Fruits Disease Classification using Machine Learning Techniques

Due to increased population, there is a high demand for agricultural products these days and therefore, effective growth and increased fruit production have become critical. Consequently, for better fruit yield cultivators employ traditional methods for monitoring fruit yield from harvest till ripening of fruit. However, manual monitoring and visual inspection doesn’t always bring the actual identification of fruit disease due to variety of reasons, such as less knowledge about pathogens, requiring more time for disease analysis and that too with less accuracy and so on, consequently, leaving for the need of a professional assistance and expertise. Moreover, the task also becomes difficult as various fruits demonstrate their gesticulation by changing the colour of their skin which can come from nature and resulting in various black or dark brown spots on the fruit skin indicating various diseases. As a result, it is necessary to propose an efficient smart farming strategy that will aid in increased productivity while at the same time involving less human effort. The proposed research work attempts to classify the fruit disease at its early stage by using machine learning techniques. For this purpose, fruit’s texture, and skin colour have been utilized. The approach fundamentally employs three machine learning classifier algorithms - KNN, Decision Tree, and Random Forest. Whereas the features have been determined by using three prominent feature extractors - Haralick, Hu Moments and colour histogram. Finally, the system has been evaluated by utilizing the k-fold cross validation method. Specimen dataset was divided into two groups — the training subset and the test subset. As a rule, four-fold cross-validation, three-fourths of the images were used for training the models whereas, the remaining one-fourth were used for testing purposes. Assessment results for suggested methodology after conducting experimentation on publicly available dataset and drawn confusion matrix and learning cure shows that Random Forest classifiers achieves accuracy about 99% while for K-Means accuracy statistics stands at 98.67% and for Decision trees it is about 97.75% - for colour histogram features

A preliminary investigation of a novel mortar based on alkali-activated seashell waste powder

International audienceThis paper deals with the evolution monitoring of biomass colonization and mechanical properties of 3D printed eco-materials/mortars immersed in the sea. Measurements of tensile strength, compressive strength, and Young’s modulus were determined on samples deployed along the Atlantic coast of Europe, in France, United Kingdom, Spain, and Portugal. The samples were manufactured using 3D printing, where six mix designs with a low environmental impact binder were used. These mortars were based on geopolymer and cementitious binders (Cement CEM III), in which sand is replaced by three types of recycled sand, including glass, seashell, and limestone by 30%, 50%, and 100% respectively. The colonization of concrete samples by micro/macro-organisms and their durability were also evaluated after 1, 3, 6, 12, and 24 months of immersion. The results showed that both biomass colonization and mechanical properties were better with CEM III compared to geopolymer-based compositions. Therefore, the mixed design optimized according to mechanical properties show that the use of CEM III should be preferred over these geopolymer binders in 3D printed concrete for artificial reef applications

Locating North African microrefugia for mountain tree species from landscape ruggedness and fossil records

International audienceIn order to optimize conservation policies for endangered plant species in North Africa and minimize the investment of the public resources we explore the capacity of a mountain plant species to persist locally in restricted natural areas. Palaeoecological studies have shown that plant species survived major global climate changes within refugia which offered suitable condition for their long term persistence. Our study aims at identifying potential mountains areas which may play the role of modern microrefugia for preserving locally endangered plant species.We analysed the mountain ruggedness of an area in the North-East of the Middle Atlas mountains where a population of an endangered plant species, Cedrus atlantica, is isolated today around lake Tameda. In addition, we collected a sediment core in the lake to investigate the recent history of the species with the local environmental changes. We compared the terrain and fossil analyses with an area in the Rif mountains where the terrain rugosity is lighter than in the Middle Atlas and where Atlas cedar populations occur as well.Our results show that the Atlas cedar is better preserved in terrains with high rugosity because they offer a wider panel of suitable microclimates for the species persistence and they restrict the number of inhabitants as well which, de facto, reduces the anthropogenic disturbances.We have carried out this analysis at a very small scale (less than 40km2). A more exhaustive analysis of the terrain rugosity over the Atlas and Rif mountains, combined with historical data, will help to identify more suitable refugial areas for preserving the species at a larger scale. Protecting these refugial areas over decades from any anthropogenic activity should be possible at a minimal cost and would represent an immediate response to the ongoing climate change for preserving endangered species

Refining the outputs of a dynamic vegetation model (CARAIB):Research at ULiège, Belgium

