Automatic Guidance of an Agricultural Tractor along with the Side Shift Control of the Attached Row Crop Cultivator

This paper presents the automatic guidance system of an agricultural tractor and the side shift control of the attached row crop cultivator using electro-hydraulic actuators. In order to simulate the dynamic behaviour of the tractor along with the attached cultivator, the modified bicycle model was adopted. Steering angle sensor, fibre optic gyroscope （FOG） and RTK-DGPS technologies are assumed for measurements of the steering angle, yaw rate and the lateral position of the tractor, respectively. The kinematics model was used for the implement. In this study four cascade controllers were designed and simulated for tractor guidance which consists ofPD, PD, P and PID controllers. Other PI and PID controllers also had been designed for implement side shifting purpose. Then, these two systems were combined and the performance of the whole system was evaluated through the simulation results. According to the results tractor reaches the desired path after less than 10 seconds. Simulations showed that the maximum deviation of the tractor from the desired path was about 5 cm within this period. And the cultivator blades would follow the predetermined path with steady state error of about 5 cm too.

Study on the law of radiant directionality of row crops

The style of crops planting is frequently in row-structure, the row-structure style may result in big difference among the sunlit, shaded soil surface and foliage temperatures and cause pixel component to vary in azimuth orientation, these further lead to the change of radiant directionality of row crops in the zenith and azimuth orientations. Since the row crops are often tackled as isotropic in the azimuth orientation based on continuous vegetation assumption, big errors will be brought about. In order to eliminate the errors, it is necessary to study the law of radiant directionality of the row crops. In this paper, Monte Carlo method has been employed to simulate the angular effects on radiation caused by row architecture parameters. The simulated results show that the parameters, for example, row height, row width, row interval between the neighbor rows and the leaf area index have significant influences on the radiant directionality, but the azimuth orientation ranks the first among the parameters.

Thermal bidirectional gap probability model for row crop canopies and validation

Based on the row structure model of Kimes and the mean gap probability model in single direction, we develop a bidirectional gap probability model for row crop canopies. A concept of overlap index is introduced in this model to consider the gaps and their correlation between the sun and view directions. Multiangular thermal emission data sets were measured in Shunyi, Beijing, and these data are used in model validation in this paper. By comparison with the Kimes model that does not consider the gap probability, and the model considering the gap in view direction only, it is found that our bidirectional gap probability model fits the field measurements over winter wheat much better.

The Effect of Vineyard Cover Crop on Main Monomeric Phenols of Grape Berry and Wine in Vitis viniferal L. cv. Cabernet Sauvignon

This study was conducted to determine the effect of cover crop inter-row in vineyard on main mono-phenol content of grape berry and wine. Three such cover crops, two perennial legumes （white clover and alfalfa） and a perennial gramineous grass （tall fescue） were sown in vineyard. The main phenolic compounds of mature grape berry and wines vinified under the same conditions were extracted with ethyl acetate and diethyl ether and analyzed by high- performance liquid chromatography （HPLC） by comparing to soil tillage. A total of ten phenolic compounds were identified and quantified in the different grape berry and wines, including nonflavonoids （hydroxybenzoic and hydroxycinnamic acids） and flavonoids （flavanols and flavonols）. The concentration of flavonoid compounds （409.43 to 538.63 mg kg^-1 and 56.16 to 81.30 mg L^-1） was higher than nonflavonoids （76.91 to 98.85 mg kg^-1 and 30.65 to 41.22 mg L^-1） for Cabernet Sauvignon grape and wine under different treatments, respectively. In the flavonoid phenolics, Catechin was the most abundant in the different grapes and wines, accounting for 74.94 to 79.70% and 48.60 to 50.62% of total nonanthocyanin phenolics quantified, respectively. Compared to soil tillage, the sward treatments showed a higher content of main mono-phenol and total nonanthocyanin phenolics in grapes and wines. There were significant differences between two cover crop treatments （tall fescue and white clover） and soil tillage for the content of benzoic acid, salicylic acid, caffeic acid, catechin, and total phenolics in the grape berry （P 〈 0.05 or P〈0.01）. The wine from tall fescue cover crop had the highest gallic acid, caffeic acid and catechin. Cover crop system increased the total nonanthocyanin phenolics of grapes and wines in order of the four treatments： tall fescue, white clover, alfalfa, and soil tillage （control）. Cover crop in vineyard increased total phenols of grape berry and wine, and thus improved the quality of wine evidently.

