Leaf diseases have a detrimental effect on crop production quality in agriculture. In order to improve agricultural sector output, this has led to a greater emphasis on automating the detection of leaf diseases. In recent years, the process of categorising plant leaves using characteristics and machine learning has advanced. Usually, machine learning is used for supervised training of leaf classifiers using a set of data. Plant illnesses negatively impact both the amount and quality of agricultural goods by causing substantial growth and financial losses. Detecting plant diseases in big agricultural fields within a day has emerged as a critical topic of study. Three processes are used in this study to forecast pepper plant diseases (PPD): picture capture, feature selection, and image classification. The Kalman filter is used to remove noise from images before processing. Feature selection becomes important because it effectively solves the issue by eliminating redundant and unnecessary data, cutting down on computation time, improving learning accuracy, and improving comprehension of the model or data. For feature selection, Tom and Jerry optimisation (TJO) is used, and for final classification, the Modified Swin Transform method (MSTNet) is used. Using TJO, MSTNet's hyperparameters are adjusted to ascertain if a leaf is contaminated. On the Plant Village Dataset, experiments are carried out with different parameter measurements. The suggested MSTNet outperforms the accuracy rates of the current models with a 99.2% classification accuracy.
TopIntroduction
As members of the Capsicum genus, pepper plants (PP) are vulnerable to several illnesses that may negatively impact their development and productivity (Mathew, M. P., & Mahesh, T. Y. 2022). Bacterial spot, brought on by the bacterium Xanthomonas campestris, is one prevalent ailment. This disease appears on leaves as tiny, black lesions that seem to be drenched in water and are often encircled by a yellow halo (Bhagat, M., et al., 2020). These patches may combine as the illness spreads, resulting in defoliation and a significant decrease in the amount and quality of fruit. Since contaminated seeds, soil, and water may transmit the highly infectious bacterial spot, early diagnosis and prevention are essential for efficient management (Karadağ, K et al., 2020). Disease-resistant pepper varietals, crop rotation, and routine treatments of fungicides based on copper are among the suggested methods for managing bacterial spots in PPs.
Powdery mildew, a fungal disease caused by many species of the Erysiphales family, is another common disease that affects PPs (Jasim, M. A., & Al-Tuwaijari, J. M., 2020). Usually, powdery mildew manifests as a white, powdery material on the top parts of leaves, stems, and even fruit. Plant tissues that are impacted may undergo distortion, and early defoliation may transpire in extreme situations. In contrast to many other fungal infections, powdery mildew grows best in arid environments with high humidity, which makes it especially difficult to treat in certain regions (Baswaraju S, et al., 2023). Powdery mildew may be reduced by using cultural practices such as maintaining sufficient distance between plants, allowing for enough ventilation, and avoiding overhead watering. Fungicidal treatments may also be used to stop the disease's progress and safeguard the health of PPs, particularly in the early stages of illness (Eser, S. E. R. T. 2021).
Pepper leaf infections may result in significant output losses if left untreated, which makes early diagnosis of these diseases crucial (Jana, S et al., 2020). In extreme circumstances, fungal infections like anthracnose and bacterial illnesses like bacterial spots may lead to defoliation, lower fruit quality, and even crop collapse (Lin, T. L et al., 2020). PPs are susceptible to viral infections like cucumber mosaic virus, which may cause stunted growth and deformed fruit development. Beyond the immediate loss of the pepper crop, these illnesses impact farmers' livelihoods and the overall stability of the agricultural sector (Appalanaidu, M. V., & Kumaravelan, G. 2021).
Farmers or other agricultural specialists may often visually evaluate PPs as part of traditional techniques for detecting illness (Alguliyev, R., et al., 2020). However, These techniques have some drawbacks as they depend on the human eye to detect minute signs, and illnesses can only be detected once they have reached more advanced phases (Zilvan, V et al., 2020). Furthermore, large-scale agricultural operations make manual inspection difficult since early discovery is essential to prevent widespread contamination. Therefore, effective and precise automated systems are necessary to identify pepper leaf disease (Guan, X. 2021).