Integrated analysis of machine learning and deep learning in chili pest and disease identification.

BACKGROUND: Chili is one of the most important and high-value vegetable crops worldwide. However, pest and disease infections are among the main limiting factors in chili cultivation. These diseases cannot be eradicated but can be handled and monitored to mitigate the damage. Hence, the use of an automated identification system based on images will promote quick identification of chili disease. The features extracted from the images are of utmost importance to develop such an accurate identification system. RESULTS: In this research, chili pest and disease features extracted using the traditional approach were compared with features extracted using a deep-learning-based approach. A total of 974 chili leaf images were collected, which consisted of five types of diseases, two types of pest infestations, and a healthy type. Six traditional feature-based approaches and six deep-learning feature-based approaches were used to extract significant pests and disease features from the chili leaf images. The extracted features were fed into three machine learning classifiers, namely a support vector machine (SVM), a random forest (RF), and an artificial neural network (ANN) for the identification task. The results showed that deep learning feature-based approaches performed better than the traditional feature-based approaches. The best accuracy of 92.10% was obtained with the SVM classifier. CONCLUSION: A deep-learning feature-based approach could capture the details and characteristics between different types of chili pests and diseases even though they possessed similar visual patterns and symptoms. © 2020 Society of Chemical Industry.

The association of nuclear abnormalities in exfoliated buccal epithelial cells with the health status of different agricultural activities farmers in Peninsular Malaysia.

BACKGROUND: Pesticide exposure possesses risk of genotoxicity to humans, particularly farmers. Despite accumulating evidences linking genotoxicity to pesticide exposure, epidemiological studies to address pesticide toxicity in occupationally exposed farmers in Malaysia remain underreported. Thus, this study was aimed to determine the presence of nuclear abnormalities through the assessment of micronucleus (MN) and binucleus (BNu) frequencies in exfoliated buccal epithelial cells from farmers who were exposed to pesticides. A cross-sectional study of farmers among different agricultural activities farmers in Bachok and Pasir Puteh, Kelantan, North East of Peninsular Malaysia was done to evaluate the presence of nuclear abnormalities and its correlation with their health status and farming activities. RESULTS: Analysis of buccal cells revealed that the frequency of MN was significantly higher (p < 0.05) in farmers as compared to controls. In contrast, no significant difference (p > 0.05) was observed for BNu frequency in between groups. Correlation analysis showed that apart from a significant (p < 0.05) and positive correlation between the duration of fertilizers exposure and frequencies of MN (r = 0.42, P = 0.001) and BNu (r = 0.37, P = 0.02), no other correlation of various confounding factors on the formation of MN and BNu were observed. CONCLUSION: In conclusion, pesticide and fertilizers exposure may contribute to the promotion of nuclear anomalies among Malaysian farmers who are engaged in mixed plantation activities. Further assessment of larger populations is important to address and overcome the potential risk of pesticide-induced genotoxicity.