Layering Precision Land Leveling and Furrow Irrigated Raised Bed Planting: Productivity and Input Use Efficiency of Irrigated Bread Wheat in Indo-Gangetic Plains

Stagnating yield and declining input use efficiency in irrigated wheat of the Indo-Gangetic Plain (IGP) coupled with diminishing availability of water for agriculture is a major concern of food security in South Asia. The objective of our study was to establish an understanding of how wheat yield and input use efficiency can be improved and how land leveling and crop establishment practices can be modified to be more efficient in water use through layering of precision-conservation crop management techniques. The “precision land leveling with raised bed” planting can be used to improve crop yield, water and nutrient use efficiency over the existing “traditional land leveling with flat” planting practices. We conducted a field experiment during 2002-2004 at Modipuram, India to quantify the benefits of alternate land leveling (precision land leveling) and crop establishment (furrow irrigated raised bed planting) techniques alone or in combination (layering precision-conservation) in terms of crop yield, water savings, and nutrient use efficiency of wheat production in IGP. The wheat yield was about 16.6% higher with nearly 50% less irrigation water with layering precision land leveling and raised bed planting compared to traditional practices (traditional land leveling with flat planting). The agronomic (AE) and uptake efficiency (UE) of N, P and K were significantly improved under precision land leveling with raised bed planting technique compared to other practices.

Artificial Intelligence Enabled Apple Leaf Disease Classification for Precision Agriculture

Precision agriculture enables the recent technological advancements in farming sector to observe,measure,and analyze the requirements of individual fields and crops.The recent developments of computer vision and artificial intelligence(AI)techniques find a way for effective detection of plants,diseases,weeds,pests,etc.On the other hand,the detection of plant diseases,particularly apple leaf diseases using AI techniques can improve productivity and reduce crop loss.Besides,earlier and precise apple leaf disease detection can minimize the spread of the disease.Earlier works make use of traditional image processing techniques which cannot assure high detection rate on apple leaf diseases.With this motivation,this paper introduces a novel AI enabled apple leaf disease classification(AIE-ALDC)technique for precision agriculture.The proposed AIE-ALDC technique involves orientation based data augmentation and Gaussian filtering based noise removal processes.In addition,the AIE-ALDC technique includes a Capsule Network(CapsNet)based feature extractor to generate a helpful set of feature vectors.Moreover,water wave optimization(WWO)technique is employed as a hyperparameter optimizer of the CapsNet model.Finally,bidirectional long short term memory(BiLSTM)model is used as a classifier to determine the appropriate class labels of the apple leaf images.The design of AIE-ALDC technique incorporating theWWO based CapsNetmodel with BiLSTM classifier shows the novelty of the work.Awide range of experiments was performed to showcase the supremacy of the AIE-ALDC technique.The experimental results demonstrate the promising performance of the AIEALDC technique over the recent state of art methods.

Contrast Test of Precision Electric Mist Duster and Electric Sprayer

[Objectives]The paper was to compare and verify the operation performance of precision electric mist duster and electric sprayer,and to select the optimum plant protection equipment suitable for greenhouse application.[Methods]Agents were sprayed by precision electric mist duster and electric sprayer to control tomato downy mildew in greenhouse,and the control effect and pesticide utilization rate were compared.[Results]Compared with electric sprayer,precision electric mist duster improved the pesticide utilization rate by 31.7%,improved the spray efficiency by 20 times,and increased the control effect by 13.2%.[Conclusions]The study provides technical support for the popularization and application of precision electric mist duster in greenhouse and other facilities cultivation.

智能精密播种机的优化设计

智能精密播种机是一种自动化的农业设备,可大幅提高种植效率、减少种植成本。但是,目前智能精密播种机依赖高精度的传感器、控制系统和算法来实现准确的种子投放和深度控制,需要较高的技术要求和技术支持,且对于一些不规则形状的种子或大粒小种的种子,适应性较差。为此,提出了一种智能精密播种机优化方案,并使用高精度传感器和控制系统实现多功能播种,控制种子的投放量和调整播种深度,使用节能设备模块化设计和使用易于更换的部件、数据分析功能和简单易用的用户界面。最后,通过田间试验进行功能性试验,结果表明:经过优化后的智能精密播种机播种变异系数≤5%,满足播种农艺要求,可以实现准确的种子投放和深度控制,避免种植密度不均匀和深度不一致等问题,从而提高种植效率和作物品质。

Design of depth-control planting unit with single-side gauge wheel for no-till maize precision planter

