Technologies, methods, and approaches on detection system of plant pests and diseases

This research aims to identify the technology, methods, approaches applied in developing plant pest and disease detection systems. For this purpose, it mainly reviews systematically related research on identification, monitoring, detection, and control techniques of plant pests and diseases using a computer or mobile technology. Evidence from the literature shows previous both academia and practitioners have used various technologies, methods and approaches for developing detection system of plant pests and diseases. Some technologies have been applied for the detection system, such as web-based, mobile-based, and internet of things (IoT). Furthermore, the dominant approaches are expert system and deep learning. While backward chaining, forward chaining, fuzzy model, genetic algorithm (GA), K-means clustering, Bayesian networks and incremental learning, Naïve Bayes and Certainty Factors, Convolutional Neural Network, and Decision Tree are the most frequently methods applied in the previous researches. The review also indicated that no single technology or technique is best for developing accurate pest/disease detection system. Instead, the combination of technologies, methods, and approaches resulted in different performance and accuracies. A possible explanation for this is because the systems are used for detecting, controlling and monitoring various plants, such as corn, onion, wheat, rice, mango, flower, and others that are different. This research contributes by providing a reference for technologies, methods, and approaches to the detection system for plant pests and diseases. Also, it adds a way of literature review. This research has implications for researchers as a reference for researching in the computer system, especially for the detection of plant pest and disease research. Hence, this research also extends the body of knowledge of the intelligence system, deep learning, and computer science. For practice, the method references can be used for developing technology for detecting plant pest and disease

Internet of Things and Machine Learning Applications for Smart Precision Agriculture

Agriculture forms the major part of our Indian economy. In the current world, agriculture and irrigation are the essential and foremost sectors. It is a mandatory need to apply information and communication technology in our agricultural industries to aid agriculturalists and farmers to improve vice all stages of crop cultivation and post-harvest. It helps to enhance the country’s G.D.P. Agriculture needs to be assisted by modern automation to produce the maximum yield. The recent development in technology has a significant impact on agriculture. The evolutions of Machine Learning (ML) and the Internet of Things (IoT) have supported researchers to implement this automation in agriculture to support farmers. ML allows farmers to improve yield make use of effective land utilisation, the fruitfulness of the soil, level of water, mineral insufficiencies control pest, trim development and horticulture. Application of remote sensors like temperature, humidity, soil moisture, water level sensors and pH value will provide an idea to on active farming, which will show accuracy as well as practical agriculture to deal with challenges in the field. This advancement could empower agricultural management systems to handle farm data in an orchestrated manner and increase the agribusiness by formulating effective strategies. This paper highlights contribute to an overview of the modern technologies deployed to agriculture and suggests an outline of the current and potential applications, and discusses the challenges and possible solutions and implementations. Besides, it elucidates the problems, specific potential solutions, and future directions for the agriculture sector using Machine Learning and the Internet of things

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

IoT Applications Computing

The evolution of emerging and innovative technologies based on Industry 4.0 concepts are transforming society and industry into a fully digitized and networked globe. Sensing, communications, and computing embedded with ambient intelligence are at the heart of the Internet of Things (IoT), the Industrial Internet of Things (IIoT), and Industry 4.0 technologies with expanding applications in manufacturing, transportation, health, building automation, agriculture, and the environment. It is expected that the emerging technology clusters of ambient intelligence computing will not only transform modern industry but also advance societal health and wellness, as well as and make the environment more sustainable. This book uses an interdisciplinary approach to explain the complex issue of scientific and technological innovations largely based on intelligent computing