Abstract
Strawberry cropping system relies heavily on proper disease management to maintain high crop yield. Powdery mildew, caused by Sphaerotheca macularis (Wall. Ex Fries) is one of the major leaf diseases in strawberry which can cause significant yield losses up to 70%. Field scouts manually walk beside strawberry fields and visually observe the plants to monitor for powdery mildew disease infection each week during summer months which is a laborious and time-consuming endeavor. The objective of this research was to increase the efficiency of field scouting by automatically detecting powdery mildew disease in strawberry fields by using a real-time machine vision system. A global positioning system, two cameras, a custom image processing program, and a ruggedized laptop computer were utilized for development of the disease detection system. The custom image processing program was developed using color co-occurrence matrix-based texture analysis along with artificial neural network technique to process and classify continuously acquired image data simultaneously. Three commercial strawberry field sites in central Nova Scotia were used to evaluate the performance of the developed system. A total of 36 strawberry rows (~1.06 ha) were tested within three fields and powdery mildew detected points were measured manually followed by automatic detection system. The manually detected points were compared with automatically detected points to ensure the accuracy of the developed system. Results of regression and scatter plots revealed that the system was able to detect disease having mean absolute error values of 4.00, 3.42, and 2.83 per row and root mean square error values of 4.12, 3.71, and 3.00 per row in field site-I, field site-II, and field site-III, respectively. The slight deviation in performance was likely caused by high wind speeds (>8 km h−1), leaf overlapping, leaf angle, and presence of spider mite disease during field testing.
Highlights
Powdery mildew (Sphaerotheca macularis) is a serious disease affecting strawberry production in both warm and dry climates [1] and reduces crop yields by causing decreased fruit set, inadequate ripening, poor flavor, fruit cracking and deformation, and reducing postharvest storage time [2].Powdery mildew (PM) can be remarkably problematic when strawberry plants are grown in greenhouses or polythene tunnels, both of which are conducive to severe outbreaks of PM [3]
Laboratory evaluation suggested that the artificial neural leaves network (ANN) classifier performed better with healthy and other diseases image classification resulting in fewer numbers of misclassifications, whereas a comparatively higher misclassification rate was obtained with powdery mildew images
Acquired images were converted to green ratio, hue, saturation, and intensity images before generating co-occurrence matrix (CCM) from each
Summary
Powdery mildew (Sphaerotheca macularis) is a serious disease affecting strawberry production in both warm and dry climates [1] and reduces crop yields by causing decreased fruit set, inadequate ripening, poor flavor, fruit cracking and deformation, and reducing postharvest storage time [2]. Powdery mildew (PM) can be remarkably problematic when strawberry plants are grown in greenhouses or polythene tunnels, both of which are conducive to severe outbreaks of PM [3]. The PM disease symptoms primarily appear on young leaves [5], scatter through petioles, runners, flowers, and fruits except roots [6]. When the PM disease becomes severe, the fruit may crack, causing exposure to secondary infections [7] and causing yield losses of up to 70% [8]. According to Zhang et al [9], the traditional visual inspection of diseases is a time-consuming and labor-intensive approach and is quite impractical on large farms
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