Expert System for Crop Disease based on Graph Pattern Matching: A proposal

Para la agroindustria, las enfermedades en cultivos constituyen uno de los problemas más frecuentes que generan grandes pérdidas económicas y baja calidad en la producción. Por otro lado, desde las ciencias de la computación, han surgido diferentes herramientas cuya finalidad es mejorar la prevención y el tratamiento de estas enfermedades. En este sentido, investigaciones recientes proponen el desarrollo de sistemas expertos para resolver este problema haciendo uso de técnicas de minería de datos e inteligencia artificial, como inferencia basada en reglas, árboles de decisión, redes bayesianas, entre otras. Además, los grafos pueden ser usados para el almacenamiento de los diferentes tipos de variables que se encuentran presentes en un ambiente de cultivos, permitiendo la aplicación de técnicas de minería de datos en grafos, como el emparejamiento de patrones en los mismos. En este artículo presentamos una visión general de las temáticas mencionadas y una propuesta de un sistema experto para enfermedades en cultivos, basado en emparejamiento de patrones en grafos.For agroindustry, crop diseases constitute one of the most common problems that generate large economic losses and low production quality. On the other hand, from computer science, several tools have emerged in order to improve the prevention and treatment of these diseases. In this sense, recent research proposes the development of expert systems to solve this problem, making use of data mining and artificial intelligence techniques like rule-based inference, decision trees, Bayesian network, among others. Furthermore, graphs can be used for storage of different types of variables that are present in an environment of crops, allowing the application of graph data mining techniques like graph pattern matching. Therefore, in this paper we present an overview of the above issues and a proposal of an expert system for crop disease based on graph pattern matching

Forecasting Adverse Weather Situations in the Road Network

Weather is an important factor that affects traffic flow and road safety. Adverse weather situations affect the driving conditions directly; hence, drivers must be informed about the weather conditions downstream to adapt their driving. In the framework of intelligent transport systems, several systems have been developed to know the weather situations and inform drivers. However, these systems do not forecast weather in advance, and they need the support of road operators to inform drivers. This paper presents a new autonomous system to forecast weather conditions in a short time and to give users the information obtained. The system uses a set of algorithms and rules to determine the weather and to forecast dangerous situations on the road network. It has been implemented using a multiagent approach and tested with real data. Results are very promising. The system is able to forecast adverse situations with a high degree of quality. This quality makes it possible to trust in the system and to avoid the supervision of operators

The NEBLINE, June 2006

Contents: Bio-Fuels Can Help Bridge Energy Gap: Nebraska in Ideal Position to be Supplier of Biofuels When to Harvest Bromegrass Hay Sample Your Hay to Get Accurate Nutrient Analyses Moving Round Hay Bales Can be Dangerous Pumping Water for Ponds Controlling Pests with Home Remedies What Does Work: Boric Acid and Borates More Home Remedies Debunked Protect Stored Winter Clothing from Insect Damage Getting Past the Nutrition Headlines Bone Appetit Banana Smoothie Recipe Walk Nebraska! President’s Notes — Alice’s Analysis Household Hints: Storing Summer Swimsuits FCE News & Events Summer Energy Saving Tips Are You Ready for Sun’s Rays? How White are Your White Clothes? Bagworms, Look for Them Now! All American Roses for 2006 Care of Coleus Smart Watering Techniques Conserve Water in the Yard Some Other Factors To Consider Summer Blooming Perennials Ron Dowding New Horse Rules Book is Available Salt Creek Wranglers Hold Pre-Districts Practice, May 20 and June 11 County Fair Horse IDs Due June 1 Ft. Robinson Horse Camp, June 8–10 State Hippology and Judging Forms Due June 1 2006 4-H Horse Judging Clinics Clover College Bennet Celebrates Successful Visioning Process Community Garden Open House, June 24 When Property with Private Water, Wastewater is Sold, Systems Must be Inspected The Nebraska LEAD Program ABC’s for Good Health, June 1, 8 & 15 4-H Speech & PSA Contest Winners Rabbits ‘R’ Us 4-H Club Donates Aprons U.S. Drought Monitor Map Choose from More than 40 Nebraska 4-H Summer Camp