Effects of Tillage and Planting Methods on Narrow and Wide Row Cotton Production

Cotton (Gossypium hirsutum L.) is an economically important crop for the Southern United States. The southern US also has a long growing season suitable for double cropping a second crop after small grains;however, the harvest date for the small grains typically occurs after the optimum planting window for cotton which reduces yield potential. A relay intercropping system was developed at Clemson University that allows interseeding of cotton into standing wheat 2 to 3 weeks before harvest with interseeded cotton yields similar to the conventional mono-cropped cotton. Therefore, the objectives of this study were 1) to determine the optimum tillage and planting methods for narrow row (76-cm) and wide row (97-cm) cotton, and 2) to compare narrow and wide row systems for conventional tillage cotton, cotton interseeded into standing wheat, and cotton planted into a terminated wheat cover crop on coastal plain soil. Two replicated tests were conducted to accomplish these objectives. In Study 1, conventional narrow row cotton combined with a deep tillage operation using Paratill yielded 23% more than conventional wide row cotton which had a deep tillage operation with a subsoiler just before planting. There were no differences between the conventional (97-cm row spacing) mono-crop and interseeded cotton yields. In Study 2, there was no significant difference in yield between narrow-row and wide-row cotton for each cropping system during the two years study. Both wide and narrow-row full season cotton had significantly higher yields than interseeded and cover crop planting systems in year two of the study. The two conservation cropping practices, wheat used as a cover crop and interseeding, showed considerable promise for reducing energy requirements, soil erosion, and wind-borne cotton damage associated with bare soil in conventional tillage. This research demonstrates the benefits of interseeding and narrow row spacing for sustainable cotton production in coastal plain soils of the Southern United States.

基于激光雷达与RGB相机融合的玉米作物行检测算法研究

针对单一传感器在面对复杂田间环境适应性差的问题,本文提出了一种基于固态激光雷达(LiDAR)与RGB相机融合的玉米作物行检测方法。首先,研究了固态激光雷达和RGB相机联合标定方法,同步获取玉米作物行图像和点云数据并进行数据预处理。然后,将预处理后的图像数据和点云数据融合,实现点云“着色”,基于点云“着色”提出聚类感兴趣密度区域算法。利用“着色”点云完成聚类,并结合作物种植农艺标准(行距),分别验证点云信息和颜色信息的可用性,能够选择最优信息完成作物行感兴趣区域聚类。最后,通过划分点云水平条带的方式确定目标点云的特征点聚类区域,取作物行特征点,并利用最小二乘法拟合作物行检测线。仅需调整行距参数,算法可实现全生命周期的作物行检测,利用正常工况下玉米苗期、前期、中期和后期数据开展算法验证,作物行中心线平均误差不大于1.781°,准确率不小于92.69%,平均耗时不超过102.7 ms。此外,为验证算法鲁棒性,开展了复杂农田背景环境,如高杂草背景、断行、苗期杂草高度与玉米高度相近以及玉米完全封行4种工况作物行检测,算法平均误差不大于1.935°,准确率不小于91.94%,平均耗时不超过108.3 ms。通过讨论阐述了基于点云“着色”开展作物行中心线提取的优越性,本文算法可为作物行中心线可靠检测提供参考。

