSVM). The drying data from a small-sized wood drying kiln independently developed by Northeast Forestry University are taken as the samples for analyzing. The results indicate that the root mean square relative error (RMSRE) obtained by the proposed method (MACA-LSSVM) is only 1.82%, which is 0.77%, 0.50%, and 0.20% less than those of the BP-NN, LSSVM, and ACO-LSSVM models. The forecasting time are 0.0070 s, 0.0030 s, and 0.0010 s shorter, respectively. The relative error (RE) and the mean absolute error (MAE) are also lower than those of the latter three models. The MACA-LSSVM shows the characteristics of low computational complexity, fast convergence speed, high prediction accuracy and strong generalization ability, and the prediction effect is ideal. This model can provide the theoretical support for intelligent control of the wood drying process.

China ranks as the world’s leading potato (Solanum tuberosum L.) producer, while the poor seeding machinery performance limited a higher input–output ratio in potato cultivation and impeded sustainable development. We developed an advanced air-suction mulai-arm potato planter (ASPP) that incorporated integrated side-deep fertilization, automated seed feeding, negative-pressure seed filling, seed transportation, positive-pressure seed delivery, soil covering, and compaction. The study proposes a Negative-pressure seed extraction mechanism that minimizes seed damage by precisely controlling suction pressure, and the near-zero-speed seed delivery mechanism synchronizes seed release with ground speed, reducing bounce-induced spacing errors. Furthermore, the structural configuration and operation principle of ASPP were systematically elucidated, and key performance parameters and optimal values were identified. We conducted a randomized complete block design plot trial comparing the spoon-belt potato planter (SBPP) and spoon-chain potato planter (SCPP), evaluating sowing quality, seedling emergence rate (ER), potato yield (PY), and comprehensive economic benefits. The results revealed that plant spacing index (PSI), missed-seeding index (MI), re-seeding index (RI), and coefficient of variation (CV) of ASPP were 90.05%, 3.78%, 2.32%, and 7.93%, respectively. The mean ER values for ASPP, SBPP, and SCPP were 94.76%, 85.42%, and 83.46%, respectively, with the ASPP showing improvements of 10.93% and 13.54% over SBPP and SCPP. However, the SBPP and SCPP exhibited greater emergence uniformity than ASPP. The mean PY value was 37,205.25, 32,973.75, and 34,620 kg·ha⁻¹ for ASPP, SBPP, and SCPP. The ASPP outperformed the SBPP and SCPP by 12.83% and 7.47%. Overall, ASPP demonstrated balanced and superior performance across the above-mentioned indicators, demonstrating its potential to enable precision agriculture in tuber crop cultivation.

The health of trees has become an important issue in forestry. How to detect the health of trees quickly and accurately has become a key area of research for scholars in the world. In this paper, a living tree internal defect detection model is established and analyzed using model-driven theory, where the theoretical fundamentals and implementations of the algorithm are clarified. The location information of the defects inside the trees is obtained by setting a relative permittivity matrix. The data-driven inversion algorithm is realized using a model-driven algorithm that is used to optimize the deep convolutional neural network, which combines the advantages of model-driven algorithms and data-driven algorithms. The results of the comparison inversion algorithms, the BP neural network inversion algorithm, and the model-driven deep learning network inversion algorithm, are analyzed through simulations. The results shown that the model-driven deep learning network inversion algorithm maintains a detection accuracy of more than 90% for single defects or homogeneous double defects, while it can still have a detection accuracy of 78.3% for heterogeneous multiple defects. In the simulations, the single defect detection time of the model-driven deep learning network inversion algorithm is kept within 0.1 s. Additionally, the proposed method overcomes the high nonlinearity and ill-posedness electromagnetic inverse scattering and reduces the time cost and computational complexity of detecting internal defects in trees. The results show that resolution and accuracy are improved in the inversion image for detecting the internal defects of trees.

In response to the low soil breakage rate and poor flatness of current combined soil preparation equipment for growing potatoes under the clay loam conditions of Northeast China, this paper presents the design of an arc-shaped tooth press device for such equipment, describing its overall structure and working principle. By conducting force analysis on the press roller and shear stress analysis with MATLAB, we obtained the structural parameters and the corresponding value ranges impacting the operational effectiveness of the press device. A three-factor, five-level quadratic regression orthogonal rotational combination test was carried out using EDEM discrete element simulation software, taking the soil breakage rate and flatness as the test indicators. The forward speed, roller tooth arc length, and angle between the roller tooth and the vertical direction (ABRTVD) were the test factors. Design-Expert 8.0.6 software was used for data processing and analysis, and the results showed that the optimal parameter combination consisted of a forward speed of 0.72~1.15 m·s⁻¹, a roller tooth arc length of 58.7 mm, and an ABRTVD of 37.74°, at which point the soil breakage rate was 93.58% and the flatness value was 21.36 mm. The optimal combination of parameters was selected for the field test, resulting in a soil breakage rate of 95.6% and a flatness value of 20.6 mm. The results of the simulation test were found to be consistent with the field test results, thus validating the efficacy of the device design. The findings of this study can provide a reference for enhancing the operational performance of combined soil preparation equipment for growing potatoes under clay loam conditions.

Recently, a cosparse analysis model has been introduced as an interesting alternative to the sparse representation synthesis model. This model is focused on zero elements in the analysis representation vector rather than non-zero elements. Hence, finding cosparse solutions is a problem of important significance in signal processing. In this paper, we construct an adaptive weighted matrix in the greedy analysis pursuit algorithm and propose the reweighed greedy analysis pursuit (ReGAP) algorithm for cosparse signal reconstruction with noise. Using a weighted matrix, we fill the gap between greedy and convex relaxation techniques. Theoretical analysis shows that our algorithm is convergent. We estimate the error bound of ReGAP algorithm with cosparse analysis model, and then simulation results demonstrate that our algorithm is feasible and effective.