Dynamic vegetation models (DVMs) are process-based models combining the inputs and the outputs of sub-models, possibly in feedback loops, to simulate the plant functions. The sub-models compute conditions outside and inside the plant and physiological reactions from the environmental data (climate, light intensity, air CO2 concentration, soil properties). DVMs are tools of choice to predict the future and the past of the vegetation taking into account climatic variations. The emergence of new questions in the context of climate change, particularly on threatened species or on commercial species, compels to apply DVMs to species while the information to parameterize and validate them is largely lacking. Of particular importance are the morpho-physiological traits. These were intensively studied within the hypothesis that they could be used to predict plant performances. This hypothesis finally revealed not very suitable, but it brought to light that important traits controlling photosynthesis and water relationships could strongly vary within each species in response to environmental conditions. We studied the Atlas cedar (Cedrus atlantica (Endl.) Manetti ex Carrière), in Morocco (northern Africa). It is a threatened tree species of important economic value. We also studied the English oak (Quercus robur L.) and the sessile oak (Quercus petraea (Matt.) Liebl.) in eastern Belgium. In a series of localities, we determined several traits (specific leaf area, leaf C/N, sapwood C/N, as well as for the cedar, leaf longevity) and we assessed biomass and net primary productivity as validation data, thanks to forest inventories, dendrochronology analyses and allometric equations combined with leaf area index estimations. We compared the model simulations of the CARAIB DVM when varying the set of traits (direct site estimates or default values) to the field estimates of biomass and net primary productivity. We found that trait default values provide sufficient information for the DVM to compute mean output values but low ability to reproduce between site variations. On the contrary, the in situ traits improve drastically this ability, which indicates that the plant performances are the results of acclimation to the evolving local environmental conditions.Vulnérabilité des populations d'arbres montagnards des régions tropicales et tempérées chaudes sous des scénarios extrêmes (VULPES-ULg) / Vulnerability of tropical and warm temperate mountain tree populations under extreme scenarios (VULPES-ULg

Refining the outputs of a dynamic vegetation model (CARAIB): the importance of plant traits to improve prediction accuracy at tree species level

Dynamic vegetation models (DVMs) are process-based models combining the inputs and the outputs of sub-models, possibly in feedback loops, to simulate the plant functions. The sub-models compute conditions outside and inside the plant and physiological reactions from the environmental data (climate, light intensity, air CO2 concentration, soil properties). DVMs are tools of choice to predict the future and the past of the vegetation taking into account climatic variations. The emergence of new questions in the context of climate change, particularly on threatened species or on commercial species, compels to apply DVMs to species while the information to parameterize and validate them is largely lacking. Of particular importance are the morpho-physiological traits. These were intensively studied within the hypothesis that they could be used to predict plant performances. This hypothesis finally revealed not very suitable, but it brought to light that important traits controlling photosynthesis and water relationships could strongly vary within each species in response to environmental conditions. We studied the Atlas cedar (Cedrus atlantica (Endl.) Manetti ex Carrière), in Morocco (northern Africa). It is a threatened tree species of important economic value. We also studied the English oak (Quercus robur L.) and the sessile oak (Quercus petraea (Matt.) Liebl.) in eastern Belgium. In a series of localities, we determined several traits (specific leaf area, leaf C/N, sapwood C/N, as well as for the cedar, leaf longevity) and we assessed biomass and net primary productivity as validation data, thanks to forest inventories, dendrochronology analyses and allometric equations combined with leaf area index estimations. We compared the model simulations of the CARAIB DVM when varying the set of traits (direct site estimates or default values) to the field estimates of biomass and net primary productivity. We found that trait default values provide sufficient information for the DVM to compute mean output values but low ability to reproduce between site variations. On the contrary, the in situ traits improve drastically this ability, which indicates that the plant performances are the results of acclimation to the evolving local environmental conditions.Vulnérabilité des populations d'arbres montagnards des régions tropicales et tempérées chaudes sous des scénarios extrêmes (VULPES-ULg) / Vulnerability of tropical and warm temperate mountain tree populations under extreme scenarios (VULPES-ULg

Towards a More Realistic Simulation of Plant Species with a Dynamic Vegetation Model Using Field-Measured Traits: The Atlas Cedar, a Case Study

International audienceImproving the model-based predictions of plant species under a projected climate is essential to better conserve our biodiversity. However, the mechanistic link between climatic variation and plant response at the species level remains relatively poorly understood and not accurately developed in Dynamic Vegetation Models (DVMs). We investigated the acclimation to climate of Cedrus atlantica (Atlas cedar), an endemic endangered species from northwestern African mountains, in order to improve the ability of a DVM to simulate tree growth under climatic gradients. Our results showed that the specific leaf area, leaf C:N and sapwood C:N vary across the range of the species in relation to climate. Using the model parameterized with the three traits varying with climate could improve the simulated local net primary productivity (NPP) when compared to the model parameterized with fixed traits. Quantifying the influence of climate on traits and including these variations in DVMs could help to better anticipate the consequences of climate change on species dynamics and distributions. Additionally, the simulation with computed traits showed dramatic drops in NPP over the course of the 21st century. This finding is in line with other studies suggesting the decline in the species in the Rif Mountains, owing to increasing water stress