Early blight and late blight are two of the most prevalent and severe diseases affecting potato crops. Efficient and accurate grading of their severity is crucial for effective disease management. However, existing grading methods are limited to assessing the severity of each disease independently, often resulting in low recognition accuracy and slow grading processes. To address these challenges, this study proposes a novel two-stage approach for the rapid severity grading of both early blight and late blight in potato plants. In this research, two lightweight models were developed: Coformer and SegCoformer. In the initial stage, Coformer efficiently categorizes potato leaves into three classes: those afflicted by early blight, those afflicted by late blight, and healthy leaves. In the subsequent stage, SegCoformer accurately segments leaves, lesions, and backgrounds within the images obtained from the first stage. Furthermore, it assigns severity labels to the identified leaf lesions. To validate the accuracy and processing speed of the proposed methods, we conduct experimental comparisons. The experimental results indicate that Coformer achieves a classification accuracy as high as 97.86%, while SegCoformer achieves an mIoU of 88.50% for semantic segmentation. The combined accuracy of this method reaches 84%, outperforming the Sit + Unet_V accuracy by 1%. Notably, this approach achieves heightened accuracy while maintaining a faster processing speed, completing image processing in just 258.26 ms. This research methodology effectively enhances agricultural production efficiency.

During precision sowing, the contact process between the soil and seeds cannot be ignored. The constitutive relationship of soil is relatively complex, with characteristics such as high nonlinearity, while the contact mechanism between the soil and seeds is unclear. To better understand the contact between seeds and soil, it is necessary to establish a reasonable contact model. Ellipsoidal seeds, such as soybean, red bean, and kidney bean seeds, were adopted as research objects. In this paper, we used the discrete element method to establish an ellipsoidal seed–soil contact model. The JKR + bonding model was adopted for describing the adhesion between soil particles, and the Hertz–Mindlin new restitution (HMNS) model was used for ellipsoidal seed particles to eliminate the multiple contact point issue when modeling with the multi-sphere filling method. Moreover, both simulations and experiments were conducted to calibrate the interaction parameters between soil and seeds. The path of steepest ascent test and Box‒Behnken design (BBD) tests were also used, as well as direct shear tests. Thus, certain soil parameter values were obtained, namely the JKR surface energy was 4.436 J/m², the normal stiffness per unit area was 2.86 × 10⁶ N/m³, the shear stiffness per unit area was 5.54 × 10⁵ N/m³, the critical normal stress was 1833 Pa, and the critical shear stress was 3332 Pa. In addition, the simulation parameters for ellipsoidal seeds were obtained from previous works. Moreover, to obtain more accurate ellipsoidal seed–soil interaction parameters, collision tests, static friction tests, and rolling friction tests were adopted. A single-factor test was used to calibrate the ellipsoidal seed–soil interaction parameters. The calibration results were as follows: the collision restitution coefficients of ellipsoidal seeds with soil were all 0.25. The static friction coefficient of soybeans with soil was 0.6, that of red beans with soil was 0.65, and that of kidney beans with soil was 0.5. The rolling friction coefficient of soybeans with soil was 0.1, that of red beans with soil was 0.14, and that of kidney beans with soil was 0.14. Finally, the rationality of parameter selection was verified through piling tests between ellipsoidal seeds and soil. The relative error of the angle of repose of soybean/soil was 2.99%, that of red bean/soil was 0.60%, and that of kidney bean/soil was 0.55%. Thus, the feasibility and rationality of the contact models between the ellipsoidal seeds and soil established in this paper, as well as the parameter selection, were verified.

Soil sensors have a vital role in precision agriculture. In smart agriculture, soil moisture sensors, soil temperature–humidity sensors, soil pH sensors, soil nutrient sensors, soil pollutant and pest sensors, and wearable sensors detect data to improve crop yields and advance agricultural development. This paper summarizes the types and applications of the above sensors. Soil sensors serve as the core technological engine of smart agriculture, and this review also illustrates that the integration of the Internet of Things and sensors—including plant wearable sensors, which indirectly reflect soil conditions through plant physiological signals—can simultaneously measure or infer parameters, e.g., moisture, temperature, pH, and nutrients, that play a crucial role in precision agriculture. This paper concludes with the future prospects and challenges of soil sensors in smart agriculture, emphasizing the need to address bottlenecks in data fusion, model optimization, and cost control, as well as to advance the implementation of smart agriculture through the development of standards and ecological construction.

