Boosting precision crop protection towards agriculture 5.0 via machine learning and emerging technologies: A contextual review

Crop protection is a key activity for the sustainability and feasibility of agriculture in a current context of climate change, which is causing the destabilization of agricultural practices and an increase in the incidence of current or invasive pests, and a growing world population that requires guaranteeing the food supply chain and ensuring food security. In view of these events, this article provides a contextual review in six sections on the role of artificial intelligence (AI), machine learning (ML) and other emerging technologies to solve current and future challenges of crop protection. Over time, crop protection has progressed from a primitive agriculture 1.0 (Ag1.0) through various technological developments to reach a level of maturity closelyin line with Ag5.0 (section 1), which is characterized by successfully leveraging ML capacity and modern agricultural devices and machines that perceive, analyze and actuate following the main stages of precision crop protection (section 2). Section 3 presents a taxonomy of ML algorithms that support the development and implementation of precision crop protection, while section 4 analyses the scientific impact of ML on the basis of an extensive bibliometric study of >120 algorithms, outlining the most widely used ML and deep learning (DL) techniques currently applied in relevant case studies on the detection and control of crop diseases, weeds and plagues. Section 5 describes 39 emerging technologies in the fields of smart sensors and other advanced hardware devices, telecommunications, proximal and remote sensing, and AI-based robotics that will foreseeably lead the next generation of perception-based, decision-making and actuation systems for digitized, smart and real-time crop protection in a realistic Ag5.0. Finally, section 6 highlights the main conclusions and final remarks

The role of phytophthora in predisposing Corymbia calophylla (marri) to a canker disease

Corymbia calophylla (marri), a keystone tree species in the global biodiversity hot spot of south-western Australia, is suffering decline and mortality due to canker disease caused by the endemic fungus Quambalaria coyrecup. Phytophthora species, fine root oomycete pathogens, are frequently isolated from the rhizosphere of dying C. calophylla, raising the possibility that a Phytophthora infection predisposes C. calophylla to this endemic canker pathogen by compromising its defence mechanisms. Field surveys conducted across the C. calophylla range, found Phytophthora to be present in the rhizosphere of C. calophylla. Five Phytophthora species (P. cinnamomi, P. elongata, P. multivora, P. pseudocryptogea and P. versiformis) were recovered from healthy and cankered C. calophylla. Phytophthora incidence was significantly higher in anthropogenically disturbed areas. Pot infestation trials were conducted where the C. calophylla plants were inoculated with the recovered Phytophthora species. A significant reduction in root volume and even seedling death were observed, demonstrating that Phytophthora can adversely affect C. calophylla health. In a follow-up trial, C. calophylla plants were inoculated with both P. cinnamomi and Q. coyrecup and subjected to a drought stress treatment. Results indicated that neither P. cinnamomi nor the drought stress treatments exacerbated the pathogenic effect of Q. coyrecup on the plants. During these trials, weekly reflectance spectroscopic measurements with a portable high-resolution spectroradiometer, were also taken to investigate its potential to track biochemical changes in the C. calophylla leaves due to these treatments. Reflectance values displayed differences between treatments, as well as a seasonal trend in the leaves. Bandwidths in the visible and shortwave infrared regions of the electromagnetic spectrum were demonstrated to be important regions for characterising C. calophylla response to the Phytophthora, Q. coyrecup, waterlogging and drought stress treatments. More work is required to identify the optimum wavelengths for C. calophylla. Once the optimum bandwidths have been determined, reflectance spectroscopy measurements can be scaled up to canopy level, using unmanned vehicles or fixed-wing aircraft; thus, aiding in the management of this canker disease in C. calophylla

Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits

The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400–850 nm) and short-wave infrared regions (SWIR, 950–1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (Tc), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64–65% and kappa = 0.26–31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution.Data collection was partially supported by the European Union's Horizon 2020 research and innovation program through grant agreements POnTE (635646) and XF-ACTORS (727987). R. Calderón was supported by a post-doctoral research fellowship from the Alfonso Martin Escudero Foundation (Spain)

Análisis de los índices de vegetación NDVI, GNDVI y NDRE para la caracterización del cultivo de café (Coffea arabica)