The NEBLINE, July 2006

Contents: Regularly Test Private Well Drinking Water for Safety Approved Water Testing Laboratories Nebraska Forest Service Expert Discovers Pine Wilt Treatment Easier Gardening Odorous House Ant Chiggers Not a Health Concern, But Can Make Outdoor Activities Uncomfortable Ants in the Lawn Bats Under Porches, Patios Prepare to Plant Alfalfa in August Pesticide Container Recycling Program Apply Manure Before Seeding Alfalfa UNL Researcher Needs Alfalfa Fields With Pocket Gophers Planting Vegetables for Fall Methods of Drying Flowers Measuring Distance Caring for Animals When Gone Spider Mites Common Problem on Trees, Other Plants $tretch Your Food Dollar with Fruits and Vegetables Red, White & Blue Cereal Recipe Making Ice Cream with Cooked Eggs Washing Fruits and Vegetables Frozen Custard Ice Cream Recipe Canning & Freezing Web Resources President’s Notes — Alice’s Analysis Household Hints: Washing Pillows Tease-Proof Your Child FCE News & Events When to Turn Off Personal Computers Jean Pedersen District Speech & PSA Contest Results Last Chance for Riding Skills Group Testing, July 8 Dress Code Enforced at District and State State Horse Expo Information Interns Assist with 4-H Activities 2006 LANCASTER COUNTY FAIR P3 Intern Working at Extension Start Your Own Community Tool-Sharing Program E-Waste: What It Is, and What You Can Do Extension Calendar UNL Research Traces Traits of Strong Families Community CROPS Will Hold a Garden Open House, July 29 Insect of the Month: Fireflies U.S. Drought Monitor Map Free Test Kits Available for Toxic Blue-Green Algae Still Time to Sign Up for 4-H Summer Camps Lancaster County Fair Schedule & Map Special Pullout Sectio

Crop suitability mapping for underutilized crops in South Africa.

Doctoral Degree. University of KwaZulu-Natal, Pietermaritzburg.Several neglected and underutilised species (NUS) provide solutions to climate change and create a Zero Hunger world, the Sustainable Development Goal 2. However, limited information describing their agronomy, water use, and evaluation of potential growing zones to improve sustainable production has previously been cited as the bottlenecks to their promotion in South Africa's (SA) marginal areas. Therefore, the thesis outlines a series of assessments aimed at fitting NUS in the dryland farming systems of SA. The study successfully mapped current and possible future suitable zones for NUS in South Africa. Initially, the study conducted a scoping review of land suitability methods. After that, South African bioclimatic zones with high rainfall variability and water scarcity were mapped. Using the analytic hierarchy process (AHP), the suitability for selected NUS sorghum (Sorghum bicolor), cowpea (Vigna unguiculata), amaranth and taro (Colocasia esculenta) was mapped. The future growing zones were used using the MaxEnt model. This was only done for KwaZulu Natal. Lastly, the study assessed management strategies such as optimum planting date, plant density, row spacing, and fertiliser inputs for sorghum. The review classified LSA methods reported in articles as traditional (26.6%) and modern (63.4%). Modern approaches, including multicriteria decision-making (MCDM) methods such as AHP (14.9%) and fuzzy methods (12.9%), crop simulation models (9.9%) and machine-learning-related methods (25.7%), are gaining popularity over traditional methods. The review provided the basis and justification for land suitability analysis (LSA) methods to map potential growing zones of NUS. The review concluded that there is no consensus on the most robust method for assessing NUS's current and future suitability. South Africa is a water-scarce country, and rainfall is undoubtedly the dominating factor determining crop production, especially in marginal areas where irrigation facilities are limited for smallholder farmers. Based on these challenges, there is a need to characterise bioclimatic zones in SA that can be qualified under water stress and with high rainfall variation. Mapping high-risk agricultural drought areas were achieved by using the Vegetation Drought Response Index (VegDRI), a hybrid drought index that integrates the Standardized Precipitation Index (SPI), Temperature Condition Index (TCI), and the Vegetation Condition Index (VCI). In NUS production, land use and land classification address questions such as “where”, “why”, and “when” a particular crop is grown within particular agroecology. The study mapped the current and future suitable zones for NUS. The current land suitability assessment was done using Analytic Hierarchy Process (AHP) using multidisciplinary factors, and the future was done through a machine learning model Maxent. The maps developed can contribute to evidence-based and site-specific recommendations for NUS and their mainstreaming. Several NUS are hypothesised to be suitable in dry regions, but the future suitability remains unknown. The future distribution of NUS was modelled based on three representative concentration pathways (RCPs 2.6, 4.5 and 8.5) for the years between 2030 and 2070 using the maximum entropy (MaxEnt) model. The analysis showed a 4.2-25% increase under S1-S3 for sorghum, cowpea, and amaranth growing areas from 2030 to 2070. Across all RCPs, taro is predicted to decrease by 0.3-18 % under S3 from 2050 to 2070 for all three RCPs. Finally, the crop model was used to integrate genotype, environment and management to develop one of the NUS-sorghum production guidelines in KwaZulu-Natal, South Africa. Best sorghum management practices were identified using the Sensitivity Analysis and generalised likelihood uncertainty estimation (GLUE) tools in DSSAT. The best sorghum management is identified by an optimisation procedure that selects the optimum sowing time and planting density-targeting 51,100, 68,200, 102,500, 205,000 and 300 000 plants ha-1 and fertiliser application rate (75 and 100 kg ha-1) with maximum long-term mean yield. The NUS are suitable for drought-prone areas, making them ideal for marginalised farming systems to enhance food and nutrition security