The implementation of scientific cultivation practices on soda saline–alkali land plays a pivotal role in safeguarding food security and promoting sustainable agro-economic development at the regional scale. However, there exists a critical knowledge gap regarding the optimization of tillage strategies for rain-fed maize (Zea mays L.) cultivation across heterogeneous saline–alkali soil matrices. This study selected meadow alkaline soil, saline meadow soil, and mild saline–alkali soil under the typical micro-landscape morphological characteristics of soda saline–alkali soil in the Songnen Plain as experimental plots. Under three tillage methods, namely no tillage (NT), rotary tillage + no tillage (RT), and subsoiling + rotary tillage + no tillage (SRT), the effects of the tillage methods on the soil physical properties at the seedling stage, root development at the V6 stage, and yield at the R6 stage during the process of cultivating maize in different types of soils were analyzed. The research results showed that compared with NT and RT, the SRT treatment better improved the physical properties, such as penetration resistance and the bulk density in micro-spaces (0–40 cm), of different soil types. The SRT treatment had a positive impact on the root development of maize seedlings in saline meadow soil and meadow alkaline soil. In terms of yield, compared with the NT treatment, the SRT treatment in meadow alkaline soil and saline meadow soil had a positive effect on the plant height, root dry weight, 1000–grain weight, and grain yield of maize. The increases in maize grain yield were 27.94% and 13.24%, respectively. Compared with NT, the differences in the effects of the SRT and RT treatments on maize yield in mild saline-alkali soil were the smallest, being 6.98% and 4.77%, respectively. The relevant results provide guidance on tillage methods and a theoretical basis for improving the properties of different types of soda saline–alkali soils and increasing maize yield.

Paddy field leveling is an essential step before rice transplanting. During the operation of a paddy field grader, a common issue is the wrapping of rice straw around the blades, resulting in a low rice straw burial rate. This study focused on analyzing the operating parameters of a disc spring–tooth-combined paddy field grader. A soil–straw mechanism simulation model was created using EDEM 2021 software to simulate the field operation status. Firstly, the single-factor test was carried out, with the working speed, the working depth of the disc cutter roller, and the rotation speed of the cutter roller as the factors and the straw-buried rate (SBR) and the machine forward resistance (MFR) as the test indexes, and the parameter range was optimized. The parameters were optimized by the response surface method (RSM) and machine learning algorithms. The results indicated that the genetic algorithm–back propagation (GA-BP) neural network outperformed other optimization models in terms of prediction accuracy and stability. By utilizing the GA-BP regression model and RSM model for regression fitting, two sets of optimal parameter combinations were obtained. Verification experiments were carried out using two sets of parameter combinations. Taking the average of the experimental results, the simulation results showed that the straw burial rate was 93.47% and the forward resistance was 6487 N for the parameter combinations of RSM, and the straw burial rate was 94.86% and the forward resistance was 6352 N for the parameter combinations of GA-BP; the field experiments showed that the straw burial rate was 92.86% and the forward resistance was 6518 N for the parameter combinations of RSM, and the straw burial rate was 95.17% and the forward resistance was 6249 N for the parameter combinations of GA-BP. The results demonstrated that the GA-BP prediction model exhibited better predictive capabilities compared to the traditional RSM, providing more accurate predictions of the paddy field grader’s field operation performance.

Establishing an accurate high-moisture corn ear fragmentation model using the Discrete Element Method is crucial for studying the processing and fragmentation of high-moisture corn ears. This study focuses on high-moisture corn ears during the early harvest stage, developing a fragmentable corn ear model and calibrating its bonding parameters. First, based on the Hertz–Mindlin method in the Discrete Element Method, a three-layer corn cob bonding model consisting of pith, woody ring structure, and glume was established. Through a combined experimental and simulation calibration approach, the bonding parameters of the cob were determined using Plackett–Burman tests, the steepest ascent tests, and Box–Behnken tests. Subsequently, the same method was applied to establish a corn kernel bonding model, with the kernel bonding parameters calibrated through the steepest ascent and Box–Behnken tests. In order to arrange the kernel models on the cob model to achieve the construction of a complete ear model, this paper proposes a “matrix coordinate positioning method”. Through calculations, this method enables the uniform arrangement of corn kernels on the cob, thereby accomplishing the establishment of a composite model for the high-moisture corn ear. The bonding parameters between the cob and kernels were determined through compression tests. Finally, the reliability of the model was partially validated through shear testing; however, potential confounding variables remain unaccounted for in the experimental analysis. While this study establishes a theoretical framework for the design and optimization of machinery dedicated to high-moisture corn ear fragmentation processes, questions persist regarding the comprehensiveness of variable inclusion during parametric evaluation. This analytical approach exhibits characteristics analogous to incomplete system modeling, potentially limiting the generalizability of the proposed methodology.