Recently, vegetation indices have been used in order to determine the type of cover, with the interest of its temporal variation evaluation, or with the aim of determining the crops health status from estimation of some characteristics such as plant vigor, chlorophyll content, nutritional status or water status. Several studies have put forward a range of vegetation indices that use different bands in the visible and near-infrared spectrum to obtain characteristics of interest. In this study, statistical differences between the NDVI, GNDVI and NDRE vegetation indices estimated from aerial images taken at 30 m above the canopy of an experimental Coffea arabica crop were evaluated. Spearman correlation coefficient showed a higher correlation between the NDVI and GNDVI indices compared to that found between either of them and NDRE. In addition to this, it was observed that, according to the values of the coefficient of variation, and the subsequent analysis of the histograms, the NDRE index presented a greater sensitivity to variation in plant vigor, which would suggest a greater potential to characterize the state of development of coffee cultivation, compared to other studied indices.Los índices de vegetación se han usado en los últimos años con el fin de determinar el tipo de cobertura, para evaluar su variación temporal, o para determinar el estado de salud de cultivos a partir de estimaciones de características como vigor vegetal, contenido de clorofila, estado nutricional o estado hídrico. En diversos estudios se han propuesto una variedad de índices de vegetación que usan diferentes bandas en el espectro visible e infrarrojo cercano con el fin de obtener características de interés. En este estudio se evaluaron las diferencias estadísticas entre los índices de vegetación NDVI, GNDVI y NDRE, estimados a partir de imágenes aéreas tomadas a 30 m del dosel de un cultivo experimental de la especie vegetal Coffea arabica. El coeficiente de correlación de Spearman mostró que la correlación es mayor entre los índices NDVI y GNDVI en comparación a la correlación presentada entre cualquiera de ellos y el índice NDRE. Además de ello, se observó que de acuerdo con los valores del coeficiente de variación, y el análisis posterior de los histogramas, el índice NDRE presentó una mayor sensibilidad ante la variación de vigor vegetal, lo que sugeriría un mayor potencial a la hora de caracterizar el estado de desarrollo del cultivo de café, frente a los otros índices estudiados

Forest Pathology and Plant Health

Every year, a number of new forest pathosystems are discovered as the result of introduction of alien pathogens, host shifts and jumps, hybridization and recombination among pathogens, etc. Disease outbreaks may also be favored by climate change and forest management. The mechanisms driving the resurgence of native pathogens and the invasion of alien ones need to be better understood in order to draft sustainable control strategies. For this Special Issue, we welcome population biology studies providing insights on the epidemiology and invasiveness of emergent forest pathogens possibly by contrasting different scenarios varying in pathogen and host populations size, genetics, phenotype and phenology, landscape fragmentation, occurrence of disturbances, management practices, etc. Both experimental and monitoring approaches are welcome. In summary, this special issue focuses on how variability in hosts, pathogens, or ecology may affect the emergence of new threats to plant species

Detection of Xylella fastidiosa in almond orchards by synergic use of an epidemic spread model and remotely sensed plant traits

The early detection of Xylella fastidiosa (Xf) infections is critical to the management of this dangerous plan pathogen across the world. Recent studies with remote sensing (RS) sensors at different scales have shown that Xf-infected olive trees have distinct spectral features in the visible and infrared regions (VNIR). However, further work is needed to integrate remote sensing in the management of plant disease epidemics. Here, we research how the spectral changes picked up by different sets of RS plant traits (i.e., pigments, structural or leaf protein content), can help capture the spatial dynamics of Xf spread. We coupled a spatial spread model with the probability of Xf-infection predicted by a RS-driven support vector machine (RS-SVM) model. Furthermore, we analyzed which RS plant traits contribute most to the output of the prediction models. For that, in almond orchards affected by Xf (n = 1426 trees), we conducted a field campaign simultaneously with an airborne campaign to collect high-resolution thermal images and hyperspectral images in the visible-near-infrared (VNIR, 400–850 nm) and short-wave infrared regions (SWIR, 950–1700 nm). The best performing RS-SVM model (OA = 75%; kappa = 0.50) included as predictors leaf protein content, nitrogen indices (NIs), fluorescence and a thermal indicator (Tc), alongside pigments and structural parameters. Leaf protein content together with NIs contributed 28% to the explanatory power of the model, followed by chlorophyll (22%), structural parameters (LAI and LIDFa), and chlorophyll indicators of photosynthetic efficiency. Coupling the RS model with an epidemic spread model increased the accuracy (OA = 80%; kappa = 0.48). In the almond trees where the presence of Xf was assayed by qPCR (n = 318 trees), the combined RS-spread model yielded an OA of 71% and kappa = 0.33, which is higher than the RS-only model and visual inspections (both OA = 64–65% and kappa = 0.26–31). Our work demonstrates how combining spatial epidemiological models and remote sensing can lead to highly accurate predictions of plant disease spatial distribution

Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence

Grapevine yellows (GY) are a significant threat to grapes due to the severe symptoms and lack of treatments. Conventional diagnosis of the phytoplasmas associated to GYs relies on symptom identification, due to sensitivity limits of diagnostic tools (e.g. real time PCR) in asymptomatic vines, where the low concentration of the pathogen or its erratic distribution can lead to a high rate of false-negatives. GY's primary symptoms are leaf discoloration and irregular wood ripening, which can be easily confused for symptoms of other diseases making recognition a difficult task. Herein, we present a novel system, utilizing convolutional neural networks, for end-to-end detection of GY in red grape vine (cv. Sangiovese), using color images of leaf clippings. The diagnostic test detailed in this work does not require the user to be an expert at identifying GY. Data augmentation strategies make the system robust to alignment errors during data capture. When applied to the task of recognizing GY from digital images of leaf clippings—amongst many other diseases and a healthy control—the system has a sensitivity of 98.96% and a specificity of 99.40%. Deep learning has 35.97% and 9.88% better predictive value (PPV) when recognizing GY from sight, than a baseline system without deep learning and trained humans respectively. We evaluate six neural network architectures: AlexNet, GoogLeNet, Inception v3, ResNet-50, ResNet-101 and SqueezeNet. We find ResNet-50 to be the best compromise of accuracy and training cost. The trained neural networks, code to reproduce the experiments, and data of leaf clipping images are available on the internet. This work will advance the frontier of GY detection by improving detection speed, enabling a more effective response to the disease

Detection of grapevine yellows symptoms in Vitis vinifera L. with artificial intelligence

Abstract Grapevine yellows (GY) are a significant threat to grapes due to the severe symptoms and lack of treatments. Conventional diagnosis of the phytoplasmas associated to GYs relies on symptom identification, due to sensitivity limits of diagnostic tools (e.g. real time PCR) in asymptomatic vines, where the low concentration of the pathogen or its erratic distribution can lead to a high rate of false-negatives. GY's primary symptoms are leaf discoloration and irregular wood ripening, which can be easily confused for symptoms of other diseases making recognition a difficult task. Herein, we present a novel system, utilizing convolutional neural networks, for end-to-end detection of GY in red grape vine (cv. Sangiovese), using color images of leaf clippings. The diagnostic test detailed in this work does not require the user to be an expert at identifying GY. Data augmentation strategies make the system robust to alignment errors during data capture. When applied to the task of recognizing GY from digital images of leaf clippings—amongst many other diseases and a healthy control—the system has a sensitivity of 98.96% and a specificity of 99.40%. Deep learning has 35.97% and 9.88% better predictive value (PPV) when recognizing GY from sight, than a baseline system without deep learning and trained humans respectively. We evaluate six neural network architectures: AlexNet, GoogLeNet, Inception v3, ResNet-50, ResNet-101 and SqueezeNet. We find ResNet-50 to be the best compromise of accuracy and training cost. The trained neural networks, code to reproduce the experiments, and data of leaf clipping images are available on the internet. This work will advance the frontier of GY detection by improving detection speed, enabling a more effective response to the disease

Crop Disease Detection Using Remote Sensing Image Analysis

Pest and crop disease threats are often estimated by complex changes in crops and the applied agricultural practices that result mainly from the increasing food demand and climate change at global level. In an attempt to explore high-end and sustainable solutions for both pest and crop disease management, remote sensing technologies have been employed, taking advantages of possible changes deriving from relative alterations in the metabolic activity of infected crops which in turn are highly associated to crop spectral reflectance properties. Recent developments applied to high resolution data acquired with remote sensing tools, offer an additional tool which is the opportunity of mapping the infected field areas in the form of patchy land areas or those areas that are susceptible to diseases. This makes easier the discrimination between healthy and diseased crops, providing an additional tool to crop monitoring. The current book brings together recent research work comprising of innovative applications that involve novel remote sensing approaches and their applications oriented to crop disease detection. The book provides an in-depth view of the developments in remote sensing and explores its potential to assess health status in crops