The photodegradation process of m-cresol (3-methylphenol), induced by Fe(III)-Cit complex, was investigated upon irradiation at 365 nm in natural water. The composition and photochemical properties of Fe(III)-Cit complex were studied by UV-Visible absorption spectrophotometer for optimizing the stoichiometry of the complex and photolysis under irradiation at 365 nm, respectively. A dark investigation of the system was performed before studying the photochemical behavior. The photooxidation efficiencies of m-cresol were dependent on the pH value, optimized at pH 2.86, oxygen, initial concentrations of Fe(III)-Cit complex, and m-cresol. Additionally, to look into the mechanism of m-cresol degradation using Fe(III)-Cit, tertiobutanol alcohol was used as scavenger for hydroxyl radicals and the result suggested that hydroxyl radical attack was the main pathway of m-cresol degradation. Besides, oxygen can enhance the photolysis of Fe(III)-Citrate complex by trapping the electron on the carbon centered radical formed after the photoredox process. Then O2•−formed reacts rapidly leading finally to formation of•OH radical. In absence of oxygen, less reactive species are formed; consequently the disappearance of m-cresol was strongly inhibited. Our work shows that the presence of Fe(III)-Citrate complex could have a considerable impact on the fate of organic pollutant in aquatic environment.

The (α‐FeOOH) Goethite composite is a stable and an efficient catalyst in aqueous suspension under irradiation at 365 nm and by solar light. The photocatalytic activities of this composite were evaluated using Phenol Red (PR) dye (phenolsulfonphthalein class). In the dark, controlling factors, such as the pH and the adsorption of PR on Goethite surface were evaluated (before starting the photochemical experiments). It was found that the system PR‐Goethite present a small decrease in the main band of the dye (435 nm) which was explained by the low rate of adsorption of this dye on the Goethite. Also, we note that 40% of PR decolourisation was obtained after 200 min by the system PR‐Goethite‐hydrogen peroxide (H2O2) in dark due to the formation of •OH by thermal decomposition of H2O2 on the surface of Goethite. The effects of various experimental parameters, such as initial dye concentration, pH, photocatalyst amount, tert‐Butyl alcohol effect and H2O2 addition were investigated in the study of photodegradation of the dye. The results showed that the photodegradation of PR under UV‐A (365 nm) irradiation could be enhanced greatly in the presence of H2O2. Natural radiation tests (under sunlight) showed that degradation was faster comparing with that obtained using the artificial one at 365 nm. Studies of the mineralization using total organic carbon method under naturel light certify that this method, compatible with the environment, may be considered in the treatment of wastewater and generally in the process of removal of this kind of pollutant.

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The degradation of phenolic compounds (PC) by Fe(III) aquacomplexes was investigated in homogeneous phase at neutral pH in dark and using monochromatic irradiation lamp (365 nm). The process of degradation depends essentially on the concentration of monomeric species, which is the most reactive species in terms of HO• radicals formation. A dark study of a system containing a mixture of phenolic compounds (3,5-dimethylphenol, 2,6-dimethylphenol, m-methylphenol, phenol) and Fe(III) was performed before starting the photochemical study. In this step only the 2,6-dimethylphenol give a thermal reaction with iron in homogenous solution. Under monochromatic excitation (365 nm), the photodegradation of PC induced by Fe(III) aquacomplexes showed that this process followed pseudo first-order kinetic. We investigate also the effect of initial concentrations of Fe(OH)2+, Fe(III) and the substrate. Tertiobutanol, used as scavenger of (HO•), confirmed that this radical was the main pathway of PC degradation. However the degradation of the 2,6-dimethylphenol in the presence of tertiobutanol, was not slowed completely which confirm that the degradation was a combination of thermal and photochemical reactions.

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<p><span lang="EN-US">The agriculture sector in the Tangier-Tetouan-Al-Hoceima-Region (Northern Morocco) contributes a significant percentage to the national revenue. The Larache Province is at the regional forefront in agriculture terms due to its large irrigated areas. Golden-Gogi is a biological farm located in the Larache Province, and its objective is to produce organic crops. Besides climate change, this farm suffers from biotic factors such as snails and insects. These problems cause diseases in plants, resulting in massive crop production losses. Early detection of disease and biotic factors in plants is a difficult task for farmers, but it is now possible thanks to artificial intelligence. For that reason, we aim to contribute to this Province by comparing the well-known models in machine learning (ML) and deep learning (DL) used in early plant disease detection to specify the best-classifier in terms of detecting mint plant diseases. Mint plant is a major crop on the Golden-Gogi farm, and its dataset was collected from there. As per findings, DL classifiers outperform ML classifiers in disease detection. The best-classifier is DenseNet201, with high accuracy of 94.12%. Hence, the system using DenseNet201 offers a solution for farmers of this Province in making urgent decisions to avoid mint yield losses.</span></p>

Agriculture is an important sector for developing countries and farmers. Recently, numerous techniques for increasing agricultural productivity have been utilized. However, different issues are still encountered by farmers including various plant diseases. Plant diseases diagnoses are challenging research, and they should be analyzed and treated by detecting the diseased plant leaves. For that reason, in this paper, we develop our proposed architecture using convolutional neural networks (OP-CNN) as a computer-aided to detect and diagnose plant diseases. The proposed architecture can assist farmers in increasing both the quantity and quality of their agricultural productivity. Besides this, the OP-CNN helps to reduce disease prevalence through early detection. The performance of our proposed model is compared with other convolutional neural networks (CNN) architectures in order to validate its capability. The strawberry dataset was employed to train and test the models since the strawberry is one of the main crops in the Larache Province (Morocco). The experimental tests demonstrate that our proposed OP-CNN reaches the highest values versus DenseNet121, VGG19, and ResNet50 with 100%, 99%, 97%, and 63% respectively for classification accuracy, 100%, 100%, 98% and, 79% respectively for precision, 100%, 99%, 97%, and 63% respectively for recall, and 100%, 99%, 97%, and 58% respectively for "F" _1Score.