Light Drone-Based Application to Assess Soil Tillage Quality Parameters

Fanigliulo, Roberto; Antonucci, Francesca; Figorilli, Simone; Pochi, Daniele; Pallottino, Federico; Fornaciari, Laura; Grilli, Renato; Costa, Corrado

doi:10.3390/s20030728

Cited by 21 publications

(16 citation statements)

References 19 publications

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“…To evaluate the seedbed quality, soil coverage by crop residues, surface roughness and cloddiness in the tilled layer were measured [31]. Generally, conventional primary tillage increases soil roughness and cloddiness when compared to the no-tillage system or minimum tillage [32,33].…”

Section: Introductionmentioning

confidence: 99%

Conventional and Conservation Seedbed Preparation Systems for Wheat Planting in Silty-Clay Soil

Fanigliulo¹,

Pochi²,

Servadio³

2021

Sustainability

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Conventional seedbed preparation is based on deep ploughing followed by lighter and finer secondary tillage of the superficial layer, normally performed by machines powered by the tractor’s Power Take-Off (PTO), which prepares the seedbed in a single pass. Conservation methods are based on a wide range of interventions, such as minimum or no-tillage, by means of machines with passive action working tools which require two or more passes The aim of this study was to assess both the power-energy requirements of conventional (power harrows and rotary tillers with different working width) and conservation implements (disks harrow and combined cultivator) and the soil tillage quality parameters, with reference to the capability of preparing an optimal seedbed for wheat planting. Field tests were carried out on flat, silty-clay soil, using instrumented tractors. The test results showed significant differences among the operative performances of the two typologies of machines powered by the tractor’s PTO: the fuel consumption, the power and the energy requirements of the rotary tillers are strongly higher than power harrows. However, the results also showed a decrease of these parameters proceeding from conventional to more conservation tillage implements. The better quality of seedbed was provided by the rotary tillers.

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Section: Introductionmentioning

confidence: 99%

Conventional and Conservation Seedbed Preparation Systems for Wheat Planting in Silty-Clay Soil

Fanigliulo¹,

Pochi²,

Servadio³

2021

Sustainability

Self Cite

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“…We redivided the testing site and stratified sampling the plots based on the differences in farming operations (Figure 4d), and the evaluation results of prediction ability based on STM was generally better in both 1YC and 2YC, followed by MA and CM treatments. Recently, remotely-sensed soil tillage quality evaluation on the bare soil level has contributed to compare several tillage techniques to increase the quality of farming work and energy conservation [75]. However, there are still few applications of the assessment of tillage methods and yield performance during the growth period of forage crops.…”

Section: The Impact Of the Cultivated Period Flight Times And Farming Operationsmentioning

confidence: 99%

The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Estimation under Variety Performance Trials

Burnside

Lima

et al. 2021

Remote Sensing

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A significant trend has developed with the recent growing interest in the estimation of aboveground biomass of vegetation in legume-supported systems in perennial or semi-natural grasslands to meet the demands of sustainable and precise agriculture. Unmanned aerial systems (UAS) are a powerful tool when it comes to supporting farm-scale phenotyping trials. In this study, we explored the variation of the red clover-grass mixture dry matter (DM) yields between temporal periods (one- and two-year cultivated), farming operations [soil tillage methods (STM), cultivation methods (CM), manure application (MA)] using three machine learning (ML) techniques [random forest regression (RFR), support vector regression (SVR), and artificial neural network (ANN)] and six multispectral vegetation indices (VIs) to predict DM yields. The ML evaluation results showed the best performance for ANN in the 11-day before harvest category (R² = 0.90, NRMSE = 0.12), followed by RFR (R² = 0.90 NRMSE = 0.15), and SVR (R² = 0.86, NRMSE = 0.16), which was furthermore supported by the leave-one-out cross-validation pre-analysis. In terms of VI performance, green normalized difference vegetation index (GNDVI), green difference vegetation index (GDVI), as well as modified simple ratio (MSR) performed better as predictors in ANN and RFR. However, the prediction ability of models was being influenced by farming operations. The stratified sampling, based on STM, had a better model performance than CM and MA. It is proposed that drone data collection was suggested to be optimum in this study, closer to the harvest date, but not later than the ageing stage.

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“…In recent years, unmanned aerial vehicle (UAV) technology has also been applied in SSR measurement. Compared with ground measurement technology, UAV has higher efficiency and larger measuring area, but also higher cost [21,22].…”

Section: Introductionmentioning

confidence: 99%

Evaluation Method of Soil Surface Roughness after Ditching Operation Based on Wavelet Transform

Liu

Zhang

et al. 2022

Actuators

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Soil surface roughness (SSR) is an important parameter affecting surface hydrology, erosion, gas exchange and other processes. The surface roughness of the farmland environment is directly related to the tillage process. In order to accurately characterize the random roughness (RR) parameters of the surface after ditching, a three-dimensional (3D) digital model of the surface was obtained by laser scanning under the conditions of an indoor ditching test, and the influence of oriented roughness components formed by removing ridge characteristics on the RR of the surface was analyzed by introducing the wavelet processing method. For this reason, four groups of ditching depths and two types of surface conditions (whether the surface was agglomerated or not) were designed in this paper. By comparing the root mean squared height (RMSH) and correlation length (CL) data calculated before and after wavelet processing under each group of tests, it was concluded that the RMSH values of the four groups before and after wavelet processing all change more than 200%, the change amplitude reached 271.02% under the treatment of 12 cm ditching depth, meanwhile, the average CL value of five cross-sections under each group of ditching depths decreased by 1.43–2.28 times, which proves that the oriented roughness component formed by furrows and ridges has a significant influence on the calculation of RR. By further analyzing the roughness value differences of clods and pits in different directions and local areas before and after wavelet transform, it was shown that the wavelet transform can effectively remove the surface anisotropy characteristics formed in the tillage direction and provide a uniform treatment method for the evaluation of surface RR at different ditching depths.

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Light Drone-Based Application to Assess Soil Tillage Quality Parameters

Cited by 21 publications

References 19 publications

Conventional and Conservation Seedbed Preparation Systems for Wheat Planting in Silty-Clay Soil

Conventional and Conservation Seedbed Preparation Systems for Wheat Planting in Silty-Clay Soil

The Application of an Unmanned Aerial System and Machine Learning Techniques for Red Clover-Grass Mixture Yield Estimation under Variety Performance Trials

Evaluation Method of Soil Surface Roughness after Ditching Operation Based on Wavelet Transform

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