Rapid economic growth since the turn of the century has often been accompanied by significant challenges, including fossil fuel depletion, environmental degradation, and energy security concerns. Urgent measures are essential to promote environmentally friendly advancements and adopt sustainable energy solutions. Biomass energy, an important component of renewable energy, stands out as the sole renewable energy source containing carbon and has attracted significant attention from governments and the scientific community worldwide. Attention to biomass conversion technologies and their practical applications has gradually increased. This paper provides an in-depth analysis of the utilization of biomass and its wastes, and systematically introduces the progress of the application of biomass conversion technologies, including biochemical and thermochemical conversion, to provide readers with a clear picture of the technological development. By meticulously summarizing the current status of the application of different products produced by these technologies, it provides a valuable reference for researchers and practitioners in the field of biomass energy, aiming to meet the challenges of clean energy production and biomass waste management, and to mitigate the adverse impacts of human activities on the environment. In addition, this paper explores the application of machine learning in the field of biomass conversion, especially its potential in optimizing the biomass conversion process, improving the accuracy of energy yield prediction, and enhancing process control. Despite challenges such as data quality and model interpretability, developments in machine learning, particularly advances in feature engineering and interpretable AI, promise to address these issues. This study contributes positively to advancing biomass energy technologies.

Widespread infestation of pests and pathogens during winter wheat’s heading stage poses significant risks to yield loss. In this study, an assessment model of health degree (HD) of winter wheat under field conditions was established by using unmanned aerial vehicle remote sensing (UAV RS) imagery. Firstly, non-photosynthetic features were identified from the UAV RS imagery based on different machine learning methods, including Minimum Distance (MD), Maximum Likelihood Estimation (MLE), and Support Vector Machine (SVM). Classification results indicated that MD demonstrates the best performance, according to the values of Overall Accuracy (0.898), Kappa Coefficient (0.863), and Precision (0.856). Therefore, the inversion model between the proportion of pixels classified as non-photosynthetic features and the corresponding ground truth of the incidence of non-photosynthetic features was established. Coefficient of determination (R²), RMSE (root mean square error), and RRMSE (Relative RMSE) of the inversion model are 0.73, 4.86%, and 19.81%, respectively, demonstrating strong correlation and high accuracy. Subsequently, an assessment model for HD of the wheat field was generated based on the predicted incidence of the non-photosynthetic features, and the conclusion was reached that HD1 (pre-symptoms of the infestation of pests and pathogens) dominated in the wheat field, with the proportion of area as 56.16%, while HD4 and HD5 (severe infestation of pests and pathogens) were negligible, with proportions of area of 2.29% and 17.75%. Finally, the assessment model of HD was used to simulate the precision OSMP (One-Spray-Multiple-Protection), and the agricultural chemical could be reduced to 69.11% of the conventional OSMP operation, which provides theoretical and methodological support for the reduction of agricultural chemicals in the domain of precision agriculture.

While straw mulching has been recognized for mitigating compaction, the multifactorial effects of straw parameters (content, length, laying modes) under static versus dynamic loads remain poorly quantified. Straw mulching may alter the stress transfer in the soil when applying static or dynamic loads. This study systematically evaluated stress and energy dissipation mechanisms using laboratory simulations: a plate sinkage test and an adapted Proctor test. The results demonstrated that the straw content (0-20 Mg/hm²) dominantly governs dissipation efficiency, with maximum stress dissipation ratios of 45.6% (static load >200 kPa) and energy dissipation ratios of 38.64% (dynamic high-energy). Longer straw (0.20 m) and ordered laying modes enhanced stress dispersion only under low static loads, while dynamic loads exhibited weaker dissipation. The study reveals that the damping effect of straw is strongest under low stress static load, so it is necessary to reduce the compaction of agricultural machinery and optimize the allocation of straw, such as 15-20 Mg/hm², to alleviate compaction in clay loam soils. These findings can provide actionable insights for designing straw-based soil conservation strategies and improving compaction prediction models in mechanized agriculture.

The facility-based production method is an important stage in the development of modern agriculture, lifting natural light and temperature restrictions and helping to improve agricultural production efficiency. To address the problems of difficulty and low accuracy in detecting pests and diseases in the dense production environment of tomato facilities, an online diagnosis platform for tomato plant diseases based on deep learning and cluster fusion was proposed by collecting images of eight major prevalent pests and diseases during the growing period of tomatoes in a facility-based environment. The diagnostic platform consists of three main parts: pest and disease information detection, clustering and decision-making of detection results, and platform diagnostic display. Firstly, based on the You Only Look Once (YOLO) algorithm, the key information of the disease was extracted by adding attention module (CBAM), multi-scale feature fusion was performed using weighted bi-directional feature pyramid network (BiFPN), and the overall construction was designed to be compressed and lightweight; Secondly, the k-means clustering algorithm is used to fuse with the deep learning results to output pest identification decision values to further improve the accuracy of identification applications; Finally, a detection platform was designed and developed using Python, including the front-end, back-end, and database of the system to realize online diagnosis and interaction of tomato plant pests and diseases. The experiment shows that the algorithm detects tomato plant diseases and insect pests with mAP (mean Average Precision) of 92.7%, weights of 12.8 Megabyte (M), inference time of 33.6 ms. Compared with the current mainstream single-stage detection series algorithms, the improved algorithm model has achieved better performance; The accuracy rate of the platform diagnosis output pests and diseases information of 91.2% for images and 95.2% for videos. It is a great significance to tomato pest control research and the development of smart agriculture.