No-till planters are very popular for maize seeding in fields covered with residue in annual wheat-maize double cropping system in North China Plain.However,there is no suitable depth control mechanism for existing no-till maize planters,and as a result,it is hard to obtain consistent planting depth,uniform emergence,and good passing ability at the same time.For the above reasons,a proper planting unit with a new type of depth-control mechanism was developed in this study.The mechanism consists of a single-side gauge wheel,a parallel four-bar linkage,a pair of double-disc opener,a V-shape press wheel and a depth regulator,which can adjust the planting depth from 30 mm to 90 mm to satisfy the agronomic requirement under different field conditions.Based on analysis and calculation,the width of gauge wheel was set to 50 mm while the length of parallel four-bar linkage was set to 350 mm.Field experiment was conducted and the results indicated that the newly designed planting unit with single-side gauge wheel performed well with regard to planting depth uniformity and anti-blocking ability.The planting depth uniformity and seed spacing quality were 95.45%and 91.90%,respectively,when the average height of stubble was 22.5 cm and residue amount was 0.64 kg/m^(2)in the field.It can satisfy the requirement of no-till maize planting on the cropland with residue and stubble in North China Plain.

Design and evaluation of a maize monitoring system for precision planting

To increase the accuracy and real-time performance of on-line assessment of maize planting,a CAN bus based maize monitoring system for precision planting was designed and tested both in laboratory and field.The system was mainly comprised of:(a)seeding rate sensors based on opposite-type infrared photoelectric cell for counting the dropping seeds;(b)a decimeter GPS receiver for acquiring planter position and operation speed;(c)a vehicle monitoring terminal based on ARM Cotex-m4 core chip to acquire and process the whole-system data;(d)a touchscreen monitor to display the planter performance for the operator;and(e)a buzzer alarm to sound a warning when skip and double seeding happened.Taking the applicability,dependability and feasibility of the monitoring system into consideration,the opposite-type infrared photoelectric sensors were selected and their deployment strategies in the 6-port seed tube were analyzed.To decrease the average response time,a distributed information communication structure was adopted.In this information communication mode,collectors were designed for each individual sensor and communicated with sensors through two-wire CAN bus.A sensor together with the designed collector is called a sensor node,and each of them worked individually and took the responsibility for acquiring,processing,and transiting the on-going information.Laboratory test results showed that the random error distribution was approximately normal,and by liner analysis,the system observed value and the true value had as a liner relationship with coefficient of determination R^(2)=0.9991.Series of field tests showed that the seeding rate maximum relative error of the 6-port seed tube was 2.92%,and the maximum root mean square error(RMSE)was about 1.64%.The monitoring system,including sensor nodes,vehicle monitoring terminal and a touch-screen monitor,was proved to be dependable and stable with more than 14 d of continuous experiments in field.

From Parallel Plants to Smart Plants:Intelligent Control and Management for Plant Growth

Precision management of agricultural systems, aiming at optimizing profitability, productivity and sustainability, comprises a set of technologies including sensors, information systems, and informed management, etc. Expert systems are expected to aid farmers in plant management or environment control, but they are mostly based on the offline and static information, deviated from the actual situation. Parallel management, achieved by virtual/artificial agricultural system, computational experiment and parallel execution, provides a generic framework of solution for online decision support. In this paper, we present the three steps toward the parallel management of plant: growth description U+0028 the crop model U+0029, prediction, and prescription. This approach can update the expert system by adding learning ability and the adaption of knowledge database according to the descriptive and predictive model. The possibilities of passing the knowledge of experienced farmers to younger generation, as well as the application to the parallel breeding of plant through such system, are discussed. © 2017 Chinese Association of Automation.

Planting uniformity performance of motor-driven maize precision seeding systems

Low accuracy planting uniformity affects yield.Seed meter type and forward speed typically interfere with the planting uniformity accuracy of motor-driven seeding systems.Two types of maize precision planters equipped with motor-driven planting systems are investigated in this study to ascertain the rule of planting uniformity in both simulated and field speeds.The simulated speed increases from 5 to 12 km/h at a 1 km/h interval in a laboratory environment.The test results show that the quality of feed index(QTFI)of the two planters decreased by 16.79%and 9.88%.This is primarily attributed to the increase in the miss index(MISS)by 11.62%and 9.70%,respectively.The field speed was set to four levels from 5 to 12 km/h in a field environment.The plant spacing scatter distribution results were analyzed,and the results of the two planters indicated that the average positive difference of the two planters linearly increased with the forward speed,and the negative difference of the two planters did not exhibit a linear correlation.The number of positive moving average points was 2.49 times greater than that of the negative moving average points of the finger pick-up maize precision planter,and 4.49 times in the air-suction maize precision planter.The results indicated that the increase of the positive difference of plant spacing is the major effect factor in the field planting uniformity of the two motor-driven maize precision planters.In addition,the plant spacing corresponded to the distribution frequency of the two planters in field was close to the target seed spacing of 25 cm with a max coefficient of variation(CV)of 21.55%and 20.66%,respectively,and those plant spacing values corresponded to max distribution frequency of the two planters at the four level field speeds were(24.69±0.63)cm and(25.63±0.32)cm,respectively.However,the multiples index(MUL)changed randomly affected by the increasing speed.The research results provide a direction for the optimization design of motor-driven maize precision planters.

