A Review on the Application of Natural Computing in Environmental Informatics

Natural computing offers new opportunities to understand, model and analyze the complexity of the physical and human-created environment. This paper examines the application of natural computing in environmental informatics, by investigating related work in this research field. Various nature-inspired techniques are presented, which have been employed to solve different relevant problems. Advantages and disadvantages of these techniques are discussed, together with analysis of how natural computing is generally used in environmental research.Comment: Proc. of EnviroInfo 201

Prediction of Crop Yield Using LS-SVM

Agriculture sector is the backbone of Indian economy. Yield prediction of crop is very important problem in agriculture field. Predicting the yield is very popular among farmer nowadays, which especially contributes to the correct choice of crop for growing. Earlier, crop production was implemented by considering the farmer's experience on an appropriate field as well as crop. Information technology has an important area to predict the crop production because it includes the latest advancement. In this paper, our aim is to develop the system to achieve high accuracy and resolve the problem of yielding in agriculture. We use the LS-SVM and deep neural network learning technique to predict the yield of crop based physical and chemical feature of plant and soil. This system helps the farmer to take the right decision about sowing particular crop in available land

Data mining and wireless sensor network for agriculture pest/disease predictions

Data driven precision agriculture aspects, particularly the pest/disease management, require a dynamic crop-weather data. An experiment was conducted in a semi-arid region to understand the crop-weather-pest/disease relations using wireless sensory and field-level surveillance data on closely related and interdependent pest (Thrips) - disease (Bud Necrosis) dynamics of groundnut crop. Data mining techniques were used to turn the data into useful information/knowledge/relations/trends and correlation of crop-weather-pest/ disease continuum. These dynamics obtained from the data mining techniques and trained through mathematical models were validated with corresponding surveillance data. Results obtained from 2009 & 2010 kharif seasons (monsoon) and 2009-10 & 2010-11 rabi seasons (post monsoon) data could be used to develop a real to near real-time decision support system for pest/disease predictions

Agricultura de precisión con sensores inalámbricos

En el artículo se presentan las ideas generales de un sistema experto que forma parte del proyecto de investigación “Desarrollo de herramientas informáticas basadas en el conocimiento. Aplicaciones agrícolas”. El sistema experto cubrirá el área de cultivos cítricos y se está  desarrollada sobre una herramienta que también se obtiene como parte del proyecto

MEMPREDIKSI HARGA JUAL RUMPUT LAUT KERING PADA TINGKAT PETANI DENGAN DATA MINING

Abstrak Eucheuma cotonii sebagai salah satu jenis rumput laut kering cottonii (Dried Euchema Seaweed (DES)) adalah salah satu komoditas perikanan budidaya utama di Indonesia. Petani lokal menanam, memanen, dan mengeringkan rumput laut, dan kemudian menjualnya ke pedagang. Harga jual rumput lauttergantung pada faktor internal dan eksternal. Untuk memaksimalkan keuntungan mereka, para petani harus memperkirakan perkembangan harga di masa mendatang dengan menggunakan faktor-faktor ini. Penelitian ini menyajikan metode berbasis data baru untuk memperkirakan harga DES di masa depan untuk membantu petani dalam membuat prediksi tersebut. Dalam metode kami , kami menerapkan data mining untuk tugas memprediksi harga jual rumput laut pada saat penjualan, delapan minggu ke depan. Algoritma data mining, yaitu, penggolong, membutuhkan atribut sebagai inputnya. Dalam percobaan kami, kami mengidentifikasi tiga faktor internal dan tiga faktor eksternal sebagai atribut masukan. Atribut internal faktor yang digunakan adalah: harga DES terkini, kadar kebersihan dari DES, dan kadar air dari DES. Tiga eksternal-faktor atribut semua berhubungan dengan cuaca dan suhu minimum, suhu maksimum, dan curah hujan yang. Semua atribut yang diukur setiap hari di hari d. Output dari classifier adalah prediksi, klasifikasi biner yang menunjukkan apakah rumput laut harga jual pada waktu d ditambah delapan minggu lebih besar atau lebih kecil dari harga pada saat itu d

WHEAT YIELD PREDICTION USING NEURAL NETWORK AND INTEGRATED SVM-NN WITH REGRESSION

The production of wheat plays an important role in Pakistan’s economy. Wheat yield forecasting is significant farming problem as it’s the most important crop of Pakistan. Prediction of the wheat yield has been determined by data mining techniques with different environmental factors. Data mining techniques have been developed for analysing and implementation on wheat yield dataset to predict the yield which is very helpful to produce wheat. In this study, Neural Network and a Novel Integrated approach of Neural Network, Support Vector Machine and Regression are used to analyze and estimates the wheat yield production. We have used these predictive techniques with area, yield, production, soil pH, temperature, air pressure, rainfall, water availability, humidity, pesticides and fertilizer parameter for wheat yield prediction

Machine Learning in Agriculture: Crop Yield Prediction

The amount of crops harvested varies every year due to changes in climate and other operational as well as economic factors. Predicting the amount of crops a land will produce will result in more efficient field operations and management. At a national level, crop yield prediction can help work towards achieving food security. This, at a global level, will serve as a step towards the UN Sustainable Development Goal of Zero Hunger. This research identifies the significant factors that affect the production of staple crops in regions with desert and semi-arid climate in Africa and predict their yield. Different techniques are experimented to create the model and Random Forest proves to be the most suitable for this problem

Application of artificial neural networks in the design of drainage systems in data-poor areas.

