Many species of insect pests can be detected and monitored automatically. Several systems have been designed in order to improve integrated pest management (IPM) in the context of precision agriculture. Automatic detection traps have been developed for many important pests. These techniques and new technologies are very promising for the early detection and monitoring of aggressive and quarantine pests. The aim of the present paper is to review the techniques and scientific state of the art of the use of sensors for automatic detection and monitoring of insect pests. The paper focuses on the methods for identification of pests based in infrared sensors, audio sensors and image-based classification, presenting the different systems available, examples of applications and recent developments, including machine learning and Internet of Things. Future trends of automatic traps and decision support systems are also discussed.

Feeding the growing global population requires an annual increase in food production. This requirement suggests an increase in the use of pesticides, which represents an unsustainable chemical load for the environment. To reduce pesticide input and preserve the environment while maintaining the necessary level of food production, the efficiency of relevant processes must be drastically improved. Within this context, this research strived to design, develop, test and assess a new generation of automatic and robotic systems for effective weed and pest control aimed at diminishing the use of agricultural chemical inputs, increasing crop quality and improving the health and safety of production operators. To achieve this overall objective, a fleet of heterogeneous ground and aerial robots was developed and equipped with innovative sensors, enhanced end-effectors and improved decision control algorithms to cover a large variety of agricultural situations. This article describes the scientific and technical objectives, challenges and outcomes achieved in three common crops.

Time-resolved reflectance spectroscopy can be used to assess nondestructively the bulk (rather than the superficial) optical properties of highly diffusive media. A fully automated system for time-resolved reflectance spectroscopy was used to evaluate the absorption and the transport scattering spectra of fruits in the red and the near-infrared regions. In particular, data were collected in the range 650–1000 nm from three varieties of apples and from peaches, kiwifruits, and tomatoes. The absorption spectra were usually dominated by the water peak near 970 nm, whereas chlorophyll was detected at 675 nm. For all species the scattering decreased progressively with increasing wavelength. A best fit to water and chlorophyll absorption line shapes and to Mie theory permitted the estimation of water and chlorophyll content and the average size of scattering centers in the bulk of intact fruits.

The aim of this research was to implement a methodology through the generation of a supervised classifier based on the Mahalanobis distance to characterize the grapevine canopy and assess leaf area and yield using RGB images. The method automatically processes sets of images, and calculates the areas (number of pixels) corresponding to seven different classes (Grapes, Wood, Background, and four classes of Leaf, of increasing leaf age). Each one is initialized by the user, who selects a set of representative pixels for every class in order to induce the clustering around them. The proposed methodology was evaluated with 70 grapevine (V. vinifera L. cv. Tempranillo) images, acquired in a commercial vineyard located in La Rioja (Spain), after several defoliation and de-fruiting events on 10 vines, with a conventional RGB camera and no artificial illumination. The segmentation results showed a performance of 92% for leaves and 98% for clusters, and allowed to assess the grapevine’s leaf area and yield with R² values of 0.81 (p < 0.001) and 0.73 (p = 0.002), respectively. This methodology, which operates with a simple image acquisition setup and guarantees the right number and kind of pixel classes, has shown to be suitable and robust enough to provide valuable information for vineyard management.

