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Conventional methodology in the field for the sampling of coffee leaf rust, caused by Hemileia vastatrix, has proven to be impractical. This paper proposes a method for the early detection of this disease, which is the most significant pathogen of coffee plants worldwide, using multispectral images acquired using a Mapir Survey3W camera and an unmanned aerial vehicle (UAV). For this purpose, 160 coffee seedlings of the coffee cultivar 'Mundo Novo' were inoculated with urediniospores of H. vastatrix and compared with 160 control (non-inoculated) seedlings to determine the most favorable interval for distinguishing healthy and infected plants. The 320 seedlings were placed on a dark surface to perform the imaging flights. In vitro analyses of the physiological parameters of 20 specimens were then performed for each condition (inoculated/non-inoculated) to obtain the hyperspectral curves, and this process was repeated three times at 15, 30, and 45 days after inoculation (DAI). Based on the simulated hyperspectral curves, a discrepancy between the red and near-infrared (NIR) bands was identified at 15 DAI, with the inoculated plants showing greater absorption in the red band and a greater spectral response in the NIR band. Thus, multispectral images were able to distinguish H. vastatrix infection in coffee seedlings at an asymptomatic stage (15 DAI) using a support vector machines (SVM) algorithm. Detection accuracy was 80% and the Kappa index of agreement was moderate (0.6). The early detection of this pathogen in the field using low-cost technology can be an important tool for the monitoring of coffee leaf rust and, consequently, a more sustainable management of the pathogen, causing farmers to make applications of chemical fungicides only when necessary. [ABSTRACT FROM AUTHOR]

Biofortified mini lettuce resistant to root-knot nematode with high agronomic potential is not yet a reality in Brazil. Thus, the objective of this work was to evaluate the agronomic potential of lines of biofortified mini lettuces combined with resistance to M. javanica. Therefore, two experiments were performed: 1 - agronomic performance in the field; 2-resistance to M. javanica in the greenhouse. The experiments were arranged in a randomized block design (experiment 1) and in a completely randomized design (experiment 2), with ten treatments (six advanced lines and four commercial cultivars) and eight replications, conducted at the Federal University of Uberlândia (UFU). The following parameters were evaluated in experiment 1: total chlorophyll content; plant and stem diameter; and number of leaves plant-1. In experiment 2: gall index; number of galls and eggs root-1; reproduction factor. The evaluated genotypes which presented agronomic potential were generally not resistant to M. javanica, except UFU-66#6, an advanced lineage which is innovative in the segment of biofortified mini lettuce, combining agronomic potential and resistance to M. javanica. [ABSTRACT FROM AUTHOR]

The coffee crops are exposed to different pathogens that directly affect yield. These include nematodes, which attack the roots of plants and compromise their physiological development. Given the losses caused by this pathogen and the lack of information on spatial distribution in infested areas, it is important to adopt technologies that enable crops under different management systems to be monitored during their growth cycle. The remote sensing associated with machine learning algorithms is presented as a potential tool for monitoring agricultural crops. The present study assesses different machine learning algorithms, using radiometric values of multispectral images as input datasets, and identifies the best algorithms, to estimate the physiological agronomic parameters in coffee crops submitted to 11 treatments for nematode management. Based on the association between the images taken by a low-cost camera (bands: (R) red, (G) green and (B) blue) mounted on a remotely piloted aircraft (RPA), machine learning algorithms (Random Forest (RF) and support-vector machines (SVM)), the results made it possible to estimate with satisfactory accuracy (root mean square error (RMSE) less than 26.5% the main physical parameters of coffee plants: chlorophyll, plant height, branch length, number of branches and number of nodes per branch. With Planet satellite-derived multispectral bands, the SVM algorithm estimated plant canopy diameters with an RMSE of 7.74%. Based on the spatial distribution maps of the physical parameters, the application machine learning methods offered an opportunity to better use remote sensing data for monitoring coffee crop growth conditions and accurately guiding several management techniques. [ABSTRACT FROM AUTHOR]