Deeks, Lynda K. - Associate SupervisorDrainage has been identified as an often-neglected component of irrigated agriculture in arid and semi-arid areas. Even though it is accepted that drainage is often necessary to prevent waterlogging and salinity impacting productivity in irrigated agriculture, it is typically ignored when planning future irrigation schemes. Only 5 – 10% of the total irrigated land in Least Developed Countries (LDCs) that requires drainage is currently drained (compared to 25 – 30% in developed countries). This is partly due to a fundamental lack of spatially and temporally coherent datasets containing key input parameters for drainage models, local expertise and the high cost of drainage installation. Drainage simulation models can provide reliable predictions of multi-component systems to evaluate drainage system design over long periods (1 – 100 years). This study evaluated existing drainage simulation models (i.e. DRAINMOD, SWAP, ADAPT, RZWQM2, EPIC, WaSim and HYDRUS-1D) for their suitability to be applied in data-poor arid and semi-arid regions. Based on a selection criteria, the most applicable model for drainage design in arid and semi-arid areas was DRAINMOD. DRAINMOD, an agricultural drainage simulation model, is a versatile and readily available model that can be used to evaluate alternative drainage system designs. DRAINMOD requires several key inputs, including saturated hydraulic conductivity (Ksat), reference evapotranspiration (ET0) and the Electrical Conductivity of a saturated soil Extract (ECe). In LDCs, measuring these parameters is expensive and time-consuming. In addition, existing historic datasets are often spatially and temporally limited. Therefore, indirect approaches are needed to overcome incomplete data records that restrict drainage designs. This thesis evaluates the feasibility of applying indirect methods, with a focus on developing and validating the use of artificial neural networks (ANNs) using available historic measured datasets. The study data draws on the drainage design for Hammam Agricultural Project (HAP) and Eshkeda Agricultural Project (EAP), located in the south of Libya, north of the Sahara Desert. Soil texture, bulk density, field capacity, and wilting point were used to develop ANNs to predict Ksat which were significantly more accurate compared to widely adopted Pedotransfer functions (PTFs) such as Rosetta3. To calculate the daily ET0, average monthly maximum and minimum air temperature were used to develop ANNs. Arithmetic Averaging of Neighbouring Stations (AANS), MODAWEC and Era5-Land were among the indirect methods applied to predict ET0. Landsat 5 Surface Reflectance bands and the derived salinity indices were applied to develop ANNs to estimate ECe. The accuracy of the predicted values of Ksat, ET0 and ECe were evaluated by using statistical parameters such as coefficient of determination (R²), mean square error (MSE), and root mean square error (RMSE). The predicted Ksat and ET0 values were input to DRAINMOD to design drainage systems in EAP and HAP as compared to the optimum design based on measured data. The design focused on how accurately the predicted values were able to estimate drain spacing, relative yield, irrigation depth, and drainage discharge. The key findings showed that the accuracy of predicting Ksat greatly impacted predicting the optimum drain spacing and the associated relative yield. Accurate prediction of the optimum spacing between drains will reduce the overall cost by ensuring that the drains are not spaced too closely, but also lowers the risk of raising the water table and negatively impacting the yield by preventing the drains being installed on too wider a spacing. In addition, precisely predicting ET0 is essential to quantify the irrigation water requirement and drainage discharge. Finally, predicting soil salinity using remote sensing data can be used as an early warning tool to monitor irrigated lands affected by salinity, evaluate the performance of existing drainage systems, and indicate areas that need improvement. Future research recommendations identified by this research include the need for (1) critical evaluation of the accuracy of using ANNs and other machine learning approaches to predict other input parameters required for drainage design such as the water retention curve, depth of impermeable layer, hourly or daily rainfall, and initial water table depth. (2) development and validation of ANNs and other machine learning approaches that can predict Ksat, ET0, and ECe on a national level (Libya) and/or regional level (Middle East and North Africa) to overcome the challenge of incomplete data records that restrict drainage designs.Libyan Ministry of Higher Education & Scientific Research, via the Government of National Unity, Libyan Academic Attach ́e – London (FA042-185-5447)PhD in Environment and Agrifoo