Assessing Phytosanitary Application Efficiency of a Boom Sprayer Machine Using RGB Sensor in Grassy Fields

Abrougui, Khaoula; Boughattas, Nour El Houda; Belhaj, Meriem; Buchaillot, Ma. Luisa; Segarra, Joel; Dorbolo, Stéphane; Amami, Roua; Chehaibi, Sayed; Tarchoun, Neji; Kefauver, Shawn C.

doi:10.3390/su14063666

Cited by 2 publications

(1 citation statement)

References 36 publications

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“…Because of that, the primal view of the challenge and the adoption of practices to escape greater losses are of great significance (Guan et al 2014). The below-mentioned control tools represent lead-ins for the use of precision farming, either as localized practices or through variable rate technologies (Abrougui et al 2022a). The collection of root samplings and soil in high density for transferring to the laboratory can bring prohibitive, and indispensable styles are required to characterize the distribution of nematodes with the applicable resolution level (Abd-Elgawad.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence applications in precision agriculture to predict the effect of Root-Knot nematodes and grafting on vegetable crop health from proximal remote sensing machines

Hamdane¹,

Abrougi²,

Sorribas³

et al. 2022

J. OASIS AGRIC. SUSTAIN. DEV

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Climate change, especially in arid and semi-arid areas, affects the production of fruit trees. In this region, fruit tree production requires an efficient water supply that maintaining safe and stable yields. The aim of this work is to study the influence of irrigation modes on the nutritional peach fruit quality and the control of water stress indicators rates. Our experiment was carried out at the CRRA Sidi Bouzid (Central-West of Tunisia). It focused on four varieties of peach (Prunus persica L), two early varieties (Flordastar (FS) and Early Maycrest (EMC)), a seasonal variety (Rubirich (RUB)) and a late variety (O'Henry (O'H)). Three different irrigation treatments were applied to the experimental plot: full irrigation (T1; 100% ETc), sustained deficit irrigation (T2; 50% ETc) and cyclical deficit irrigation (T3). The contents of total sugar, protein, and proline as well as some bioactive compounds and stress indicators (MDA, H2O2) were quantified in the exocarp and mesocarp of the fruit. The results showed that O’H fruits are the richest in phenolic compounds, as well as they have significant antioxidant activity. While, both FS tissues accumulated more sugar (55.15 and 81.31g/100g in the mesocarp and exocarp, respectively). Protein level was much higher under T2 and T3 treatments compared to the control treatment (T1) in all varieties. Water stress mainly T2 had significantly stimulated the accumulation of proline in the mesocarp of FS (the content increased from 0.61 to 2.1 µmol/100 g MS). In addition, in the four varieties, the cyclic water treatment (T3) has a significant effect on the accumulation of sugar and phenolic compounds. In conclusion, T3 seems to be the most adequate water regime to be applied in semi-arid region, saving water resources and maintaining fruit quality.

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Section: Introductionmentioning

confidence: 99%

Artificial intelligence applications in precision agriculture to predict the effect of Root-Knot nematodes and grafting on vegetable crop health from proximal remote sensing machines

Hamdane¹,

Abrougi²,

Sorribas³

et al. 2022

J. OASIS AGRIC. SUSTAIN. DEV

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A Micro-Scale Approach for Cropland Suitability Assessment of Permanent Crops Using Machine Learning and a Low-Cost UAV

et al. 2023

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This study presents a micro-scale approach for the cropland suitability assessment of permanent crops based on a low-cost unmanned aerial vehicle (UAV) equipped with a commercially available RGB sensor. The study area was divided into two subsets, with subsets A and B containing tangerine plantations planted during years 2000 and 2008, respectively. The fieldwork was performed on 27 September 2021 by using a Mavic 2 Pro UAV equipped with a commercial RGB sensor. The cropland suitability was performed in a two-step classification process, utilizing: (1) supervised classification with machine learning algorithms for creating a vegetation mask; and (2) unsupervised classification for the suitability assessment according to the Food and Agriculture Organization of the United Nations (FAO) land suitability standard. The overall accuracy and kappa coefficients were used for the accuracy assessment. The most accurate combination of the input data and parameters was the classification using ANN with all nine input rasters, managing to utilize complimentary information regarding the study area spectral and topographic properties. The resulting suitability levels indicated positive suitability in both study subsets, with 63.1% suitable area in subset A and 59.0% in subset B. Despite that, the efficiency of agricultural production can be improved by managing crop and soil properties in the currently non-suitable class (N1), providing recommendations for farmers for further agronomic inspection. Alongside low-cost UAV, the open-source GIS software and globally accepted FAO standard are expected to further improve the availability of its application for permanent crop plantation management.

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Assessing Phytosanitary Application Efficiency of a Boom Sprayer Machine Using RGB Sensor in Grassy Fields

Cited by 2 publications

References 36 publications

Artificial intelligence applications in precision agriculture to predict the effect of Root-Knot nematodes and grafting on vegetable crop health from proximal remote sensing machines

Artificial intelligence applications in precision agriculture to predict the effect of Root-Knot nematodes and grafting on vegetable crop health from proximal remote sensing machines

A Micro-Scale Approach for Cropland Suitability Assessment of Permanent Crops Using Machine Learning and a Low-Cost UAV

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