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| 1 | Optimizing the seed-cell filling performance of an inclined plate seed metering device using integrated ANN-PSO approach显示文摘Uniformseed distribution within the row is the prime objective of precision planters for better crop growth and yield.Inclined plate planters are generally used for sowing bold seeds likemaize,groundnut,chickpea,and their operating parameters like the forward speed of operation,the seedmetering plate inclination,and the seed level in the hopper affect the cell fill and subsequently the uniformseed distribution.Therefore,to achieve precise seed distribution,these parameters need to be optimized.In the present study,out of the different optimization techniques,a new intelligent optimization technique based on the integrated ANN-PSO approach has been used to achieve the set goal.A 3–5-1 artificial neural network(ANN)model was developed for predicting the cell fill of inclined plate seedmetering device,and the particle swarmoptimization(PSO)algorithmwas applied to obtain the optimum values of the operating parameters corresponding to 100%cell fill.The most appropriate optimal values of the forward speed of operation,the seed metering plate inclination,and the seed level in the hopper for achieving 100%cell fill were found to be 3 km/h,50-degree,and 75%of total height,respectively.The proposed integrated ANN-PSO approach was capable of predicting the optimal values of operating parameters with amaximumdeviation of 2%compared to the experimental results,thus confirmed the reliability of the proposed optimization technique. | C.M.Pareek V.K.Tewari Rajendra Machavaram Brajesh Nare | 2021 | Artificial Intelligence in Agriculture2021，，1： | 4 |
| 2 | Image processing based real-time variable-rate chemical spraying system for disease control in paddy crop显示文摘The agrochemical applicationwith conventional sprayers results inwastage of applied chemicals,which not only increases the economic losses but also pollutes the environment.In order to overcome these drawbacks,an image processing based real-time variable-rate chemical spraying systemwas developed for the precise application of agrochemicals in diseased paddy crop based on crop disease severity information.The developed system comprised ofweb cameras for image acquisition,laptop for image processing,microcontroller for controlling the system functioning,and solenoid valve assisted spraying nozzles.The chromatic aberration(CA)based image segmentation method was used to detect the diseased region of paddy plants.The system further calculated the disease severity level of paddy plants,based onwhich the solenoid valves remained on for a specific timeduration so that the required amount of agrochemical could be sprayed on the diseased paddy plants.Field performance of developed sprayer prototype was evaluated in the variable-rate application(VRA)and constant-rate application(CRA)modes.The field testing results showed a minimum 33.88%reduction in applied chemical while operating in the VRA mode as compared with the CRA mode.Hence,the developed systemappears promising and could be used extensively to reduce the cost of pest management as well as to control environmental pollution due to such agrochemicals. | V.K.Tewari C.M.Pareek Gurdeep Lal L.K.Dhruw Naseeb Singh | 2020 | Artificial Intelligence in Agriculture2020，，1： | 2 |
| 3 | A fuzzy logic algorithm derived mechatronic concept prototype for crop damage avoidance during eco-friendly eradication of intra-row weeds显示文摘Crop damage during the intra-row weed eradiation is one of the biggest challenges in intercultural agricultural operations.Several available mechanical systems provide effective weeding but result in excess crop damage.On the other hand,chemical based systems have been raising serious environmental and food concerns.This study presents development of a cost-effectivemechatronic prototype for intra-rowweeding operation.The primary focus was on incurring minimal crop damage.The system integrates time of flight and inductive sensing into fuzzy logic algorithm for electronic control of a four-bar linkage mechanism(FBLM).The crank of FBLM was connected to the vertical rotary weed control shaft with weeding blades.The crop sensing triggers the electronic control to laterally shift the control shaft away from crop,proportional to the forward speed and soil conditions.The developed algorithm incorporates varied conditions of soil,forward speed,and plant spacing to calculate dynamic lateral shift speed(SRPM).The prototype was evaluated to determine the relationships between the operating conditions and electronic control parameters.Moreover,the plant damage was assessed under varied conditions of plant spacing,forward speeds,soil cone index,operational depth and electronic control parameters.The derived SRPM was established as the ultimate governing factor for avoiding crop damage that varied significantlywith electronic response time and soil strength(P<0.05).Plant damage increased significantly under higher forward speeds and lower plant spacing(P<0.05).Preliminary field evaluation of the developed prototype showed a significant potential of this system for effective control on weeds(>65%)and crop damage(<25%). | Satya Prakash Kumar V.K.Tewari Abhilash K.Chandel C.R.Mehta Brajesh Nare C.R.Chethan Kaustubh Mundhada Prateek Shrivastava Chanchal Gupta Smrutilipi Hota | 2020 | Artificial Intelligence in Agriculture2020，，1： | 1 |
| 4 | Environmental Degradation Behavior of High-velocity Arc Sprayed FeCrBMnSi Alloy Coatings in a Coal Fired Boiler显示文摘FeCrBMnSi alloy coating on steel substrate(310S)was fabricated by a high-velocity arc spraying(HVAS)process.Degradation behavior of the coatings at temperatures of 700℃ and 900℃ in air and in the coal fired boiler at(700±10)℃ was studied.X-ray difraction(XRD)and scanning electron microscopy/energydisperse spectroscopy(SEM/EDS)were used to characterize the coating as well as to analyze the corrosion products for elucidating the corrosion mechanisms.Coating exhibited dense and globular morphology and the splats were interconnected as evident from SEM analysis.The maximum microhardness of the coating was found to be 630–1060 HV.The FeCrBMnSi alloy coating has imparted necessary resistance to oxidation and showed good adherence to the 310S substrate during the exposures with no tendency for spallation of its oxide scale. | V.N.Shukla R.Jayaganthan V.K.Tewari | 2013 | Acta Metallurgica Sinica(English Letters)2013，26，5： | 1 |