Fruit firmness measurement is a good way to monitor fruit softening and to predict bruising damage during harvest and postharvest handling. Ripening protocols traditionally utilize a destructive penetrometer-type fruit firmness measure to monitor ripening. Until recently, methods of assessing fruit texture properties nondestructively were not commercially available. The nondestructive Sinclair iQ™ firmness tester was investigated to monitor ripening and predict bruising susceptibility in stone fruit. This work was carried out on four peach, three plum, and five nectarine cultivars over two seasons. The correlations between destructive and nondestructive firmness measurements were significant ( p -value = 0.0001), although too low for commercial applications as they varied from r 2 = 0.60–0.71 according to fruit type. Using a different approach, the relationship between destructive and nondestructive firmness measures was characterized in terms of segregating these fruit according to their stages of ripening. This was done by using discriminant analysis (66–90% agreement in ripeness stage classification was observed in validation tests). Discriminant analysis consistently segregated nondestructive firmness measured fruit into commercially important classes (“ready to eat”, “ready to buy”, “mature and immature”). These represented key ripening stages with different bruising potentials and consumer acceptance. This work points out the importance to relate nondestructive measurements directly to important commercial physiological stages rather than to correlate them with the current standard penetrometer values. Thus, destructive and nondestructive firmness measurements can be directly used to identify the stage of ripeness and potential susceptibility to bruising during postharvest changes. Further work is recommended to evaluate the performance of this nondestructive sensor in segregating fruit according to their stage of ripeness under packinghouse or processing plant conditions. Keywords Impact firmness sensor Sinclair iQ™ firmness tester Discriminant analysis Critical bruising thresholds “Ready to eat” “Ready to buy” 1 Introduction Fruit firmness changes have been a reliable way to describe ripening changes and/or predict bruising damage ( Kader, 1992; Crisosto et al., 2001; Metheney et al., 2002 ). The loss of fruit firmness is a physiological process that occurs during fruit maturation/ripening on the tree, during cold storage and retail handling ( Delwiche, 1987; Chen, 1996; Abbott, 1999 ). In most of these previous studies, fruit firmness was measured destructively. When destructive measurements are used, the tendency is to use as few samples as possible which often results in increased lot to lot variability in the parameter measured. Thus, the sample variability became a factor to consider during laboratory studies and/or commercial applications involving destructive firmness measures. Currently, growers, handlers, and produce store managers still rely on traditional destructive penetrometers ( Magness and Taylor, 1925 ) to control their operations. The use of nondestructive firmness measurements, based on elastic tissue properties rather than tissue failure properties, is becoming available for several industries ( Chen and Tjan, 1998; Abbott, 1999; De Ketelaere et al., 2006 ). From a commercial point of view, several nondestructive firmness testing systems are being evaluated for packingline and laboratory measurements ( Aweta, 2004; Greefa, 2004; Sinclair, 2004 ). Other researchers have been comparing the Sinclair iQ™ firmness tester with acoustic methods and other firmness testers on apples, melons, avocados, nectarines and mangos ( De Belie et al., 2000; Shmulevich, 2003 ). The availability of new technology that allows automated nondestructive firmness measurements will allow greater sampling, reducing variability in firmness measurements of bulk lots, and will help to segregate fruit highly susceptible to bruising or fruit with a similar ripening stage from the sample. In addition, the reduction of fruit sample variability commonly associated with destructive measures will improve our research programs. The relationship between fruit firmness measurements at harvest and postharvest quality, based on fruit susceptibility to mechanical damage during handling and packaging, has been studied in apples ( Kunze et al., 1975 ) and peaches ( Crisosto et al., 2001 ). The prediction of potential fruit loss due to mechanical damage during harvesting/postharvest handling has been modeled for several commodities ( Chen, 1996; Barreiro et al., 1997 ). For the fresh fruit market, measuring firmness and pulp temperature during postharvest handling (production and retail sites) are key tools for controlling ripening. This provides useful information to manage marketing, storage and shipment decisions for several commodities ( Thompson and Crisosto, 2002 ). The establishment of ripening protocols will allow fruit handlers to meet consumer demands by delivering “ready to eat” fruit at its optimum firmness ( Bruhn, 1995 ). In addition to comparing nondestructive Sinclair iQ™ firmness tester measurements with the standard destructive penetrometer values, this work focused on the direct relationship between nondestructive measurements and relevant fruit texture changes during ripening. 2 Materials and methods 2.1 Fruit material Fruit from four peach ( Prunus persica (L.) Batsch), five nectarine ( P. persica (L.) Batsch), and three plum ( Prunus salicina L.) cultivars growing near the University of California, Kearney Agricultural Center (KAC), Parlier, CA were used for this study. All fruit was harvested when commercially mature during the 2002 and 2003 seasons. The peach cultivars tested were the yellow flesh ‘Flavorcrest’, ‘Summer Lady’ and...