Artificial Neural Networks in Agriculture

Modern agriculture needs to have high production efficiency combined with a high quality of obtained products. This applies to both crop and livestock production. To meet these requirements, advanced methods of data analysis are more and more frequently used, including those derived from artificial intelligence methods. Artificial neural networks (ANNs) are one of the most popular tools of this kind. They are widely used in solving various classification and prediction tasks, for some time also in the broadly defined field of agriculture. They can form part of precision farming and decision support systems. Artificial neural networks can replace the classical methods of modelling many issues, and are one of the main alternatives to classical mathematical models. The spectrum of applications of artificial neural networks is very wide. For a long time now, researchers from all over the world have been using these tools to support agricultural production, making it more efficient and providing the highest-quality products possible

Una nueva aproximación metodológica basada en redes conceptuales y redes probabilísticas para evaluar la provisión de servicios de los ecosistemas

10-17En los últimos años han surgido varias herramientas para evaluar la provisión de servicios de los ecosistemas (SE) desde un punto de vista ecológico. No obstante, la complejidad de los SE desalienta los intentos de adoptar una única aproximación metodológica. El objetivo de este trabajo consistió en evaluar un nuevo marco de análisis de la provisión de SE, sobre la base de redes conceptuales y redes probabilísticas. Para cumplir con el objetivo se describió el desarrollo de una red conceptual y se representó el conjunto de variables que determinan la provisión de ocho SE de la Región Pampeana. Luego, se parametrizó esa red mediante una metodología probabilística conocida como Redes Bayesianas, para ser, después, aplicada a tres zonas agrícolas pampeanas. Por último, se plantearon ventajas y desventajas de este nuevo marco de análisis mediante una comparación con otras aproximaciones para el estudio de la provisión de SE, desarrolladas en Argentina y en otras partes del mundo, tales como InVEST, RIOS, ARIES y ECOSER. El enfoque aquí presentado puede ser útil para: a) evaluar la sustentabilidad de los agroecosistemas pampeanos desde una dimensión ecológica, y/o b) asistir a los tomadores de decisión (i.e., productores y asesores agropecuarios) para implementar estrategias sustentables de uso de la tierra

Sustainable Agriculture and Advances of Remote Sensing (Volume 1)

Agriculture, as the main source of alimentation and the most important economic activity globally, is being affected by the impacts of climate change. To maintain and increase our global food system production, to reduce biodiversity loss and preserve our natural ecosystem, new practices and technologies are required. This book focuses on the latest advances in remote sensing technology and agricultural engineering leading to the sustainable agriculture practices. Earth observation data, in situ and proxy-remote sensing data are the main source of information for monitoring and analyzing agriculture activities. Particular attention is given to earth observation satellites and the Internet of Things for data collection, to multispectral and hyperspectral data analysis using machine learning and deep learning, to WebGIS and the Internet of Things for sharing and publishing the results, among others

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

The promise of biochar: From lab experiment to national scale impacts

Biochar is a carbon rich soil amendment produced from biomass by a thermochemical process, pyrolysis or gasication. Soil biochar applications have generated a great deal of interest as a strategy for mitigating climate change by sequestering carbon in soils, and simultaneously as a strategy for enhancing global food security by increasing crop yields especially on degraded and poor quality soils. In this study we evaluated the eect of biochars presence on soil and crop in various spatial scales ranging from lab experiments to regional scale simulations. In the rst chapter, we used an incubated experiment with 3 biochar application rates (0%, 3% and 6%), two application methods and three replications. Soil water retention curves (SWRC) were determined at three sampling times. The Van-Genuchten (VG) model was tted to all SWRCs and then used to estimate the pore size distribution (PSD). Standard deviation (SD), skewness and mode (D) were calculated in order to interpret the geometry of PSDs. The Dexter S-index and saturated hydraulic conductivity (Ks) were also estimated. Statistical analysis was performed for all parameters using a linear mixed model. Relative to controls, all biochar treatments increased porosity, water content at both saturation and eld capacity and improved soil physical quality. Biochar applications lowered Ks, bulk density and D indicative of a shift in pore size distributions toward smaller pore sizes. The second chapter was focused on evaluating the impacts of biochar on soil hydraulic properties at the eld scale by combining a modeling approach with soil water content measurements. Soil water measurements were collected from a corn-corn cropping system over two years. The eect of biochar was expected to be the difference between the physical soil properties of the biochar and no-biochar treatments. An inverse modeling was performed after a global sensitivity analysis to estimate the parameters for the soil physical properties of the APSIM (The Agricultural ProductionSystems sIMulator ) model . Results of the sensitivity analysis showed that the drainage upper limit (DUL) was the most sensitive soil property followed by saturated hydraulic conductivity (KS), saturated water content (SAT), maximum rate of plant water uptake (KL), maximum depth of surface storage (MAXPOND), lower limit volumetric water content (LL15) and lower limit for plant water uptake (LL). The dierence between the posterior distributions (with and without biochar) showed an increase in DUL of approximately 10%. No considerable change was noted in LL15, MAXPOND and KS whereas SAT and LL showed a slight increase and decrease in biochar treatment, respectively, compared to no-biochar. In the third chapter, we tried to ans r the question: Where should we apply biochar? For this task, we developed an extensive informatics workflow for processing and analyzing crop yield response data as well as a large spatial-scale modeling platform. we used a probabilistic graphical model to study the relationships between soil and biochar variables and predict the probability of crop yield response to biochar application. Our Bayesian network model was trained using the data collected from 103 published studies reporting yield response to biochar. Our results showed an average 12% increase in crop yield from all the studies with a large variability ranging from -24.4% to 98%. Soil clay content, pH, cation exchange capacity and organic carbon appeared to be strong predictors of crop yield response to biochar. we also found that biochar carbon, nitrogen content and highest pyrolysis temperature signicantly inuenced the yield response to biochar. Our large spatial-scale modeling revealed that 8.4% to 30% of all U.S. cropland can be targeted and is expected to show a positive yield response to biochar application. It was found that biochar application to areas with high probability of crop yield response in the U.S could ofset a maximum of 2% of the current global anthropogenic carbon emissions per year. In the last chapter, we made regional scale simulations of biochar effects on crop yield and nitrate leaching using APSIM for parts of Iowa and California. Three main pieces of work were integrated in this study. The suitable areas found for biochar application in the previous chapter in both states, the biochar module in the APSIM model and a new developed algorithm for speeding up the large spatial scale simulations. This allowed us to simulate 30 years of biochar effects on soil and crop for corn-corn cropping system in Iowa and alfalfa in California starting in1980 until 2016. Model outputs were then aggregated at a climate division level and the eect of biochar was estimated as the percent change relative to no biochar. In this study, the APSIM model suggested an insignicant change in crop yield/biomass following biochar application with a more substantial eect on nitrate leaching depending on weather conditions. It was found that in wet years (PDSI\u3e3) there is a reduction in nitrate leaching along with an increase in crop yield, suggesting more mineral nitrogen being available for the crop. As one of the significant findings of this study, it was found that the biochar effect lasted almost for the entire 30 years of simulation period while biochar application allowed for sustainable harvest of the crop residue without losing yield or increasing nitrate leaching. During the simulation period, biochar acted as a source of carbon which consistently helped with increasing the mineral nitrogen pool through carbon mineralization and relieving nitrogen stress