RPNet: Rice plant counting after tillering stage based on plant attention and multiple supervision network

Rice is a major food crop and is planted worldwide. Climatic deterioration, population growth, farmland shrinkage, and other factors have necessitated the application of cutting-edge technology to achieve accurate and efficient rice production. In this study, we mainly focus on the precise counting of rice plants in paddy field and design a novel deep learning network, RPNet, consisting of four modules: feature encoder, attention block, initial density map generator, and attention map generator. Additionally, we propose a novel loss function called RPloss. This loss function considers the magnitude relationship between different sub-loss functions and ensures the validity of the designed network. To verify the proposed method, we conducted experiments on our recently presented URC dataset, which is an unmanned aerial vehicle dataset that is quite challenged at counting rice plants. For experimental comparison, we chose some popular or recently proposed counting methods, namely MCNN, CSRNet, SANet, TasselNetV2, and FIDTM. In the experiment, the mean absolute error(MAE), root mean squared error(RMSE), relative MAE(rMAE) and relative RMSE(rRMSE) of the proposed RPNet were 8.3, 11.2, 1.2% and 1.6%, respectively,for the URC dataset. RPNet surpasses state-of-the-art methods in plant counting. To verify the universality of the proposed method, we conducted experiments on the well-know MTC and WED datasets. The final results on these datasets showed that our network achieved the best results compared with excellent previous approaches. The experiments showed that the proposed RPNet can be utilized to count rice plants in paddy fields and replace traditional methods.

Reasons Why the Great Pyramids of Giza Remain the Only Surviving Wonder of the Ancient World： Drawing Ideas from the Structure of the Giza Pyramids to Nuclear Power Plants

Selecting a site for a nuclear power plant requires extensive studies to ensure its safety and stability during its operation until its decommissioning. The 4,500-year old Egyptian pyramids at Giza are buildings to learn from. This paper tries to pin down the reasons for the survival of the Giza pyramids in order to reach a criterion for choosing sites for important buildings. It argues that the site selection and the geological properties of the area, being away from seismic effects,, floods and groundwater levels, the stability of the geometric form of the pyramid, the solidity of the structural engineering and precision of execution arguably are the reasons why the Great Pyramids of Giza are the only survivors of the seven wonders of the ancient world.

精密设备防微振基台动力特性分析与研究

电子制程产业属于高投入、高产出的技术密集型行业,其精密工业设备对微振动环境要求较严格,因此防微振基台尤为重要。基于有限元分析与工程实测方法,分析基台板厚对防微振基台结构固有频率、动刚度等动力特性的影响,并结合精密设备容许振动标准对不同厚度基台振动响应进行振动评估,为精密设备防微振基台设计提供理论依据和制程。

水稻精量施肥喷药同步机插控制系统的设计与试验

研究了一种水稻精量侧深施肥、喷药除草同步机插控制系统,以单片机STM32为控制核心硬件,基于FreeRTOS多线程任务,获取插秧机的作业速度,根据插秧施肥、喷药除草的农艺措施要求,确定同步作业数据模型,控制螺旋施肥电机和喷药泵电机的转速,实现施肥量和喷药量与插秧的实时调控,同时开展了不同作业速度、施肥和喷药量条件下的控制系统静态试验。结果表明:各行排肥量稳定性变异系数为1.94%~6.46%,总排肥量稳定性变异系数为3.34%~5.27%,各行喷药量稳定性变异系数为5.21%~7.63%,控制系统性能稳定;田间试验结果表明,施肥准确率为97.61%,喷药准确率为94.47%,能够满足水稻精量侧深施肥和喷药除草同步机插的技术要求;与常规机插种植相比,作业35 d后的杂草防效均在98.00%以上;在减少20%氮肥量、50%除草剂的情况下,能确保水稻产量,提高水稻种植效益。

一型手推式玉米小区播种机研制与试验

由于我国育种单位众多且较分散,考虑投入产出比等经济效益指标影响,面临着小区试验面积小、分布不均等问题。针对玉米小区试验播种用工多、出苗差等问题,研制一种简单实用的手推式玉米小区试验播种机。对比国内外研发推广的小区播种机,该机械对操作人员技术要求不高,操作更简单;对播种地块、种子排列没有严格要求,实用性更强。非常切合我国玉米育种现状,具有很好的应用前景。