The application of biological products in agricultural crops has become increasingly prominent. The growth-promoting bacterium Azospirillum brasilense has been used as an alternative to promote greater yield in maize crops. In the context of precision agriculture, interpreting geospatial data has allowed for monitoring the effect of the application of products that increase the yield of corn crops. The objective of this work was to evaluate the potential of Kriging techniques and spectral models through images in estimating the gain in yield of maize crop after applying A. brasilense. Analyses were carried out in two commercial areas treated with A. brasilense. The results revealed that models of yield prediction by Kriging with a high volume of training data estimated the yield gain with a root-mean-square error deviation (RMSE%), mean absolute percentage error (MAPE%), and R2 to be 6.67, 5.42, and 0.88, respectively. For spectral models with a low volume of training data, yield gain was estimated with RMSE%, MAPE%, and R2 to be 9.3, 7.71, and 0.80, respectively. The results demonstrate the potential to map the spatial distribution of productivity gains in corn crops following the application of A. brasilense. [ABSTRACT FROM AUTHOR]

The coffee plant is one of the main crops grown in Brazil. However, strategies to estimate its yield are questionable given the characteristics of this crop; in this context, robust techniques, such as those based on machine learning, may be an alternative. Thus, the aim of the present study was to estimate the yield of a coffee crop using multispectral images and machine learning algorithms. Yield data from a same study area in 2017, 2018 and 2019, Sentinel 2 images, Random Forest (RF) algorithms, Support Vector Machine (SVM), Neural Network (NN) and Linear Regression (LR) were used. Statistical analysis was performed to assess the absolute Pearson correlation and coefficient of determination values. The Sentinel 2 satellite images proved to be favorable in estimating coffee yield. Despite the low spatial resolution in estimating agricultural variables below the canopy, the presence of specific bands such as the red edge, mid infrared and the derived vegetation indices, act as a countermeasure. The results show that the blue band and green normalized difference vegetation index (GNDVI) exhibit greater correlation with yield. The NN algorithm performed best and was capable of estimating yield with 23% RMSE, 20% MAPE and R² 0.82 using 85% of the training and 15% of the validation data of the algorithm. The NN algorithm was also more accurate (27% RMSE) in predicting yield. [ABSTRACT FROM AUTHOR]

Brazil is the largest coffee-producing nation in the world. Over 50% of the national production comes from the Minas Gerais state, with relevant contribution of the region covered by the Cerrado biome. Given the threat posed by root-knot nematodes (RKN, Meloidogyne spp.) to coffee production, we collected plant and soil samples from infested plantations across 16 counties and identified the species of this pathogen. Based on the female perineal patterns, male morphology, esterase phenotypes and SCAR markers, the species found were Meloidogyne exigua, Meloidogyne paranaensis, Meloidogyne incognita and Meloidogyne arenaria. The most prevalent species was M. exigua, occurring in 83.8% of the sampled sites, followed by M. paranaensis (19%) and M. incognita (4.9%). Mixed populations of RKN were observed in 7.7% of the sites, with the highest prevalence of M. exigua + M. paranaensis and M. paranaensis + M. incognita, followed by M. exigua + M. incognita. Meloidogyne arenaria was found in one site, in a mixed population with M. paranaensis + M. exigua. Strategies to prevent the spread of these nematodes to non-infested areas are highly recommended, particularly focusing on the most aggressive species such as M. paranaensis and M. incognita. [ABSTRACT FROM AUTHOR]

Anaerobic soil disinfestation (ASD) is an ecological alternative to chemical soil fumigation. The method consists in incorporating easily decomposable organic materials into the soil, followed by irrigation to saturation and soil cover with impermeable plastic. Cereal brans, molasses, ethanol, and vegetable wastes are the main sources of carbon used in ASD. The soil remains covered from 3 to 10 weeks. Accumulation of toxic anaerobic decomposition products, antagonism by anaerobic organisms, lack of oxygen and the combination of all these factors are the main mechanisms of action of the technique against plant pathogens. Here, we reviewed the potential of ASD in the management of plant-pathogenic fungi, bacteria and nematodes. • Anaerobic soil disinfestation is useful for the control of soilborne pathogens. • The mode of action of this method involves the interaction between several factors. • The incubation period of anaerobic soil disinfestation ranges from 3 to 6 weeks. • The aspects that influence the efficiency of this technique are discussed. • Anaerobic soil disinfestation shifts the composition of the soil microbial community. [ABSTRACT FROM AUTHOR]