Detecting fungi-affected multi-crop disease on heterogeneous region dataset using modified ResNeXt approach.

Crop diseases pose significant threats to agriculture, impacting crop production. Biotic factors contribute to various diseases, including fungal, bacterial, and viral infections. Recent advancements in deep learning present a novel approach to the detection and recognition of these crop diseases. While considerable research has focused on identifying and recognizing crop diseases, fungal disease-affected crops have received relatively less attention and also detecting disease on different region datasets. This paper is about spotting fungal diseases in crops across different regions with diverse climates. It emphasizes the need for tailored detection methods, addressing the risk of mycotoxin production by fungi, which can harm both humans and animals. Detecting fungal diseases in apple, guava, and custard apple crops such as spot, scab, rust, rot, leaf spot, and insect ate. In the proposed work, the modified ResNeXt variant of the convolution neural network (CNN) technique was employed to predict 3 major crop classes of fungal disease. Initially, using Inception-v7 and ResNet for fungal disease in crops did not yield satisfactory results. A modified ResNeXt CNN model was proposed, showing improved fungal disease prediction. The novel model underwent a comparison with established methodologies. The suggested model draws upon a benchmark dataset consisting of 14,408 images capturing fungal diseases, categorized into three distinct classes: apple, custard apple, and guava. Experimental outcomes show that the proposed mutated ResNeXt model outperformed the state-of-the-art approaches. The model achieved 98.92% accuracy and high performance across recall, precision, and F1-score (above 99%) for the benchmark dataset, which gained encouragement and was comparable with the state-of-the-art approach.

Pharmacodynamic interaction of cumin seeds (Cuminum cyminum L.) with glyburide in diabetes.

Background and objective The plethora of anti-diabetic agents available today has many side effects, especially on chronic usage. Hence, alternative approaches utilizing natural and synthetic agents are sought after. Cumin has been shown to be beneficial in treating diabetes. This study evaluates the anti-diabetic effect of cumin and glyburide in the streptozotocin induced diabetes model in rats, and investigates their pharmacodynamic interactions and its implication in diabetes. Methodology The phytoconstituents present in the ethanolic cumin seed extract were determined using appropriate analytical methods. After acute toxicity studies (OECD 2001), the anti-diabetic effect of the extract was evaluated in wistar rats. The rats were divided into five groups - Groups I and II served as the normal and diabetic control. Group III was the standard control (glyburide 5 mg/kg), while groups IV and V received the extract (600 mg/kg) and a combination of the extract (600 mg/kg) and glyburide (2.5 mg/kg; half dose). Biochemical parameters viz. plasma glucose and glycosylated haemoglobin, were measured periodically during the 28 day treatment. On the 28th day, oral glucose tolerance test, lipid profile, renal profile and histopathological evaluation were performed after completion of the study. To investigate the nature of herb-drug interaction, HPLC analysis for estimation of glyburide concentration in the blood was conducted. Results Acute toxicity studies showed the extract to be safe till a dose of 2 g/kg. The extract alone, and in combination with glyburide (half-dose), significantly lowered elevated glucose (by more than 45% from baseline; without producing hypoglycemia), and other lipid and renal parameters. The effects produced by 2.5 mg/kg glyburide, and 5 mg/kg glyburide (without extract) were similar. Histopathological analysis also showed that the extract was able to reverse the degeneration brought about by streptozotocin which was especially notable on the pancreatic and renal tissue. HPLC analysis revealed differing pharmacokinetics of glyburide in the groups treated with 5 mg/kg dose, and 2.5 mg/kg + 600 mg/kg extract. Conclusion The results obtained in this study suggest that Cuminum cyminum L. is a promising anti-diabetic agent, and exhibits pharmacodynamic interaction with glyburide to mitigate symptoms of diabetes mellitus.