The most important soil parameters for compressed surfaces are aeration, bulk density, and permeability. The experiment was managed in a split-plot design with a randomized complete block design with three replications. Results indicated that cone-penetrometer was used to measure the soil compaction of furrow bottom under without-compaction/control (C1), compaction with three passes of tractor wheel (C2), and compaction with six passes of tractor wheel (C3) treatments for the years 2017-18 and 2018-19. In the case of clay loam (S1) soil compacted under treatment C3, the compaction was maximum with mean values of 1.23 MPa followed by treatment C2 with 1.09 MPa and C1 with compaction of 0.55 MPa during the 2017-18 Rabi crop season. While, for silty clay loam (S2) soil compacted under treatment C3, the compaction was maximum with mean values of 1.12 MPa followed by treatment C2 with 0.80 MPa and C1 with compaction of 0.42 MPa during the 2017-18 Rabi crop season. Similarly, during the year 2018-19, for different dry densities under soil compaction treatments C1, C2, and C3, the soil penetration resistance values were 1.10 MPa, 0.81 MPa, and 0.40 MPa, respectively. The reduction in soil EC1:5 was determined in the without-compaction/control (C1) plots under both cropping years. However, substantial change in the soil EC1:5 was observed in compact treatment three tractor wheel passes (C2) and six tractor wheel passes (C3) plots. Generally, a slight rise in soil EC1:5 was noticed in both C2 and C3 plots over C1 treatment plots for both cropping seasons. Furthermore, a slight change in the soil pH was observed in compact treatment, three tractor wheel passes (C2) and six tractor wheel passes (C3) plots during both the years. Mostly, a slight rise in soil pH was noticed in both C2 and C3 plots over C1 treatment plots. It is also noticeable from the data that compacting the soil with a tractor using three-wheel and six-wheel passes displayed no difference in pH in bed furrow treatment plots. Our findings concluded that the effect of soil compactions on soil EC and pH values were formed slightly increased during both years as compared to without-compaction treatment. Nevertheless, soil EC values were increased with increasing soil depths under C2 and C3 over C1 treatment, while maximum EC values were recorded under furrow plots among all soil depths and soil different soil textures. Even though the soil pH was not affected by soil compaction treatments under all soil depths and soil textures during both years.
Plant diseases are a significant threat to global food security, impacting crop yields and economic growth. Accurate identification of plant diseases is crucial to minimize crop loses and optimize plant health. Traditionally, plant classification is performed manually, relying on the expertise of the classifier. However, recent advancements in deep learning techniques have enabled the creation of efficient crop classification systems using computer technology. In this context, this paper proposes an automatic plant identification process based on a synthetic neural network with the ability to detect images of plant leaves. The trained model EfficientNet-B3 was used to achieve a high success rate of 98.80% in identifying the corresponding combination of plant and disease. To make the system user-friendly, an Android application and website were developed, which allowed farmers and users to easily detect diseases from the leaves. In addition, the paper discusses the transfer method for studying various plant diseases, and images were captured using a drone or a smartphone camera. The ultimate goal is to create a user-friendly leaf disease product that can work with mobile and drone cameras. The proposed system provides a powerful tool for rapid and efficient plant disease identification, which can aid farmers of all levels of experience in making informed decisions about the use of chemical pesticides and optimizing plant health.
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