Accurate prediction of sugarcane yield using a random forest algorithm

International audienceAbstractForeknowledge about sugarcane crop size can help industry members make more informed decisions. There exists many different combinations of climate variables, seasonal climate prediction indices, and crop model outputs that could prove useful in explaining sugarcane crop size. A data mining method like random forests can cope with generating a prediction model when the search space of predictor variables is large. Research that has investigated the accuracy of random forests to explain annual variation in sugarcane productivity and the suitability of predictor variables generated from crop models coupled with observed climate and seasonal climate prediction indices is limited. Simulated biomass from the APSIM (Agricultural Production Systems sIMulator) sugarcane crop model, seasonal climate prediction indices and observed rainfall, maximum and minimum temperature, and radiation were supplied as inputs to a random forest classifier and a random forest regression model to explain annual variation in regional sugarcane yields at Tully, in northeastern Australia. Prediction models were generated on 1 September in the year before harvest, and then on 1 January and 1 March in the year of harvest, which typically runs from June to November. Our results indicated that in 86.36 % of years, it was possible to determine as early as September in the year before harvest if production would be above the median. This accuracy improved to 95.45 % by January in the year of harvest. The R-squared of the random forest regression model gradually improved from 66.76 to 79.21 % from September in the year before harvest through to March in the same year of harvest. All three sets of variables—(i) simulated biomass indices, (ii) observed climate, and (iii) seasonal climate prediction indices—were typically featured in the models at various stages. Better crop predictions allows farmers to improve their nitrogen management to meet the demands of the new crop, mill managers could better plan the mill’s labor requirements and maintenance scheduling activities, and marketers can more confidently manage the forward sale and storage of the crop. Hence, accurate yield forecasts can improve industry sustainability by delivering better environmental and economic outcomes

Probabilistic and artificial intelligence modelling of drought and agricultural crop yield in Pakistan

Pakistan is a drought-prone, agricultural nation with hydro-meteorological imbalances that increase the scarcity of water resources, thus, constraining water availability and leading major risks to the agricultural productivity sector and food security. Rainfall and drought are imperative matters of consideration, both for hydrological and agricultural applications. The aim of this doctoral thesis is to advance new knowledge in designing hybridized probabilistic and artificial intelligence forecasts models for rainfall, drought and crop yield within the agricultural hubs in Pakistan. The choice of these study regions is a strategic decision, to focus on precision agriculture given the importance of rainfall and drought events on agricultural crops in socioeconomic activities of Pakistan. The outcomes of this PhD contribute to efficient modelling of seasonal rainfall, drought and crop yield to assist farmers and other stakeholders to promote more strategic decisions for better management of climate risk for agriculturalreliant nations

Evaluation of Three Feature Dimension Reduction Techniques for Machine Learning-Based Crop Yield Prediction Models

Machine learning (ML) has been widely used worldwide to develop crop yield forecasting models. However, it is still challenging to identify the most critical features from a dataset. Although either feature selection (FS) or feature extraction (FX) techniques have been employed, no research compares their performances and, more importantly, the benefits of combining both methods. Therefore, this paper proposes a framework that uses non-feature reduction (All-F) as a baseline to investigate the performance of FS, FX, and a combination of both (FSX). The case study employs the vegetation condition index (VCI)/temperature condition index (TCI) to develop 21 rice yield forecasting models for eight sub-regions in Vietnam based on ML methods, namely linear, support vector machine (SVM), decision tree (Tree), artificial neural network (ANN), and Ensemble. The results reveal that FSX takes full advantage of the FS and FX, leading FSX-based models to perform the best in 18 out of 21 models, while 2 (1) for FS-based (FX-based) models. These FXS-, FS-, and FX-based models improve All-F-based models at an average level of 21% and up to 60% in terms of RMSE. Furthermore, 21 of the best models are developed based on Ensemble (13 models), Tree (6 models), linear (1 model), and ANN (1 model). These findings highlight the significant role of FS, FX, and specially FSX coupled with a wide range of ML algorithms (especially Ensemble) for enhancing the accuracy of predicting crop yield

Improvements to the functionality of the mycanesim® irrigation scheduling advice system for sugarcane.

