## 1 Introduction  
Image processing and machine vision have been widely used in monitoring plants, harvesting, and other stages of plant growing. For example, [PERSON] et al. [1] presented a review about the use of machine vision systems in identifying common invertebrates on crops, such as butterflies, locusts, snails and slugs. Image processing is usually combined with artificial intelligence like neural networks to detect mature fruits [2; 3; 4; 5]. In these cases, the accuracy ranges between 60% and 100% depending on the type of fruit and other conditions. Crop monitoring is another domain where machine vision has been adopted (e.g., for production monitoring [6], the detection of diseases, or insect invasion) [7]. Unmanned aerial vehicles (UAVs) have been recently employed for the monitoring of large rural areas by analyzing images captured from a high altitude. [PERSON] et al. [8] discussed several UAV approaches based on red-green-blue (RGB), near infrared camera and other sensors. [PERSON] et al. [9] used a multispectral image and thermal view from an UAV to detect if opium poppy was affected by downy mildew. Common image segmentation techniques and classification methods like neural networks, decision trees, support vector machines (SVM), etc., have been used in both the diagnosis of plant and human diseases (e.g., skin disorders) [10]. The early detection of plant diseases is necessary for effective control reassuring that the financial cost and the environmental impact of their treatment will be minimized. If plant diseases are not treated in their early stages, the production cost can be significantly increased since the disease can propagate to the whole crop. The traditional way is to hire professional agricultrists who monitor the plants, but this may not always be possible for farms set in isolated rural areas. Moreover, the cost may not be affordable for smaller farmers. Remote monitoring and automation offered by precision agriculture solutions can reduce the cost and offer efficient protection. The initial detection of a disease can be based on machine vision and image processing that will generate an alert if its symptoms are recognized. Molecular analyses have a higher cost but may be carried out later if a plant disease has to be formally confirmed. The plant disease diagnosis can be based on various symptoms as described in [11; 12]. Symptoms can often be grouped as: (a) underdevelopment of tissues or organs (short internodes, underdeveloped roots, malformed leaves, lack of chlorophyll, fruits and flowers that do not develop), (b) overdevelopment of plant parts like tissues or organs, (c) necrosis of plant parts (wilts, shoot or leaf blights, leaf spots, fruit rots) and (d) alternations like mosaic patterns and altered coloration in leaves and flowers. The progression of the disease symptoms can vary significantly. Biotic agents affect the speed of the symptom progression. There are primary and secondary symptoms of a disease. For example, a primary symptom can be the root decay of a tree while the secondary symptom can be the tree toppling over. Secondary invaders that attack the tree in the later stages of a disease may obscure the original disease symptoms making the diagnosis more complicated. Other facts like improper herbicide usage can cause similar symptoms to spots that are present as a result of an infectious agent. Nevertheless, the symptoms caused by herbicide injury appear suddenly and no progression is observed. The spots may also follow spray patterns of the herbicide. Herbicides can also cause leaf distortion which may be confused with viral diseases. However, the new leaves are free of symptoms, indicating a lack of symptom progression. More than one problem or pathogen infecting the plant can be present. In this case, the symptoms may significantly differ from that of the individual pathogens when they act alone. Several image processing techniques have been presented in the literature for plant disease recognition.