Plant Genetics and Biotechnology in Biodiversity

Plant genetic resources for food and agriculture (PGRFA) have been collected and exchanged for centuries. The rapid development of novel tools for genetic and phenotypic analysis is changing the way we can uncover diversity and exploit its value in modern agriculture. The integration of novel analytical tools is crucial for translating research into much-needed, more efficient management and use of PGRFA. This Special Issue provides an overview of recent topics on plant genetics and biotechnology in biodiversity. The proposed reviews and research papers present current trends and examples of genetic resources’ description, conservation, management, and exploitation, highlighting that new approaches and methodogies can increase our understanding and efficient use of PGRFA to address the agricultural challenges that lie ahead

Plant Parasitic Nematodes

Plant-parasitic nematodes (PPNs) are economically important pests for numerous agriculture and forestry crops, representing a significant constraint on global food security and forestry health. Root knot nematodes (Meloidogyne spp.), potato cyst nematodes (Globodera spp.), and root lesion nematodes (Pratylenchus spp.) are some examples of PPNs that are ranked at the top in the list of the most economically and scientifically important species. Current approaches to controlling these PPNs include the use of nematicides, but many pose serious concerns for human health and the environment. To cope with such threats, accurate diagnostic methods for nematode detection and a deep understanding of nematode infection processes, as well as of their intricate relationships with the host plants, are crucial for the development of effective integrated nematode management programs. This Special Issue entitled “Pant Parasitic Nematodes” is a collection of 11 original papers that cover a wide range of topics, including the state of the art of important PPN, and the detection and management of PPNs through sustainable and eco-friendly strategies

Developing land management units using Geospatial technologies: An agricultural application

This research develops a methodology for determining farm scale land managementunits (LMUs) using soil sampling data, high resolution digital multi-spectral imagery (DMSI) and a digital elevation model (DEM). The LMUs are zones within a paddock suitable for precision agriculture which are managed according to their productive capabilities. Soil sampling and analysis are crucial in depicting landscape characteristics, but costly. Data based on DMSI and DEM is available cheaply and at high resolution.The design and implementation of a two-stage methodology using a spatiallyweighted multivariate classification, for delineating LMUs is described. Utilising data on physical and chemical soil properties collected at 250 sampling locations within a 1780ha farm in Western Australia, the methodology initially classifies sampling points into LMUs based on a spatially weighted similarity matrix. The second stage delineates higher resolution LMU boundaries using DMSI and topographic variables derived from a DEM on a 10m grid across the study area. The method groups sample points and pixels with respect to their characteristics and their spatial relationships, thus forming contiguous, homogenous LMUs that can be adopted in precision agricultural applications. The methodology combines readily available and relatively cheap high resolution data sets with soil properties sampled at low resolution. This minimises cost while still forming LMUs at high resolution.The allocation of pixels to LMUs based on their DMSI and topographic variables has been verified. Yield differences between the LMUs have also been analysed. The results indicate the potential of the approach for precision agriculture and the importance of continued research in this area