AI-Driven Framework for Recognition of Guava Plant Diseases through Machine Learning from DSLR Camera Sensor Based High Resolution Imagery.

Plant diseases can cause a considerable reduction in the quality and number of agricultural products. Guava, well known to be the tropics' apple, is one significant fruit cultivated in tropical regions. It is attacked by 177 pathogens, including 167 fungal and others such as bacterial, algal, and nematodes. In addition, postharvest diseases may cause crucial production loss. Due to minor variations in various guava disease symptoms, an expert opinion is required for disease analysis. Improper diagnosis may cause economic losses to farmers' improper use of pesticides. Automatic detection of diseases in plants once they emerge on the plants' leaves and fruit is required to maintain high crop fields. In this paper, an artificial intelligence (AI) driven framework is presented to detect and classify the most common guava plant diseases. The proposed framework employs the ΔE color difference image segmentation to segregate the areas infected by the disease. Furthermore, color (RGB, HSV) histogram and textural (LBP) features are applied to extract rich, informative feature vectors. The combination of color and textural features are used to identify and attain similar outcomes compared to individual channels, while disease recognition is performed by employing advanced machine-learning classifiers (Fine KNN, Complex Tree, Boosted Tree, Bagged Tree, Cubic SVM). The proposed framework is evaluated on a high-resolution (18 MP) image dataset of guava leaves and fruit. The best recognition results were obtained by Bagged Tree classifier on a set of RGB, HSV, and LBP features (99% accuracy in recognizing four guava fruit diseases (Canker, Mummification, Dot, and Rust) against healthy fruit). The proposed framework may help the farmers to avoid possible production loss by taking early precautions.

Landscape crop composition effects on cotton yield, Lygus hesperus densities and pesticide use.

BACKGROUND: Landscape crop composition surrounding agricultural fields is known to affect the density of crop pests, but quantifying these effects, as well as measuring how they translate to changes in yield, is difficult. Using a large dataset consisting of 1498 records of commercial cotton production in California between 1997 and 2008, we explored the relationship between landscape composition and cotton yield, the density of Lygus hesperus (a key cotton pest) at field-level and within-field spatial scales and pesticide use. RESULTS: We found that the crop composition immediately adjacent to a cotton field was associated with substantial differences in cotton yield, L. hesperus density and pesticide use. Furthermore, crops that tended to be associated with increased L. hesperus density also tended to be associated with increased pesticide use and decreased cotton yield. CONCLUSION: Our results suggest a possible mechanism by which landscape composition can affect cotton yield: by increasing the density of pests which in turn damage cotton plants. Our quantification of how surrounding crops affect pest densities, and in turn yield, in cotton fields has significant impacts for cotton farmers, who can use this information to help optimize crop selection and ranch layout. © 2016 Society of Chemical Industry.