Biomass gasification process generates the residual tar, which in turn exerts some negative influence on biomass gasification system. To reduce this harmful influence, an evaluation of the tar properties during the biomass gasification was studied. The chemical composition and pyrolysis behavior of biomass tar were investigated. The complex chemical composition of the tar, which includes phenol derivatives, naphthalene derivatives, other macromolecular aromatic compounds, furans, and other compounds (carbon number from 7 to 14), was established by gas chromatography-mass spectrometry technique. Thermogravimetric analysis was performed with two heating rates (10°C/min and 20°C/min), and Coats-Redfern method was applied to assess the kinetic parameters, i.e., the activation energy (E) and pre-exponential factor (A) of tar thermo-chemical decomposition. The results showed that the main degradation of tar is a two-step process, including a volatilization step at lower temperatures (<105°C) and a pyrolysis step at higher temperatures (105°C-380°C). The application of the Coats-Redfern method revealed a variation trend of the activation energy during the decomposition of tar in a non-isothermal model. It shows that high temperature is more conducive to tar pyrolysis. By adjusting the temperature to control the generation and removal of tar, new approaches are provided for designing and optimizing biomass gasification systems.

Recognition of the boundaries of farmland plow areas has an important guiding role in the operation of intelligent agricultural equipment. To precisely recognize these boundaries, a detection method for unmanned tractor plow areas based on RGB-Depth (RGB-D) cameras was proposed, and the feasibility of the detection method was analyzed. This method applied advanced computer vision technology to the field of agricultural automation. Adopting and improving the YOLOv5-seg object segmentation algorithm, first, the Convolutional Block Attention Module (CBAM) was integrated into Concentrated-Comprehensive Convolution Block (C3) to form C3CBAM, thereby enhancing the ability of the network to extract features from plow areas. The GhostConv module was also utilized to reduce parameter and computational complexity. Second, using the depth image information provided by the RGB-D camera combined with the results recognized by the YOLOv5-seg model, the mask image was processed to extract contour boundaries, align the contours with the depth map, and obtain the boundary distance information of the plowed area. Last, based on farmland information, the calculated average boundary distance was corrected, further improving the accuracy of the distance measurements. The experiment results showed that the YOLOv5-seg object segmentation algorithm achieved a recognition accuracy of 99% for plowed areas and that the ranging accuracy improved with decreasing detection distance. The ranging error at 5.5 m was approximately 0.056 m, and the average detection time per frame is 29 ms, which can meet the real-time operational requirements. The results of this study can provide precise guarantees for the autonomous operation of unmanned plowing units.

Harvesting represents the crucial stage in the cultivation process of Agaricus bisporus mushrooms. An important way for the production process of Agaricus bisporus to reduce costs and increase income is to ensure timely harvest of Agaricus bisporus, reduce harvesting costs, and improve harvesting efficiency. There are many disadvantages in manual picking, such as high labor intensity, time-consuming work and high cost. In this study, a set of mushroom picking platform including climbing mechanism, picking robot, and control system was designed and developed. The picking robot consisted of a truss mechanism, an image acquisition device, a mushroom collection device, and a picking actuator. The profile picking actuator could realize the function of constant force clamping. An online size detection algorithm for Agaricus bisporus based on deep image processing was proposed. The algorithm included removal of abnormal noise points, background segmentation, coordinate conversion, and diameter detection. The precision picking system for Agaricus bisporus with coordinate compensation function controlled by Industrial Personal Computer was designed, and the visual control interface was developed based on Labview. Through the performance test, the reliability of machine vision recognition and the overall operating stability of the picking platform were verified. The test results showed that in the process of machine vision recognition, the recognition accuracy rate was higher than 92.50%, the missed detection rate was lower than 4.95%, the false detection rate was lower than 2.15%, and the diameter measurement error was less than 4.50%. The image processing algorithm had high recognition rate and small diameter measurement error, which could meet the requirements of picking operation. The picking platform’s picking success rate was higher than 95.45%, the picking damage rate was lower than 3.57%, and the picking output rate was higher than 87.09%. Compared with manual picking, the recognition accuracy rate of the picking platform was increased by 6.70%, the picking output rate was increased by 1.51%. The overall performance of the picking platform was stable and practical.