Planting Geometry Effects on the Growth and Yield of Dryland Cotton

The declining Ogallala Aquifer beneath the Southern High Plains may necessitate dryland crop production and cotton (Gossypium hirsutum L.) is a well-adapted and potentially profitable alternative crop. The limited growing season duration of the Texas Panhandle and southwestern Kansas, however, imposes significant production risk due to incomplete boll maturation. Emphasizing earlier boll production that is usually confined to sites on lower fruiting branches may reduce risk, but offsetting high planting densities are needed to maintain desirable lint yield. Our objectives were to quantify planting: 1) row width and 2) in-row spacing effects on growth, yield, and fiber quality of dryland cotton. Field tests of row widths from 0.25 to 0.76 m and plant densities with in-row spacing ranging from 0.075 to 0.15 m were conducted from 1999 to 2005 on a nearly level Pullman clay loam (fine, mixed, superactive, thermic Torrertic Paleustoll) managed in a wheat (Triticum aestivum L.), cotton, fallow (W-Ctn-F) rotation. To expand the basis of comparison, cotton growth and yields were simulated using GOSSYM and long-term (1958-2000) weather records from Bushland, TX, as input for all combinations of 0.38 or 0.76 m row widths and plant spacing of 0.075, 0.10 and 0.15 m. Experimental and computer simulated plant height and harvested boll number increased significantly with increased row spacing and, occasionally, in-row plant spacing. Modeled lint yield for 0.38 m rows decreased by approximately 50% compared with the 582 kg·ha-1 yield for conventional row spacing, which was practically duplicated by field observations in 2001 and 2004. Measured fiber quality occasionally improved with conventional row spacing over ultra-narrow rows, but was unaffected by plant spacing. Because narrow rows and frequent plant spacing did not improve lint yield or fiber quality of dryland cotton, we do not recommend this strategy to overcome a thermally limited growing season.

基于深度学习和高斯过程回归的玉米冠下视觉导航路径提取方法

面对田间作业过程中大型机器机动性差及复杂场景下导航路径拟合精度差的问题,提出一种基于深度学习和高斯过程回归的玉米冠层下导航路径提取方法。首先,基于四足机器人采集玉米冠下作物行图像,对Mask R-CNN实例分割方法进行改进,在特征融合网络引入简化路径增强特征金字塔网络(Simple path aggregation network,Simple-PAN),通过增加自底向上的路径增强模块和特征融合操作模块,提高图像上下文特征的融合能力。其次,以模型识别的冠下作物行目标为基础构建两侧区域分界线,计算可通行区域两侧下垂叶片的分布情况,优化基于加权平均的导航路径算法。对高斯过程回归(Gaussian process regression,GPR)算法进行改进,添加DotProduct线性核对曲线拟合进行优化,优化GPR方法的直线拟合效果。最后,在验证集上进行导航路径识别,计算不同方法拟合导航路径的平均偏差。试验结果表明,该算法能够适应玉米田中叶片遮挡根茎的情况,优化的Mask R-CNN模型具备更高的冠下目标分割精度,基于改进GPR算法拟合的导航线平均偏差为0.7像素,处理一帧分辨率为1280像素×720像素的图像平均耗时为227 ms,该算法能提供在玉米冠层下具备一定避障能力的导航路径,满足导航实时性和准确性的要求。结果可为田间智能农业装备的导航算法研究提供技术与理论支撑。

InstaCropNet:An efficient Unet-Based architecture for precise crop row detection in agricultural applications

Autonomous navigation in farmlands is one of the key technologies for achieving autonomous management in maize fields.Among various navigation techniques,visual navigation using widely available RGB images is a cost-effective solution.However,current mainstream methods for maize crop row detection often rely on highly specialized,manually devised heuristic rules,limiting the scalability of these methods.To simplify the solution and enhance its universality,we propose an innovative crop row annotation strategy.This strategy,by simulating the strip-like structure of the crop row's central area,effectively avoids interference from lateral growth of crop leaves.Based on this,we developed a deep learning network with a dual-branch architecture,InstaCropNet,which achieves end-to-end segmentation of crop row instances.Subsequently,through the row anchor segmen-tation technique,we accurately locate the positions of different crop row instances and perform line fitting.Experimental results demonstrate that our method has an average angular deviation of no more than 2°,and the accuracy of crop row detection reaches 96.5%.