Perancangan Sistem Prediktor Cuaca dengan Metode ANFIS untuk Menentukan Produktivitas Panen Sayuran Kubis Putih (Brassica oleracea var. capitata) di Karangploso Kabupaten Malang

Kubis (Brassica oleracea var. capitata) merupakan sayuran yang banyak ditanam oleh petani di Indonesia karena permintaan pasar yang besar dan masa tanam yang tidak terlalu lama. Namun, cuaca ekstrem sering tidak terduga beberapa tahun ini membuat peluang terjadinya gagal panen semakin meningkat. Tujuan dari penelitian ini adalah merancang suatu sistem prediksi cuaca satu bulan kedepan dengan metode ANFIS (Adaptive Neuro Fuzzy Inference System) yang meliputi suhu udara, kelembaban dan curah hujan. Sistem prediksi cuaca ini selanjutnya akan digunakan untuk memperkirakan produktivitas kubis hasil panen. Perancangan prediktor dilakukan dengan dua skenario yaitu sistem prediksi cuaca dengan data per 3 jam dan sistem prediksi cuaca dengan data bulanan. Masing-masing sistem prediktor tersebut dibagi menjadi dua menurut jenis masukannya, yaitu dengan model masukan time series dan multi variabel. Keempat model sistem prediktor cuaca tersebut menunjukkan bahwa sistem prediktor dengan data bulanan dan masukan model time series mempunyai galat yang paling kecil, yaitu 0.58ºC untuk suhu, 1.6% untuk kelembaban dan 1.39 mm untuk curah hujan. Variabel keluaran sistem prediktor yang baik tersebut selanjutnya dijadikan masukan sistem pengambilan keputusan produktivitas kubis. Performansi sistem pengambil keputusan kualitas kubis sebesar 58.3%. ======================================================================================================= Cabbage (Brassica oleracea var. capitata) is a vegetable that is commonly cultivated by farmers in Indonesia because of the huge market demand and the growing season is not too long. However, unpredictable extreme weather that happened this several years makes the chances of crop failure is increasing. The purposes of this study is to design a weather prediction system for a month later using ANFIS (Adaptive Neuro Fuzzy Inference System) method that includes air temperature, humidity and rainfall. The weather prediction system is used to estimate the productivity of the cabbage harvest. Weather predictor systems designed with two scenarios, weather prediction systems with per 3 hours data and weather prediction systems with monthly data. Based on the input type, each weather prediction system is subdivided into two types, the time series model and multivariate model. The fourth weather predictor model shows that the weather prediction system with monthly data and time series input type has the smallest error, 0.58ºC for temperature, 1.6% for humidity and 1.39 mm for rainfall. Output variables are good predictors of the system is then used as input predictor of the cabbage quality system. Validation of the quality cabbage shows the system performance of 58.3%

Precision Agriculture Technology for Crop Farming

This book provides a review of precision agriculture technology development, followed by a presentation of the state-of-the-art and future requirements of precision agriculture technology. It presents different styles of precision agriculture technologies suitable for large scale mechanized farming; highly automated community-based mechanized production; and fully mechanized farming practices commonly seen in emerging economic regions. The book emphasizes the introduction of core technical features of sensing, data processing and interpretation technologies, crop modeling and production control theory, intelligent machinery and field robots for precision agriculture production