Abstract
There are many methods to detect plant pests and diseases, but they are primarily time-consuming and costly. Computer vision techniques can recognize the pest- and disease-damaged fruits and provide clues to identify and treat the diseases and pests in their early stages. This study aimed to identify common pests, including the apple capsid (Plesiocoris rugicollis)/AC, apple codling moth (Cydia pomonella)/ACM, Pear lace bug (Stephanitis pyri)/PLB, and one physiological disease-apple russeting/AR in two cultivars, Golden Delicious and Red Delicious, using the digital image processing and sparse coding method. The Sparse coding method is used to reduce the storage of the elements of images so that the matrix can be processed faster. There have been numerous studies on the identification of apple fruit diseases and pests. However, most of the previous studies focused only on diagnosing a pest or disease, not on computational volume reduction and rapid detection. This research focused on the comprehensive study on identifying pests and diseases of apple fruit using sparse coding. The sparse coding algorithm in this work was designed using Matlab software. The apple pest and disease detection were performed based on 11 characteristics: R, G, B, L, a, b, H, S, V, Sift, and Harris. The class detection accuracy using the sparse coding method was obtained for 10 classes with three views of apple for S. pyri of red apple as 81%, S. pyri of golden apple as 88%, golden apple russeting as 85%, S. pyri and russeting of red apple as 100%, S. pyri and russeting of golden apple as 80%, codling moth of red apple as 86%, codling moth of golden apple as 72%, S. pyri of red apple as 83%, S. pyri of golden apple as 90%, codling moth and S. pyri of red apple as 80%, and codling moth and S. pyri of golden apple as 67%. The total processing time for developing the dictionary was 220 s. Once the dictionary was developed, pest and disease detection took only 0.175 s. The results of this study can be useful in developing automatic devices for the early detection of common pests and diseases of apples. Although the study was focused on apple diseases, results for this work have huge potential for other crops.
Highlights
The early and smart detection of plant pests and diseases has many advantages in monitoring large fields and gardens
In order to calculate the accuracy of the proposed method, the clustering accuracy was first obtained using the perceptron artificial neural network with 10 neurons and a learning rate of 0.2 for each view of the apples
In some apples that had a particular feature and were only identified in one view, such as worminess, the accuracy of one view was very low, but in the second phase, two views were selected from each apple to be used in the training of the neural network
Summary
The early and smart detection of plant pests and diseases has many advantages in monitoring large fields and gardens. The necessary information about crop health and the early detection of pests and diseases can increase productivity through the appropriate management strategies, such as recommending the appropriate pesticides and fungicides and the quarantine regulations. Kubiak et al [1] examined the pests in an apple tree and reported that the number of pests varies from season to season throughout the year. Maintaining effective storage and identifying the pests and risk levels caused by different species are among the most critical tasks for protecting the apple tree. The biological structure and different quantitative and qualitative evaluations for crops have led to the development of non-destructive experiments in recent years, among which computer vision has a particular position [5].With the recent advancement of precision agriculture in spot treatment, this technology’s efficacy lies in the accuracy of detection of the area, where it needs intervention
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