Accurate crop row recognition of maize at the seedling stage using lightweight network

Accurate extraction of crop row is very important for automation of agricultural production.Crop rows are required for accurate machine guidance in agricultural production such as fertilization,plant protection,weeding and harvesting.In this study,an efficient crop row detection algorithm called Crop-BiSeNet V2 was proposed,which combined BiSeNet V2 with a spatial convolutional neural network.The proposed Crop-BiSeNet V2 detected crop rows in color images without the use of threshold and other pre-information such as number of rows.A data set had 2697 maize crop images was constructed in challenging field trial conditions such as variable light,shadows,presence of weeds,and irregular crop shape.The proposed system was experimentally determined to overcome the interference of different complex scenes.And it can be applied to crop rows of different numbers,straight lines and curves.Different analyses were performed to check the robustness of the algorithm.Comparing this algorithm with the Fully Convolutional Networks(FCN)algorithm,it exhibited superior performance and saved 84.85 ms.The accuracy rate reached 0.9811,and the detection speed reached 65.54 ms/frame.The Crop-BiSeNet V2 algorithm proposed in this study show strong generalization performance for seedling crop row recognition.It provides high-reliability technical support for crop row detection research and assists in the study of intelligent field operation machinery navigation.

基于YOLOv8和伪彩色处理的玉米去雄机导航线提取算法

抽雄期玉米的去雄打顶是提高玉米产量的重要任务,但该时期玉米的叶片、雄穗和土壤色彩难以分割,这给玉米去雄机的行间导航任务造成了困难。考虑到导航线提取的关键是在农业机械行驶区域内进行作物特征点的检测,故本文通过YOLOv8神经网络自主提取玉米雄穗感兴趣区域(ROI),并在ROI内采用Jet映射和Otsu算法分割雄穗、绿叶和土壤,然后采用FAST角点检测方法提取雄穗特征点并根据导航区域划分特征点集,最后采用最小二乘法拟合作物行检测线。实验结果表明,该算法实现了精确的、快速的玉米抽雄期导航线提取,处理单帧图像(600 pix×700 pix)平均耗时56.7 ms,平均偏差角为1.03°,能够满足玉米去雄机田间导航行驶的需求。

西北半干旱区葡萄园生草体系中土壤生物学特性与土壤养分的关系

【目的】在葡萄园行间生草条件下研究土壤微生物数量、土壤酶活性与土壤养分的变化及其相关性,阐明土壤生物学指标与土壤肥力的关系。【方法】在酿酒葡萄园行间播种白三叶草、紫花苜蓿和高羊茅,以清耕为对照,测定土壤5种微生物数量、6种土壤酶活性及8个养分指标,分析其变化特征及相互关系。【结果】行间生草总体使土壤微生物数量和土壤酶活性提高,但高羊茅处理使脲酶和蔗糖酶活性降低;行间生草均使土壤有机质含量升高,白三叶草和紫花苜蓿处理使葡萄园土壤碱解N、全N、速效K含量升高,高羊茅处理使其降低,禾本科牧草高羊茅活化P的作用较大。相关分析表明,土壤有机质、全N、碱解N、全P、速效K与土壤细菌、真菌、放线菌、固氮菌、纤维素分解菌、脲酶、碱性磷酸酶、淀粉酶、蔗糖酶、纤维素酶均呈极显著或显著正相关;除过氧化氢酶外,土壤微生物数量与其余酶活性之间也存在极显著正相关关系。而过氧化氢酶与土壤养分和微生物相关性均不显著,全K与所有生物学特性指标无显著相关关系,土壤pH与土壤生物学特性指标呈负相关关系。【结论】行间生草可提高土壤微生物数量、酶活性及土壤肥力,豆科牧草紫花苜蓿和白三叶草提高的效应较明显;土壤生物学指标与土壤养分具有一定的相关性,土壤生物学指标可以反映葡萄园土壤质量的变化。

