Mapping Dindéresso Forest Landscapes with Sentinel-2 and Machine Learning | InformativeBD

Boalidioa Tankoano,  Dramane Ouedraogo,  Zézouma Sanon, Jérôme T. Yameogo, and Mipro Hien, from the different institute of Burkina Faso. wrote a Reseach Article about, Mapping Dindéresso Forest Landscapes with Sentinel-2 and Machine Learning. Entitled, Mapping heterogeneous landscapes using sentinel-2 imagery and machine learning algorithms: A case of the Dindéresso classified forest. This research paper published by the International Journal of Biosciences (IJB). an open access scholarly research journal on Biosciences. under the affiliation of the International Network For Natural Sciences| INNSpub. an open access multidisciplinary research journal publisher.

Abstract

The anthropization of natural ecosystems has not excluded the domain classified by the State. As a result, the landscape of protected areas such as the Dinderesso Classified Forest is highly heterogeneous. The overall objective was to assess the performance of machine learning algorithms in better mapping the land use classes of the Dinderesso Classified Forest. To do this, a Sentinel-2 image and information collected in the field were used. The Sentinel-2 image was classified using Random Forest and Support Vector Machine algorithms. 850 regions of interest were selected for model training and validation. Random Forest performed best, with a Kappa coefficient of 91.49% compared with 90.17% for Support Vector Machine. The F-score for the Bare land and Agroforestry parks class was the highest (0.98) and the Gallery and Dense Vegetation class had the lowest F-score (0.82). Both algorithms showed high levels of performance, so they are suitable for classifying heterogeneous landscapes. The proportion of the Bare land and Agroforestry parks class was 29.29% compared with 70.71% for the natural formation classes (shrub savannahs, tree savannahs, Gallery, and Dense Vegetation). Given the level of anthropization of the Classified Forest, measures need to be taken to limit this process to conserve biodiversity.

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Introduction

Burkina Faso, a Sahelian country, is home to major reservoirs of biodiversity in West Africa (Ouoba, 2006; Tankaono et al., 2017; Tiendrebeogo et al., 2019). The State's classified domain, which covers around 14% of the national territory, is the foundation of the national biodiversity conservation policy (Tankaono et al., 2016; Zida et al., 2015). However, human activities such as inappropriate agricultural practices, overpopulation, exploitation, and urban sprawl, combined with the poverty of rural populations, constitute serious threats to this classified State domain (Tankoano et al., 2015; Sanon et al., 2019). According to the latest report on Burkina Faso's forests, around 60% of the country's protected areas are under human occupation (DIFOR, 2007). Between 1990 and 2015, the surface area of plant cover was reduced by around 1% per year (FAO, 2015). One of the main causes of this deforestation of protected areas is agriculture and gold panning (Ouedraogo et al., 2010; Dimobe et al., 2015; Soulama et al., 2015; Zoungrana et al., 2015; Semeki Ngabinzeke et al., 2016). These two main activities lead to the fragmentation of the forest ecosystems in these protected areas (Kabulu et al., 2008; Kpedenou et al., 2016; Tankoano et al., 2016; Sanon et al., 2019). Faced with this situation, monitoring the country's last vestiges of biodiversity is becoming crucial, even imperative, at the risk of witnessing an erosion of national biodiversity. Unfortunately, financial and human resources are lacking.

Most studies concerning vegetation cover mapping in Burkina Faso are based on Landsat satellite images, but very few have used Sentinel-2 images. Nowadays, remote sensing has become a powerful tool for monitoring protected areas. Satellite imagery is commonly used to study the dynamics of land-use units, mutations between land-use units, and the impacts of agricultural activities and logging (JofackSokeng et al., 2016; Gansaonré et al., 2020; Tankoano et al., 2023). These various activities within protected areas lead to a certain het erogeneity in the landscape, which makes it difficult to classify land-use units with a high level of precision.

More and more satellites and classification algorithms are being developed for this purpose. Machine learning algorithms are also being used to classify satellite images. Sentinel-2 images, with their high resolution (10m), make it easier to detect the smallest units in the landscape. Machine learning algorithms enable accurate cartographic results, facilitating timely decision-making by protected area managers. 

However, the application of machine learning algorithms in classifying heterogeneous ecosystems has been explored little. Their contribution to improved accuracy, hence the reduction of interclass confusion, therefore needs to be explored in highly heterogeneous savannah ecosystems.

This study aims to evaluate the ability of machine learning algorithms to classify a heterogeneous landscape using a sentinel-2 image with high accuracy. Specifically, the aim was to (i) map the Dinderesso Classified Forest using a Sentinel-2 image and machine learning ; (ii) assess the ability of each two machine learning algorithms (RF and SVM) to better classify the land use/land cover within Dinderesso classified forest.

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Source : Mapping heterogeneous landscapes using sentinel-2 imagery and machine learning algorithms: A case ofthe Dindéresso classified forest 

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Machine Learning Predicts Thrips Occurrence in Tomatoes Under Weather Variations | InformativeBD

Satish Kumar Yadav,  from the institute of India. D. Pawar, from the institute of India. Latika Yadav , from the institute of India. and Saurabh Tripathi, from the institute of India. wrote a Research Article about, Machine Learning Predicts Thrips Occurrence in Tomatoes Under Weather Variations. Entitled, Predictive Analysis of Occurrence of Thrips in Tomato Subject to Weather Parameters Using Machine Learning Techniques. This research paper published by the International Journal of Biosciences (IJB).  an open access scholarly research journal on Biosciences. under the affiliation of the International Network For Natural Sciences | INNSpub. an open access multidisciplinary research journal publisher.

