Due to current global population growth, resource shortages, and climate change, traditional agricultural models face major challenges. Precision agriculture (PA), as a way to realize the accurate management and decision support of agricultural production processes using modern information technology, is becoming an effective method of solving these challenges. In particular, the combination of remote sensing technology and machine learning algorithms brings new possibilities for PA. However, there are relatively few comprehensive and systematic reviews on the integrated application of these two technologies. For this reason, this study conducts a systematic literature search using the Web of Science, Scopus, Google Scholar, and PubMed databases and analyzes the integrated application of remote sensing technology and machine learning algorithms in PA over the last 10 years. The study found that: (1) because of their varied characteristics, different types of remote sensing data exhibit significant differences in meeting the needs of PA, in which hyperspectral remote sensing is the most widely used method, accounting for more than 30% of the results. The application of UAV remote sensing offers the greatest potential, accounting for about 24% of data, and showing an upward trend. (2) Machine learning algorithms displays obvious advantages in promoting the development of PA, in which the support vector machine algorithm is the most widely used method, accounting for more than 20%, followed by random forest algorithm, accounting for about 18% of the methods used. In addition, this study also discusses the main challenges faced currently, such as the difficult problems regarding the acquisition and processing of high-quality remote sensing data, model interpretation, and generalization ability, and considers future development trends, such as promoting agricultural intelligence and automation, strengthening international cooperation and sharing, and the sustainable transformation of achievements. In summary, this study can provide new ideas and references for remote sensing combined with machine learning to promote the development of PA.

In recent years, industrial production has become more and more automated. Machine cutting tool as an important part of industrial production have a large impact on the production efficiency and costs of products. In a real manufacturing process, tool breakage often occurs in an instant without warning, which results a extremely unbalanced ratio of the tool breakage samples to the normal ones. In this case, the traditional supervised learning model can not fit the sample of tool breakage well, which results to inaccurate prediction of tool breakage. In this paper, we use the high precision Hall sensor to collect spindle current data of computer numerical control (CNC). Combining the anomaly detection and deep learning methods, we propose a simple and novel method called CNN-AD to solve the class-imbalance problem in tool breakage prediction. Compared with other prediction algorithms, the proposed method can converge faster and has better accuracy.

As one of the important greenhouse gas, nitrous oxide (N₂O) has attracted much attention globally under climate change context. Agricultural practices are the main sources of greenhouse gas emissions. Nevertheless, scarcity of literature is available on the effects of different tillage measures on soil N₂O emission under spring wheat (Triticum aestivum L.) ecosystem in the semi-arid area of the Loess Plateau. The main objective of the experimental study was to explore the influence of conservation tillage techniques on soil physicochemical properties, nitrous oxide emission and yield in the Northern semi-arid Dingxi region of China. Four treatments viz., conventional tillage (CT), no tillage (NT), straw mulch with conventional tillage (TS) and stubble-return with no-till (NTS) were evaluated under randomized complete block design with three replications. Our results depicted that compared with conventional tillage, bulk density and water content of topsoil was increased and soil pH value was reduced under conservation tillage techniques. Conservation tillage NT, TS and NTS increased organic carbon, TN, MBN and NH₄⁺-N and reduced the accumulation of NO₃^–N. Additionally, although the N₂O emission under NT, TS and NTS was 8.95, 41.90 and 21.05% respectively higher than under T treatment, the corresponding wheat yield was 15.40, 31.97 and 63.21% higher than T treatment. Moreover, correlation analysis showed that soil moisture and temperature were the most significant factors affecting soil N₂O emission. The NTS treatment pointedly increased crop yield without significantly increasing soil N₂O emission. Consequently, based on economic and environmental benefits and considering N₂O emission and crop yield, we suggest that NTS technique is the best conservation tillage strategy in the semi-arid environmental zone of the Loess Plateau of Dingxi China.