多光谱多角度遥感数据综合反演叶面积指数方法研究

叶面积指数是陆地生态系统的一个十分重要的结构参数。用遥感数据求取叶面积指数可以利用光谱的信息,比如通过植被指数来拟合一个经验关系,但很多植被指数明显受土壤背景的影响,对于有明显行结构的农作物,土壤的影响很难消除,植被指数的方法误差较大。多角度遥感包含了大量的地面目标的立体结构信息,具备求解植被特征参数的潜力,但通常多角度遥感反演对光谱信息的利用不足。与以往的反演方法相区别,该文利用行播作物二向反射模型,将多角度与多光谱数据结合进行行播作物LAI反演实验,并对反演算法进行了详细的敏感性分析实验,结果表明采用多角度、多光谱遥感数据相结合的方法可以有效反演行播作物的叶面积指数。

不同作物种植方式产出效果的定量评价

应用土地当量比、产量当量、产值当量等不同量化指标,对复种、间作、轮作等不同种植方式的产出效果进行了比较研究。结果表明,与单作一熟农田相比,复种、间作及轮作农田土地利用效率分别提高62%、38%和21%;单位面积产量分别为单作一熟农田的1.63、1.29 和1.16 倍;复种、间作农田单位面积产值分别为单作一熟农田的1.76 和1.35 倍。通过综合分析,进一步明确了评价不同种植方式产出效果的客观方法,为今后农作制度中不同种植方式农田产出效果的比较研究提供理论依据。

行间生草对葡萄园土壤水分含量及贮水量变化的影响

研究葡萄园生草对土壤水分影响,可为葡萄园生草在半干旱地区的应用和推广提供理论依据。在葡萄园种植多年生白三叶草、紫花苜蓿和高羊茅,采用TRIME-FM时域反射仪对生草葡萄园0～80cm土层进行土壤水分定位观测,采用环刀法对0～60cm土层土壤物理性状进行测定。结果表明,生草可改善葡萄园土壤物理性状,使0～60cm土壤容重平均降低8.5%～9.8%,总孔隙度提高11.5%～13.9%。在不同降水年份,葡萄园行间生草均使土壤含水量降低,在2006及2007年,生草使0～80cm土层土壤含水量平均降低幅度分别为2.3%～7.5%及10.7%～15.9%,在冬季贮水量不足、春季干旱的年份土壤含水量降低幅度较大。生草种类不同,对土壤贮水量及增减量的影响存在差异,紫花苜蓿区土壤贮水量较低。

株间除草装置横向偏移量识别与作物行跟踪控制

株间机械除草技术与装置能有效摆脱田间除草的繁重体力劳动并消除化学除草方法所带来的危害,株间机械除草装置的牵引拖拉机在跟踪作物行时总会产生航向偏差,导致除草装置出现横向偏移,甚至无法进入除草的株间区域,同时还会增加伤苗率。为增大株间机械除草的作用区域和降低伤苗率,该文提出了通过作物行信息识别出株间机械除草装置与作物行横向偏移量的方法,并设计了株间机械除草作物行跟踪机构和控制器,实现了株间机械除草跟随作物行。采用正弦波和三角波2种标准信号作为横向偏移补偿量信号,对作物行跟踪控制器的性能进行了测试,试验结果表明:作物行跟踪控制器能较好地控制除草装置跟随横向偏移补偿信号,前进速度为0.5m/s时正弦波信号跟踪最大误差10mm,平均误差0.8mm,三角波信号跟踪最大误差11mm,平均误差1.2mm。除草试验表明,作物行跟踪控制系统能较好地控制株间除草装置跟踪作物行,在0.5m/s前进速度下跟踪最大误差为20.8mm,平均误差2.5mm;作物行跟踪控制明显减少了除草爪齿未进入株间区域的比例,在300mm株距下,可保证93.3%的株间区域有除草爪齿进行除草作业,在200mm株距下为85.9%;作物行跟踪控制降低了除草爪齿对作物的损伤,伤苗率从20%以上降到了12%以内,提高了株间机械除草的作业效果。