Abstract

Thrips (Thripidae) on tomato (Solanum lycopersicum L.) at Rajendranagar, Andhra Pradesh, India is modelled based on field data sets generated during six kharif seasons [2011-18]. The weather variables considered are maximum & minimum temperature (MaxT & MinT) (0C), morning and evening humidity (RHM & RHE) (%), sunshine hours (SS) (hr/d), wind velocity (Wind) (km/hr), total rainfall (RF) (mm) and rainy days (RD). Thrips incidence was higher during 2012 and lowest in 2014. Correlation analyses significant positive influence of maximum temperature and negative influence of wind of one lags, RHM both current and one lags, rainfall one lag of negative influence on thrips. Machine learning techniques namelyAn empirical comparison of the above models [support vector regression (SVR), random forest (RF) and the other statistical models e.g., multiple linear regression (MLR), ridge regression (RR), least absolute shrinkage and selection operator (LASSO), and elastic net (EN)] is based on root mean square error (RMSE). It is observed that, for thrips, the RMSE values of RF and LASSO are less as compared to other competing models. Diebold-Mariano (D-M) test was applied for comparison of forecasting performance among the applied models. It is observed that, predictive accuracy of RF and LASSO is higher than that of other models.

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Introduction

Tomato (Solanum lycopersicum L.) is one of the most popular produced and extensively consumed vegetable crops in the world (Grandillo et al., 1999). It is one of the most important vegetable crops in India that can be eaten raw in salads or as an ingredient in many dishes and in drinks (Alam et al., 2007). Tomatoes and tomato-based foods provide a wide variety of nutrients and many health-related benefits to the body. In regions where it is being cultivated and consumed, it constitutes a very essential part of people’s diet. Tomatoes production accounts for about 4.8 million hectares of harvested land area globally with an estimated production of 165 million tonnes (FAOSTAT, 2017). China leads world tomato production with about 50 million tonnes followed by India with 17.8 million tonnes. Tomato production can serve as a source of income for most rural and peri-urban producers in most developing countries. Despite all the numerous benefits from the crop, many challenges are making its production unprofitable in most developing countries, especially those in Africa. The challenges faced by producers are seen either in production, post-harvest, marketing or a combination of any of them. The purpose of this paper is to look at the postharvest challenges that result in losses and recommend some low-cost intermediate technologies needed to remedy the situation. Accounting for about 8.23% of the total vegetable production in the country. Tremendous progress has been made in tomato production during the past four and half decades. At present, India is the fourth largest producer of tomato, accounting for 6.8% of the world production and the second largest in terms of acreage, accounting for 11.9% of area under tomato in the world. Tomato spotted wilt virus (TSWV) is widely distributed and has caused serious losses in the yield of this and many other crops in Australia, India, Nepal, China, Thailand, and USA. Early infections cause the most severe damage and can lead to total crop loss. Epidemics of insect-transmitted plant viruses in agricultural ecosystems require the interaction of 3 basic components: the host plant of the virus, the insect vector and the plant pathogenic virus. While this triad sounds quite straight forward, the relationships and interactions occurring between and among the basic triad components and the environment are complex and dynamic, frequently defying complete understanding by scientists and agricultural practitioners worldwide. Many plant viruses are transmitted by arthropod vectors (Nault, 1997). TSWV, the type species of the genus Tospovirus, family Bunyaviridae (Murphy et al., 1995), is exclusively transmitted by several thrips species in a propagative manner (German et al., 1992; Ammar, 1994; Goldbach and Peters, 1994). Tomatoes are susceptible to more than 200 diseases. Important achievements in chemical, biological, cultural and genetic control methods have greatly reduced economic losses and sometimes have eliminated them. Viral diseases are a special case since they cannot be controlled by chemical treatments. Crop protection must then rely on genetic resistance or on disease avoidance. TSWV was first reported in India in tomato in 1964 (Todd et al., 1975). The occurrence of TSWV on a legume in India was first recorded in 1968 (Reddy et al., 1968). Thrips (Thysanoptera: Thripidae) cause serious problems in the cultivation of a wide range of greenhouse and field crops. They create major damage on plants by causing reduction in plant growth, deformation of plant organs, and cosmetic damage in the form of silver scars on leaves and flowers. Thrips cause direct damage during feeding, plants should be released by breaking the leaf, fountain and fruits cells, leaving behind silvery patches and fruit sores to reduce plant yields and tomato market shortage (Riley and Pappu, 2004; Staford et al., 2011). And these are dependent on weather conditions (Verhage et al., 2017; Harvey et al., 2018). Thus, there is a need for the development of predictive models for the incidence of pests and diseases that can improve the interpretation of the crop cycle according to the weather, incorporating weather-soil-plant factors (Malau et al., 2018; Badnakhe et al., 2018). Machine learning is a method that works with data analysis and seeks to automate the construction of analytical models (Shekoofa et al., 2014; Li et al., 2016). It is a field of computer science that works with the recognition of patterns using computational learning theory in artificial intelligence (Sahoo et al., 2017). Machine learning algorithms are very promising for faster, more dependent variables and the meteorological elements are the independent variables of the models. Elastic Net (EN), a penalized variable selection approach that combines both ridge penalty and LASSO penalty. Different forecasting techniques e.g., Multiple Linear Regression (MLR); K Neighbors Regressor (KNN); Random Forest Regressor (RFT), and Artificial Neural Networks– Multilayer Perceptron (MLP), EN, LASSO are applied. The ridge method of MLR is utilized. This method avoids poor conditioning of the matrix of the repressor variables, controlling the inflation and the general instability found in least squares estimators. Ridge avoids the multicollinearity problem without having to exclude repressor variables, so it has no information loss.

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Source : Predictive Analysis of Occurrence of Thrips in Tomato Subject to Weather Parameters Using Machine Learning Techniques  

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