An accurate and reliable image based fruit detection system is critical for supporting higher level agriculture tasks such as yield mapping and robotic harvesting. This paper presents the use of a state-of-the-art object detection framework, Faster R-CNN, in the context of fruit detection in orchards, including mangoes, almonds and apples. Ablation studies are presented to better understand the practical deployment of the detection network, including how much training data is required to capture variability in the dataset. Data augmentation techniques are shown to yield significant performance gains, resulting in a greater than two-fold reduction in the number of training images required. In contrast, transferring knowledge between orchards contributed to negligible performance gain over initialising the Deep Convolutional Neural Network directly from ImageNet features. Finally, to operate over orchard data containing between 100-1000 fruit per image, a tiling approach is introduced for the Faster R-CNN framework. The study has resulted in the best yet detection performance for these orchards relative to previous works, with an F1-score of >0.9 achieved for apples and mangoes.
Analysis of local colours and textures has been used for pixel-wise mango classification, followed by blob extraction to identify individual mangoes @cite_19 . Avoiding the use of hand-engineered features, Convolutional Neural Networks (CNNs) have been used for pixel-wise apple classification @cite_0 , followed by Watershed Segmentation (WS) to identify individual apples. The radial symmetries in the specular reflection in berries have been shown to be useful for RoI extraction @cite_3 , where a KD-forest was used for berry classification. To detect citrus fruit, Circular Hough Transforms (CHT) have been used to extract key-points, which were then classified using a Support Vector Machine (SVM) @cite_10 .
@cite_0 @cite_19 @cite_10 @cite_3
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This paper extends a previous study on the use of image analysis to automatically estimate mango crop yield (fruit on tree) (, 2013). Images were acquired at night, using artificial lighting of fruit at an earlier stage of maturation ('stone hardening' stage) than for the previous study. Multiple image sets were collected during the 2011 and 2012 seasons. Despite altering the settings of the filters in the algorithm presented in the previous study (based on colour segmentation using RGB and YCbCr, and texture), the less mature fruit were poorly identified, due to a lower extent of red colouration of the skin. The algorithm was altered to reduce its dependence on colour features and to increase its use of texture filtering, hessian filtering in particular, to remove leaves, trunk and stems. Results on a calibration set of images (2011) were significantly improved, with 78.3 of fruit detected, an error rate of 10.6 and an R^2 value (machine vision to manual count) of 0.63. Further application of the approach on validation sets from 2011 and 2012 had mixed results, with issues related to variation in foliage characteristics between sets. It is proposed the detection approaches within both of these algorithms be used as a 'toolkit' for a mango detection system, within an expert system that also uses user input to improve the accuracy of the system. Yield mapping for tree crops by mechanical harvesting requires automatic detection and counting of fruits in tree canopy. However, partial occlusion, shape irregularity, varying illumination, multiple sizes and similarity with the background make fruit identification a very difficult task to achieve. Therefore, immature green citrus-fruit detection within a green canopy is a challenging task due to all the above-mentioned problems. A novel algorithmic technique was used to detect immature green citrus fruit in tree canopy under natural outdoor conditions. Shape analysis and texture classification were two integral parts of the algorithm. Shape analysis was conducted to detect as many fruits as possible. Texture classification by a support vector machine (SVM), Canny edge detection combined with a graph-based connected component algorithm and Hough line detection, were used to remove false positives. Next, keypoints were detected using a scale invariant feature transform (SIFT) algorithm and to further remove false positives. A majority voting scheme was implemented to make the algorithm more robust. The algorithm was able to accurately detect and count 80.4 of citrus fruit in a validation set of images acquired from a citrus grove under natural outdoor conditions. The algorithm could be further improved to provide growers early yield estimation so that growers can manage grove more efficiently on a site-specific basis to increase yield and profit. We present a vision system that automatically predicts yield in vineyards accurately and with high resolution. Yield estimation traditionally requires tedious hand measurement, which is destructive, sparse in sampling, and inaccurate. Our method is efficient, high-resolution, and it is the first such system evaluated in realistic experimentation over several years and hundreds of vines spread over several acres of different vineyards. Other existing research is limited to small test sets of 10 vines or less, or just isolated grape clusters, with tightly controlled image acquisition and with artificially induced yield distributions. The system incorporates cameras and illumination mounted on a vehicle driving through the vineyard. We process images by exploiting the three prominent visual cues of texture, color, and shape into a strong classifier that detects berries even when they are of similar color to the vine leaves. We introduce methods to maximize the spatial and the overall accuracy of the yield estimates by optimizing the relationship between image measurements and yield. Our experimentation is conducted over four growing seasons in several wine and table-grape vineyards. These are the first such results from experimentation that is sufficiently sized for fair evaluation against true yield variation and real-world imaging conditions from a moving vehicle. Analysis of the results demonstrates yield estimates that capture up to 75 of spatial yield variance and with an average error between 3 and 11 of total yield.