Master of Science in Bioresources Engineering. University of KwaZulu-Natal, Pietermaritzburg 2016.MyCanesim® is a web-based crop simulation system that can be used for irrigation scheduling and yield estimation. Two shortcomings of the system identified were that 1) advised irrigation amounts could exceed seasonal water limitations imposed on farmers and 2) simulations are only accurate if farmers follow the recommended irrigation actions and if simulated and actual available soil water content are similar. These can be addressed by incorporating algorithms for optimal scheduling of limited water, and by making use of soil water content measurements in model simulations. The objectives of this study were to 1) evaluate the performance of different optimization algorithms that schedule limited water and 2) determine the accuracy of irrigation scheduling advice and cane yield estimates with and without adjustment of simulations with soil water content records. Four irrigation scheduling algorithms were tested against a baseline algorithm, using 960 hypothetical scenarios consisting of different water supply, climate and cropping situations. These were: (a) Crop stage, which accounts for the yield sensitivity to water deficit as it varies with growth stage; (b) Stress level, which evaluates different soil water depletion levels for determining irrigation dates; (c) Prorata, which reduces irrigation throughout the growing season in proportion to the seasonal allocation shortfall; and (d) Water satisfaction, which iteratively schedules irrigation events on the day with the largest water demand. Algorithms increased simulated yields over the baseline by between 4.7 and 8.6 t/ha on average and operated at computational running times of between 1 and 40 s. The stress level algorithm was recommended for inclusion into MyCanesim®, since it had both a high yield improvement (8.5 t/ha) and quick operational time (2.5 s). Soil water measurements from capacitance probes for thirteen fields in Mpumalanga were integrated through an automated process into the MyCanesim® system. The improvements in the accuracy of irrigation scheduling advice and yield estimates by the integrated system were assessed retrospectively. The integrated system resulted in more accurate irrigation scheduling advice (by 2 days) than weather-based scheduling alone. These two improvements to MyCanesim® should allow sugarcane farmers to achieve higher irrigated water use efficiency and yields because of more accurate irrigation scheduling advice and yield estimates for full and restricted irrigation water supply

Variabilidade espacial e temporal dos atributos do solo e sua relação com a produtividade agrícola, parâmetros topográficos e condutividade elétrica aparente (CEa) em lavouras de cana-de-açúcar

Orientador: Paulo Sérgio Graziano MagalhãesTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia de AlimentosResumo: A produção de etanol no Brasil deverá ser de 54 bilhões de litros em 2030 para atender ao acordo firmado na COP21, o que representa o dobro da produção de etanol verificada em 2016. Do ponto de vista agronômico há duas alternativas: ou aumenta-se a área plantada com a cultura ou aumenta-se a produtividade por área. Ambientalmente não há dúvidas que o aumento da produtividade é a melhor alternativa, sendo que a agricultura de precisão (AP) será fundamental para contribuir com a sustentabilidade da produção. Atualmente a AP nas lavouras de cana-de-açúcar no Brasil está longe do potencial que as tecnologias disponíveis podem proporcionar para o manejo adequado da cultura. O principal objetivo da presente tese é demonstrar como as tecnologias de PA, mais especificamente, monitores de rendimento, parâmetros topográficos e sensores de condutividade elétrica aparente (CEa), podem ajudar os agricultores a gerenciar os campos de forma específica do local. Para tanto, os atributos do solo que impactam diretamente a produtividade das culturas foram avaliados espacial e temporalmente, associando esses elementos do solo com parâmetros topográficos e CEa. Os objetivos são fornecer indicadores qualitativos e quantitativos para uma caracterização espacial precisa dos campos, mostrando o potencial dos parâmetros topográficos e CEa para melhorar o manejo específico do local dos campos de cana-de-açúcar. Para aumentar a produtividade, os resultados mostraram que a matéria orgânica (MO) disponível no solo, teor de argila e capacidade de troca catiônica (CTC) são os fatores que impactam diretamente a produtividade da cana-de-açúcar. Além disso, a variabilidade temporal na produtividade foi causada principalmente pela variabilidade no pH do solo. Uma avaliação abrangente da variabilidade espacial dos atributos do solo relacionados aos parâmetros topográficos evidenciou padrões espaciais que foram temporalmente remanescentes. Os resultados mostraram que as classes morfométricas horizontais (HConv, HPlan e HDiv), associadas às áreas côncavas (Vconc), apresentaram maiores teores de MO, Soma de Bases (SB) e CTC, indicando que essas áreas apresentam maior fertilidade do solo, onde a formação VConcHDiv apresentou a maior fertilidade do solo. Para todas as classes morfométricas verticais (VConc, VRet e VConv), os níveis de pH do solo foram maiores quando associados a áreas divergentes (HDiv) e menores quando associados a áreas convergentes (HConv), sugerindo um manejo mais rigoroso da acidez do solo nas áreas HConv. As áreas VConvHConv, onde a menor fertilidade do solo foi observada, devem ser amostradas com maior acurácia para adequada caracterização espacial do solo, devido ao alto Coeficiente de Variação (CV) observado quando comparado a outras classes morfométricas avaliadas. Além disso, as classes de CEa, divididas pelo método do quantil, mostraram que os locais de menor condutividade elétrica apresentam menores teores de MO e CTC. As classes de CEa mais altas mostraram CV menor para todos os atributos do solo avaliados, ou seja, locais que podem ser caracterizados com menores quantidades de amostras para um mapeamento de solo adequado. A variabilidade do conteúdo de argila foi diretamente proporcional à variabilidade da CEa (R2 = 0,97). MO (R2 = 0,65) e CTC (R2 = 0,76) também apresentaram boa correlação com a variabilidade da CEa. Com alta estabilidade espacial e temporal, os parâmetros topográficos e da CEa são excelentes fontes de informação (economicamente viáveis e de fácil avaliação) para apoiar os processos de amostragem do solo e mapear as zonas de fertilidade nos camposAbstract: The ethanol production should be 54 billion liters in 2030, almost double of the current production. From the agronomic point of view, two alternatives are possible; increase the planted area and/or agricultural yield to reach the goals. Environmentally, increase the yield is a more sustainable option, and the adoption of Precision Agriculture (PA) will be essential. The current use of PA in Brazilian sugarcane industry is very far from its full potential. The main objective of the present thesis is to demonstrate how PA technologies, more specifically yield monitors, topographic parameters and apparent electrical conductivity (ECa) sensors, can help farmers to manage fields in a site-specific way. For this purpose, soil attributes that directly impact crop yield were spatially and temporally evaluated, associating these soil elements with topographic and ECa parameters. The aims are to provide qualitative and quantitative indicators for a precise soil spatial characterization of fields, showing the potential of topographic and ECa parameters to improve the site-specific management of sugarcane fields. To increase the yield, the findings showed that the amount of available soil organic matter (OM), clay content and cation exchange capacity (CEC) are important factors that directly impact sugarcane yield. Furthermore, the temporal variability in the yield is caused mainly by the variability in the soil pH. A comprehensive assessment of the spatial variability of soil attributes related to topographic parameters evidencing spatial patterns that were temporally remained. The results showed that the horizontal morphometric classes (HConv, HPlan and HDiv), associated with vertical concave areas (VConc), presented higher levels of OM, Sum of Bases (SB) and CEC, which indicated that these areas have higher soil fertility, where VConcHDiv showed the highest soil fertility. For all vertical morphometric classes (VConc, VRet and VConv), soil pH levels were higher when associated with horizontal divergent areas (HDiv) and lower when associated with convergent areas (HConv), suggesting that stricter soil acidity management was needed in the HConv areas. The VConvHConv areas, where the lower soil fertility was observed, should be sampled with greater accuracy for adequate soil spatial characterization due to the high CV observed when compared to other morphometric classes assessed. Furthermore, ECa classes, defined by quantil method, showed that the low electrical conductivity sites present lower OM and CEC contents. The higher ECa classes showed smaller CV for all soil attributes assessed, i.e., sites that can be characterized with smaller amounts of samples to an adequate soil mapping than lower ECa classes. The clay content variability was directly proportional to the ECa variability (R2 = 0.97). OM (R2 = 0.65) and CEC (R2 = 0.76) showed great correlation with ECa variability too. With high spatial and temporal stability, topographic and ECa parameters could be excellent (economically feasible and easily assessed) sources of information to support soil sampling processes and to map fertility zones within fieldsDoutoradoBioenergiaDoutor em Ciências2014/14965-0FAPES

