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Internet of Things (IoT) assisted smart farming techniques are gradually being used efficiently for identification and classification of vegetable plant diseases. Detection and classification of diseases in these plant families like Solanaceae are still problematic using DCNN due to variations in environmental conditions, genome variation, type of disease, etc. In this paper, two methods for spotting and diagnosing diseases of brinjal and tomato plants leaves named as Optimal Environmental Traversing Alert (OETA) and Optimum diagnosis of Solanaceae leaf diseases (ODSLD) respectively have been proposed. The OETA machine learning (ML) based method is used first to detect the disease, and then the ODSLD deep convolutional neural networks (DCNN) method is used to classify it. An analysis of the proposed method experiments showed that OETA disease detection for brinjal plant (eggplants) was 97.81 percent and for tomato plants was 99.03 percent. For disease classification by ODSLD method, the VGG-16 for brinjal plant and ResNet-50 for tomato plants outperformed other existing DCNN computer vision methods. [ABSTRACT FROM AUTHOR]

Global overpopulation necessitates increased crop yields, yet available arable land is limited. The study compares and evaluates the performance of three machine learning algorithms—Random Forest (RF), Extra Trees (ET), and Artificial Neural Network (ANN)—in crop yield prediction. Using 28,242 samples with seven features from 101 countries, we evaluated these models based on Mean Absolute Error (MAE), R-squared (R^2), and Mean Squared Error (MSE). The ET regression model demonstrated superior performance, achieving an MAE of 5249.03, the lowest among the models tested. Despite having the highest R^2 value of 0.9873, the ANN exhibited higher MAE and MSE values, indicating less reliability. The RF model showed intermediate results. With a prediction accuracy of 97.5%, the ET model proved to be the most effective for crop yield prediction, achieving the highest accuracy reported to date. Future research should explore more advanced algorithms and larger datasets to validate these findings further. [ABSTRACT FROM AUTHOR]

262 M6 mutants of soybean genotype BSS-2 and 254 M6 mutants of soybean genotype RKS-18 were used in the study conducted in 2019-20. Both genotypes underwent irradiation using five gamma-ray doses [50Gy, 100Gy, 150Gy, 200Gy and 400Gy] in Kharif 2014 under the BRNS-BARC project at the Department of Genetics and Plant Breeding, B.A.U., Ranchi. This resulted in 265 M6 mutant plants of BSS-2 and 254 M6 mutant plants of RKS-18 in 2018. Genetic parameters were evaluated. Non-replicated plant-to-progeny rows were grown for all mutants, and observations for quantitative traits were recorded on ten random plants of each mutant progeny of both genotypes. Both BSS-2 and RKS-18 demonstrated unique responses to different gamma-ray doses. RKS-18 displayed notable differences, particularly in the number of pods per plant, 100 seed weight and yield per plant. It indicated low genetic and phenotypic coefficients of variation, suggesting minimal environmental influence on trait expression. [ABSTRACT FROM AUTHOR]

Stress is essential for improving performance and enabling day-to-day efficient functioning. But when stress becomes more than a person can handle, it can negatively impact their productivity and health. An automated system for measuring stress can assist users in keeping their stress levels within healthy bounds by tracking stress levels via physiological and physical factors. It is crucial to create and evaluate different algorithms using a reference dataset that includes multimodal stress responses in order to establish such a system. Specific standards based on findings from both clinical and empirical research must be met by this dataset. In order to facilitate the establishment of a reference dataset for multimodal human stress detection, this work presents a thorough set of criteria. The suggested specifications cover both personal and technological elements, such as the selection of sample populations, the selection of stressors, the integration of various stress response modalities, the techniques for data annotation, and the choice of data collection tools. None of the stress datasets currently in the public domain entirely satisfied these predetermined standards, according to an analysis of them. Therefore, it is essential to give top priority to creating a reference dataset that complies with these particular specifications. The comparability and reliability of study findings in the field of stress detection will be improved by such an endeavour. [ABSTRACT FROM AUTHOR]