行间生草葡萄园土壤微生物量与土壤养分的通径分析

在酿酒葡萄(Vitis vinifera)园行间播种白三叶草(Trifolium repens L.)、紫花苜蓿(Medicago sativa L.)和高羊茅(Festuca arundinacea Schreb),以清耕为对照,研究了土壤微生物量、土壤微生物呼吸强度和土壤微生物熵的变化及其与土壤养分的关系。结果表明:与清耕(对照)相比,白三叶草和紫花苜蓿处理显著提高了土壤有机质、全氮含量,显著降低了速效磷含量;而高羊茅处理土壤有机质和速效钾含量显著低于清耕。白三叶草、紫花苜蓿和高羊茅处理土壤微生物量C含量分别比清耕增加65.2%,61.6%和6.7%;微生物量N含量分别比清耕增加53.6%,52.4%和15.0%。各生草处理土壤微生物熵均显著高于清耕,但土壤微生物呼吸强度与清耕之间无显著差异。微生物学指标总体与土壤有机质、全氮、碱解氮、速效钾呈显著或极显著正相关,与全磷和速效磷呈显著或极显著负相关。通径分析表明,碱解氮、全氮是影响土壤微生物量C、N和微生物呼吸强度、微生物熵重要的直接作用因子。

基于机器视觉和随机方法的作物行提取算法

根据农田作物图像特点,采用基于垂直投影的窗口移动方法,得到代表作物行的定位点,提出了基于机器视觉和随机方法的作物行提取算法。该算法从定位点中随机选取2个不同点,决定一条候选直线,再根据阈值规则,进一步判断候选直线的真实性。实验结果表明,该算法可以提取不同作物的作物行,处理一幅640×480像素的彩色图像平均需要220ms,正确识别率达98%。

基于改进Hough变换的农田作物行快速检测算法

选取苗期农田作为研究对象,采集了包含行栽作物和土壤背景的农田图像,针对现有作物行定位方法易受外界干扰和处理速度较慢的不足,提出将投影法和直接Hough变换法相结合检测作物行的算法。采用2G-R-B法和OTSU法将图像二值化,通过快速中值滤波算法去除噪声,再利用垂直直方图投影将图像进行水平条划分获取作物垄平均定位点,最后通过Hough变换检测垄定位点,得到作物行中心线。试验结果表明:基于垂直直方图投影的Hough变换检测作物行中心线的算法在保证高定位精度的同时,算法处理速度比直接Hough变换检测法提高了3倍,得到的定位基准线能代表作物行走向。

基于垄线平行特征的视觉导航多垄线识别

为有效快速地识别农田多条垄线以实现农业机器人视觉导航与定位,提出一种基于机器视觉的田间多垄线识别与定位方法。使用VC++6.0开发了农业机器人视觉导航定位图像处理软件。该方法通过图像预处理获得各垄行所在区域,使用垂直投影法提取出导航定位点。根据摄像机标定原理与透视变换原理,计算出各导航定位点世界坐标。然后结合垄线基本平行的特征,使用改进的基于Hough变换的农田多垄线识别算法,实现多垄线的识别与定位。使用多幅农田图像进行试验并在室内进行了模拟试验。处理一幅320×240的农田图像约耗时219.4ms,室内试验各垄线导航距与导航角的平均误差分别为2.33 mm与0.3°。结果表明,该方法能有效识别与定位农田的多条垄线,同时算法的实时性也能满足要求。