Flood Forecasting Using Machine Learning Methods

This book is a printed edition of the Special Issue Flood Forecasting Using Machine Learning Methods that was published in Wate

Multi-purposeful Application of Geospatial Data

Agriculture is the backbone of the Indian economy. Any changes in weather and climate in short term as well as long- term adversely affect the agricultural productivity and the production of food grain production. In order to minimise the adverse impact of weather and climate on crops, the use of agrometeorological information and agromet services has already been proved to be highly beneficial. Agrometeorological services rendered by India Meteorological Department (IMD), Ministry of Earth Sciences, are a step to contribute to weather information-based crop/livestock management strategies and operations dedicated to enhance crop production and food security. IMD is operating a project ‘Gramin Krishi Mausam Sewa’ (GKMS) with an objective to serve the farming community at different parts of the country. Different states of technologies including the application of geospatial technology are being used in India for further refinement of the Agromet Advisory Services. The application of geospatial technology in generating agrometeorological information and products is very necessary for preparing need-based advisories at a high-resolution scale for the farmers in the country. In this chapter, elaborate discussion has been made on how the Geographical Information System (GIS) is being used for generating information and products using ground observations as well as satellite observations

Machine Learning-Based Sugarcane Yield Prediction Using Multispectral Time-Series Imagery

Accurate sugarcane yield prediction is important for the sugar industry in serving the demands for decision-making systems such as harvest timing, product handling, and forward sales. Accurate yield modelling offers sugarcane farmers with a deeper knowledge of spatial and temporal crop variability, enhancing the quality and quantity of sugarcane yields while minimizing production costs and alleviating adverse environmental consequences. High-performance Machine Learning (ML) algorithms were applied to Remote Sensing (RS) images so that the timely acquired data with both spatial and temporal resolutions could be processed efficiently to interpret the complexity and variability of sugarcane yield. In this context, we tested advanced ML algorithms on diverse RS datasets such as Unmanned Aerial Vehicle (UAV), Sentinel-2, and Landsat-8 images, validated the results using ground measurements such as wet and dry biomasses, and crop yields; and developed a model that predicts sugarcane crop yield at the earliest possible growth stage with the least amount of spectral data. To demonstrate the scalability of the proposed yield prediction model, its performance was assessed in two regions: an experimental site in Queensland, Australia, and some sugarcane fields in Khuzestan Province, Iran. The predictive model was expanded using freely accessible Sentinel-2 satellite data so that it could be applied to a variety of sugarcane yield studies in various crop systems. For example, the expanded prediction model is particularly useful if ground data collection is limited, or UAV data is not feasible due to surveying costs. This research is anticipated to benefit agricultural producers and farmers in their decision-making and agricultural operation planning and help establish their management practises for optimal productivity