四行蔬菜精量种植播种机的研发及应用

针对蔬菜大棚、丘陵和山区等地块蔬菜种植方法主要以人工为主,生产效率低、劳动强度大、无法满足社会发展需求的问题,自行研发设计了一种适合上述区域作业的四行蔬菜精量种植播种机。对播种机的整体结构进行了设计,并阐述了其工作原理及关键技术。该播种机结构先进,自动化程度高,操作简单,运输方便,大大减轻了劳动强度,提高了劳动效率,填补了蔬菜大棚、丘陵和山地等地块播种机械的空白。

农业无人机植保服务体系产业价值评估

农业无人机植保服务是指利用无人机技术对农作物进行保护和管理的一种现代化农业服务方式,具有高效、精准、灵活的特点,能够大幅提升农作物管理的效率和效果。该文评估农业无人机植保体系在农作物保护、病虫害防治、精准农业等方面的应用和优势,通过分析无人机植保服务在提高作业效率、降低农药使用量、节约成本等方面的贡献,揭示其在提升农业生产力和可持续发展中的重要作用,讨论当前无人机植保服务体系面临的挑战和发展前景,为推动农业无人机技术的广泛应用提供参考和建议。

农业植保精准喷雾装备研究与应用特征

随着科技的不断进步和农业现代化的持续推进,农业植保技术实现了快速发展,尤其在精准农业理念影响下,精准喷雾装备的研究与应用逐渐增多。该文以精准农业理念为基础,介绍了精准喷雾技术的定义和内涵,从关键技术研究角度介绍了装备研究情况,说明了典型精准喷雾技术和装备的应用情况、技术特征,总结了农业植保精准喷雾装备发展趋势和精准化、环保化要求,为农业生产更加高效、科学发展提供解决思路。

我国蔬菜精量直播装备技术现状与发展趋势

蔬菜生长周期短,经济效益高,蔬菜产业已成为我国农业发展的重要支撑,但因其种子粒径较小、形状不规则、机械播种难度大等特点,导致目前蔬菜播种机械化程度不高,生产效率较低,严重制约蔬菜产业的进一步发展。以部分典型蔬菜播种机为例,阐述国内外蔬菜播种机的研究现状及先进蔬菜播种技术,指出目前我国蔬菜直播机存在排种器通用性较差、播种效率和精度较低、电气智能化程度不高等问题,蔬菜播种机研发仍处于初级阶段。认为我国未来蔬菜直播机的发展方向将以大型气力式和小型清洁能源的机械式为主,并建议制定农艺标准及规范,提高播种机的通用性、加快新技术应用,为精量蔬菜播种机的研制提供参考。

玉米精量播种区段控制技术研究现状

简述了玉米精量播种区段控制技术的重要意义,指出精量播种区段控制技术是节约良种、提高产量的有效手段,是实现玉米播种节种减损的重要途径。概述了区段控制的发展历程,基于区段控制流程将玉米精量播种区段控制技术分解为种子着床定位、位置关系判断、排种启停控制等3个过程,并介绍了3个过程的主要研究内容和研究历程。综合国内外研究,指出了目前区段控制技术面临的主要技术难点,并结合我国当前玉米播种情况,展望了我国玉米精量播种区段控制技术的发展趋势。

小粒径蔬菜种子双圆盘精量排种器设计与仿真分析

为减少小粒径蔬菜精量播种过程的漏播率、种子损伤率、种子重播率,设计了双圆盘精量排种器,以提高小粒径蔬菜种子排种器的作业效率。结果表明:排种盘最佳直径为200 mm,厚度为2 mm;运用EDEM离散元软件对排种器排种过程进行仿真分析发现,充种初期,种盒中的种子出现反弹现象,随种子数量的增加,逐渐趋于稳定,当排种盘携种时,盒中种子出现扰动现象。通过对种子和种盒之间碰撞机理、种盒材质及其力学特性分析发现,在排种盘转动阶段,种盒底层靠近排种盘的种子间相互作用增加,底层种子出现扰动现象,导致漏播、损伤、重播加剧。

精密种植技术在提高中药材种植效率中的应用

为提高中药材种植效率,将精密种植技术引入其中,不仅能精准控制中药材种植的深度、行距、株距,也能节约劳动成本,进一步提高中药材的种植效率。结合地区项目,分析了精密种植技术在中药材种植中的应用,以发挥精密种植技术优势,进一步提高中药材种植效率。