| 5 | Lightweight convolutional neural network models for semanticsegmentation of in-field cotton bolls显示文摘Robotic harvesting of cotton bolls will incorporate the benefits of manual picking as well as mechanical harvesting. For robotic harvesting, in-field cotton segmentation with minimal errors is desirable which is a challengingtask. In the present study, three lightweight fully convolutional neural network models were developed for thesemantic segmentation of in-field cotton bolls. Model 1 does not include any residual or skip connections,while model 2 consists of residual connections to tackle the vanishing gradient problem and skip connectionsfor feature concatenation. Model 3 along with residual and skip connections, consists of filters of multiplesizes. The effects of filter size and the dropout rate were studied. All proposed models segment the cotton bollssuccessfully with the cotton-IoU (intersection-over-union) value of above 88.0%. The highest cotton-IoU of91.03% was achieved by model 2. The proposed models achieved F1-score and pixel accuracy values greaterthan 95.0% and 98.0%, respectively. The developed models were compared with existing state-of-the-art networks namely VGG19, ResNet18, EfficientNet-B1, and InceptionV3. Despite having a limited number of trainableparameters, the proposed models achieved mean-IoU (mean intersection-over-union) of 93.84%, 94.15%, and94.65% against the mean-IoU values of 95.39%, 96.54%, 96.40%, and 96.37% obtained using state-of-the-art networks. The segmentation time for the developed models was reduced up to 52.0% compared to state-of-theart networks. The developed lightweight models segmented the in-field cotton bolls comparatively faster andwith greater accuracy. Hence, developed models can be deployed to cotton harvesting robots for real-time recognition of in-field cotton bolls for harvesting. | Naseeb Singh V.K.Tewari P.K.Biswas L.K.Dhruw | 2023 | Artificial Intelligence in Agriculture2023，，2： | 0 |
| 6 | An integrated foot transducer and data logging system for dynamic assessment of lower limb exerted forces during agricultural machinery operations显示文摘Agricultural machinery typically requires lower limb actuation forces for operations such as treadling,pedaling and tractor based.However,limited systems exist for assessment of such forces that have ergonomic influence.This study,therefore developed and evaluated a single board computer integrated foot transducer(IFT)and autonomous data logging and visualization systemtomonitor dynamic lower limb exerted forces.The systemconsists of custom developed load sensors sandwiched into foot shaped units that fit operator's both feet.Stamped forces at crank angles for operations typical to pedaling while at height(above ground level)for operation representing typical treadling operations were recorded on-board amemory card and displayed on a liquid crystal display.Evaluations were conducted by imposing external loads that significantly increased(p b 0.05)the foot exerted forces.Force trends were periodic with peaks of 73,85,110.5 and 145.4 N for left foot and 41,50,131.7 and 145.4 N for right foot at loads of 10,30,50 and 70 N,respectively during pedaling operations.Similarly,the left lower actuation limb exerted forces of 139,249 and 255 N at 5,10 and 15 N of imposed loads,respectively during treadling operation.System was also evaluated for tractor operations and exerted forces ranged from 92 to 164 and 107–176 N for clutch pedal engagement at lower to higher tractor speeds on farm and tarmacadam roads,respectively.Similarly,for brake pedal engagement,such forces ranged from106 to 173 and 120–204 N on farm and tarmacadamroads.These forces varied significantly at different forward speeds.Results suggest potential of such system for foot exerted force assessments typical to agricultural machinery systems in real field.Designsmay be evaluated or reconsidered tominimizemusculoskeletal disorder risks during prolonged operations.Work-rest schedules protocols can be developed by ergonomists for safe,efficient and comfortable operations. | Smrutilipi Hota V.K.Tewari Abhilash K.Chandel Gajendra Singha | 2020 | Artificial Intelligence in Agriculture2020，，1： | 0 |
| 7 | Image processing algorithms for in-field cotton boll detection in natural lighting conditions显示文摘In developing countries,the cotton harvesting operation is currently being performed manually.Due to the monotonous nature of this task and the involvement of a considerable amount of labor,this operation becomes very tedious and costly.The harvesting robots can be a good alternative for the selective picking of cotton bolls from the field.In this study,an attempt has been made to develop the image processing algorithms for in-field cotton boll detection in natural lighting conditions for the cotton harvesting robot.Four image processing algorithms namely color difference,band ratio,YCbCr method,and chromatic aberration were proposed for the real-time segmentation of cotton bolls under natural outdoor light conditions.The performance of developed image processing algorithms was evaluated and the experimental results revealed that the chromatic aberration method outperforms as compared to other developed algorithms.The chromatic aberration method showed the highest identification rate of 91.05%with false positive and false negative rates of 6.99%and 4.88%respectively,among all the proposed algorithms.The highest sensitivity and specificitywere found to be 81.31%and 97.53%,respectively,using the chromatic aberration method.Overall,the chromatic aberration approach demonstrated a very promising performance for in-field cotton bolls detection under natural lighting conditions which confirms its applicability for the robotic cotton harvesters. | Naseeb Singh V.K.Tewari P.K.Biswas C.M.Pareek L.K.Dhruwa | 2021 | Artificial Intelligence in Agriculture2021，，1： | 0 |
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