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Aug 2020 DOI 10.14302/issn.2641-4538.jphi-20-3475
A.J ThatheyusCorresponding author
PG & Research Department of Zoology, The American College, Madurai, Tamil Nadu, India.
Effluents discharged from various industries contain heavy metals. They reach the environment and affect the quality of air, water and soil. Though they are needed in trace quantities for living organisms, they become toxic when they exceed the threshold concentrations. Hence the present study has been designed to test the efficiency of Lycopersicon esculentum in removing zinc from soil. The tomato plants were grown in soil applied with 100, 200, 300, 400 and 500ppm of zinc sulphate for 60 days. Every fortnight, soil samples were taken and analysed for the levels of Cu, Zn, Fe and Mn. Percent removal of zinc by the plant was calculated from the residual concentration. More removal was noticed in higher concentrations of zinc. After 60 days of treatment, levels of Cu, Zn, Fe and Mn were analysed in the above ground and below ground parts of the tomato plant. Zinc level was 90 ppm in both cases and the same in plants grown in all the concentrations of zinc sulphate. Fluctuations in chlorophyll content were noticed while decline was observed in microbial colonies. The data were subjected to two way analysis of variance and the results are discussed. Graphical Abstract
May 2020 DOI 10.14302/issn.2639-3166.jar-20-3363
Masoero GiorgioCorresponding author
Accademia di Agricoltura di Torino, Via A. Doria 10, 10123 Torino (Italy).
Rapid analyses methods for the assessment of soil microbiota are lacking. In a commercial farm tomato plants were subjected to different fertilization strategies: 1. mineral Control (C); 2. Organic amendment (O); 3. Organic amendment + Micosat F © biofertilizer (OM). A first rapid method (Litterbag-NIRS) concerned hay litterbags coupled with a smart SCiOTM device. A second method (Foliar-NIRS) used the same device on the leaves. The plants showed positive responses to the amendment and biofertilization in the yield: C 60.5.1 t ha-1vs. 70.8 in O (+17%) and 74.2 in OM (+23% from C and + 5% (P 0.08) from O). The use of Litterbag-NIRS fingerprinting, completed with litterbags phenotyping and elaborated with a multivariate support vector machine classifier provided a similar knowledge to that obtained from microbial and chemical analyses of the soil. The reason for this response is that the analyses were embedded in the Litterbag-NIRS at medium-high precision. A polydromic function was hypothesized in order to disentangle the activities of different soil microbial populations from each other. The organic amendment delayed the functionality of the rapid r-strategist microbial populations, but at the same time activated slow k-strategists to intake the walls of the hay inside the litterbags. In this sense, the Litterbag-NIRS test can provide an effective “swamp” of the microbial fertility of the soil. Briefly, the Litterbag-NIRS coupled with Foliar-NIRS accounted for 95% of the average yield results, and both are therefore recommended for a rational assessment of microbial soil fertility.
Mar 2025 DOI 10.14302/issn.2638-4469.japb-25-5395
Divakar ChennamsettiCorresponding author
Plant leaf diseases pose significant threats to crop yield and agricultural sustainability, making early and accurate detection crucial for effective disease management. In current years, deep neural network (DNN) techniques have shown remarkable potential in the field of image classification, including plant disease detection. The study aims to investigate the performance of two popular deep learning architectures, namely, VGG16 and InceptionResNetV2, for the detection of tomato plant leaf disease. The proposed methodology involves acquiring a diverse dataset comprising high-resolution images of healthy and diseased leaves from the target crops. Preprocessing techniques such as image augmentation and normalization are applied to enhance the generalization ability of the models and mitigate overfitting. Transfer learning is employed to initialize the deep learning architectures with weights pre-trained on large-scale image datasets to accelerate convergence and improve the models' performance in limited data scenarios. To evaluate performance of proposed networks various metrics such as validation and test accuracies, precision and recall, F1 score, and the area under the curve (AUC) are considered. From the investigations, the classification accuracy of the finest architectures is as follows: 99.8 percent for VGG16 and 99.4 percent for InceptionResNetV2 on Corn Leaves. The results suggest that the models developed during the investigation phase to identify the leaf disease were superior to any existing Deep Neural Networks (DNNs).