Climate change is one of the main challenges, and it poses a tough challenge to the agriculture industry globally. Additionally, greenhouse gas (GHG) emissions are the main contributor to climate change; however, croplands are a prominent source of GHG emissions. Yet this complex challenge can be mitigated through climate-smart agricultural practices. Conservation tillage is commonly known to preserve soil and mitigate environmental change by reducing GHG emissions. Nonetheless, there is still a paucity of information on the influences of conservation tillage on wheat yield, soil properties, and GHG flux, particularly in the semi-arid Dingxi belt. Hence, in order to fill this gap, different tillage systems, namely conventional tillage (CT) control, straw incorporation with conventional tillage (CTS), no-tillage (NT), and stubble return with no-tillage (NTS), were laid at Dingxi, Gansu province of China, under a randomized complete block design with three replications to examine their impacts on yield, soil properties, and GHG fluxes. Results depicted that different conservative tillage systems (CTS, NTS, and NT) significantly (p < 0.05) increased the plant height, number of spikes per plant, seed number per meter square, root yield, aboveground biomass yield, thousand-grain weight, grain yield, and dry matter yield compared with CT. Moreover, these conservation tillage systems notably improved the soil properties (soil gravimetric water content, water-filled pore space, water storage, porosity, aggregates, saturated hydraulic conductivity, organic carbon, light fraction organic carbon, carbon storage, microbial biomass carbon, total nitrogen, available nitrogen storage, microbial biomass nitrogen, total phosphorous, available phosphorous, total potassium, available potassium, microbial counts, urease, alkaline phosphatase, invertase, cellulase, and catalase) while decreasing the soil temperature and bulk density over CT. However, CTS, NTS, and NT had non-significant effects on ECe, pH, and stoichiometric properties (C:N ratio, C:P ratio, and N:P ratio). Additionally, conservation-based tillage regimes NTS, NT, and CTS significantly (p < 0.05) reduced the emission and net global warming potential of greenhouse gases (carbon dioxide, methane, and nitrous oxide) by 23.44, 19.57, and 16.54%, respectively, and decreased the greenhouse gas intensity by 23.20, 29.96, and 18.72%, respectively, over CT. We conclude that NTS is the best approach to increasing yield, soil and water conservation, resilience, and mitigation of agroecosystem capacity.

An improved understanding of the effect of conservation tillage on soil physicochemical quality indicators is obligatory to manage and conserve soil in a climate change scenario. Tillage strategies change soil physicochemical characteristics, consequently modifying crop yields. Conservation tillage is generally used to improve the soil physicochemical characteristics globally. However, the impact of conservation tillage on different soil depths under wheat cultivation is not well documented. A 3-year study was conducted using a randomized complete block design (RCDB). The objective of this research was to specifically study soil physicochemical indicators (soil bulk density, porosity, hydraulic conductivity, water content, temperature, nitrogen, phosphorous, potassium, C:N ratio, pH) and (crop yield) in conventional tillage (CT), straw incorporation into the conventionally tilled soil (CTS), no-tillage (NT), and stubble-retention to the no-tilled soil (NTS) measures under wheat monocropping system across different soil layers. Averaged over 0–40 cm soil layer, the results depicted scarce differences among the tillage practices regarding soil bulk density, porosity, water content and hydraulic conductivity. CT increased soil temperature over conservation tillage systems. Overall, conservation tillage improved soil total nitrogen, available phosphorous, total potassium, C:N ratio and yield than CT, whilst it decreased soil pH. We conclude that NTS and CTS are the best strategies to enhance soil health under wheat mono-cropping system conditions.

Agricultural practices are significant to increase the soil nitrogen and organic carbon sequestration to adapt and mitigate the climate change in a recent climate change scenario. With this background, we carried out research in the Longzhong Loess Plateau region of China. This research was conducted under a randomized complete block design, with three replicates. Adopt the method of combining outdoor positioning field test with indoor index measurement to explore the soil bulk density (BD), nitrogen components (viz., nitrate nitrogen (NO₃⁻-N), ammonia nitrogen (NH₄⁺-N), total nitrogen (TN), microbial biomass nitrogen (MBN) and nitrogen storage (NS), and carbon components [viz., soil organic carbon (SOC), easily oxidized organic carbon (EOC), microbial biomass carbon (MBC) and carbon storage (CS), carbon pool index (CPI), carbon pool activity (A) and carbon pool activity index (AI) and carbon pool management index (CPMI)] and C/N, ratio under different tillage practices [namely., conventional tillage (CT), no tillage (NT), straw mulch with conventional tillage (CTS) and straw mulch with no tillage (NTS)]. Our results depicted that different conservation tillage systems significantly increased soil BD over conventional tillage. Compared with CT, the NTS, CTS and NT reduced soil NO₃⁻-N, increased the soil NH₄⁺-N, TN, MBN and NS, among them, NS under NTS, CTS and NT treatment was 25.0, 14.8 and 13.1% higher than that under CT treatment, respectively. Additionally, conservation tillage significantly increased SOC, EOC, MBC, CS, CPI, AI, CPMI and C/N, ratio than CT. Inside, CS under NTS, CTS and NT treatment was 19.4, 12.1 and 13.4% higher than that under CT treatment, respectively. Moreover, during the 3-year study period, the CPMI under NTS treatment was the largest (139.26, 140.97, and 166.17). Consequently, we suggest that NTS treatment was more sustainable strategy over other investigated conservation tillage practices and should be recommended as climate mitigation technique under climate change context.