Optical Sensing to Determine Tomato Plant Spacing for Precise Agrochemical Application: Two Scenarios.

The feasibility of automated individual crop plant care in vegetable crop fields has increased, resulting in improved efficiency and economic benefits. A systems-based approach is a key feature in the engineering design of mechanization that incorporates precision sensing techniques. The objective of this study was to design new sensing capabilities to measure crop plant spacing under different test conditions (California, USA and Andalucía, Spain). For this study, three different types of optical sensors were used: an optical light-beam sensor (880 nm), a Light Detection and Ranging (LiDAR) sensor (905 nm), and an RGB camera. Field trials were conducted on newly transplanted tomato plants, using an encoder as a local reference system. Test results achieved a 98% accuracy in detection using light-beam sensors while a 96% accuracy on plant detections was achieved in the best of replications using LiDAR. These results can contribute to the decision-making regarding the use of these sensors by machinery manufacturers. This could lead to an advance in the physical or chemical weed control on row crops, allowing significant reductions or even elimination of hand-weeding tasks.

Survey of Molds in California Processing Tomatoes.

From 2009 to 2011, freshly harvested processing tomatoes from California commercial fields were surveyed for mold species present in the mature fruit. Molds were recovered from the majority of fruit that had visual symptoms of black mold and other decays and from about a quarter of randomly sampled, asymptomatic fruit. Alternaria, Fusarium, and Geotrichum spp. were the most commonly recovered fungi in both symptomatic and random samples. Based on pairwise statistical analysis, the frequencies of 2 different fungal genera in a composite 11 kg-sample were, in general, statistically independent events, with the exception of a weak association between the incidence of Geotrichum with Alternaria, Cladosporium, or Stemphylium. The mold genera distribution data in this study provide the processing tomato industry with a valuable informational resource that can be used in the management of fungal infection in both the crop and in the final thermally processed finished product. Because of the relative abundance of these fungi, this survey supported the development of genera-specific immunochromatographic diagnostic assays to detect and quantify mold occurrence in Californian processing tomatoes as a potential alternative to the current subjective visual methods, which are characterized by imprecision and nonuniform species sensitivity. A simulation of 1 million 11 kg-composite samples based upon the distributional survey data projected that a multiantibody immunochromatographic assay using monoclonal antibodies for Alternaria, Cladosporium, Fusarium, and Geotrichum could successfully detect the presence of mold in 94% of moldy processing tomato samples collected randomly at harvest.

Structured Light-Based 3D Reconstruction System for Plants.

Camera-based 3D reconstruction of physical objects is one of the most popular computer vision trends in recent years. Many systems have been built to model different real-world subjects, but there is lack of a completely robust system for plants. This paper presents a full 3D reconstruction system that incorporates both hardware structures (including the proposed structured light system to enhance textures on object surfaces) and software algorithms (including the proposed 3D point cloud registration and plant feature measurement). This paper demonstrates the ability to produce 3D models of whole plants created from multiple pairs of stereo images taken at different viewing angles, without the need to destructively cut away any parts of a plant. The ability to accurately predict phenotyping features, such as the number of leaves, plant height, leaf size and internode distances, is also demonstrated. Experimental results show that, for plants having a range of leaf sizes and a distance between leaves appropriate for the hardware design, the algorithms successfully predict phenotyping features in the target crops, with a recall of 0.97 and a precision of 0.89 for leaf detection and less than a 13-mm error for plant size, leaf size and internode distance.

Automated Mobile System for Accurate Outdoor Tree Crop Enumeration Using an Uncalibrated Camera.

This paper demonstrates an automated computer vision system for outdoor tree crop enumeration in a seedling nursery. The complete system incorporates both hardware components (including an embedded microcontroller, an odometry encoder, and an uncalibrated digital color camera) and software algorithms (including microcontroller algorithms and the proposed algorithm for tree crop enumeration) required to obtain robust performance in a natural outdoor environment. The enumeration system uses a three-step image analysis process based upon: (1) an orthographic plant projection method integrating a perspective transform with automatic parameter estimation; (2) a plant counting method based on projection histograms; and (3) a double-counting avoidance method based on a homography transform. Experimental results demonstrate the ability to count large numbers of plants automatically with no human effort. Results show that, for tree seedlings having a height up to 40 cm and a within-row tree spacing of approximately 10 cm, the algorithms successfully estimated the number of plants with an average accuracy of 95.2% for trees within a single image and 98% for counting of the whole plant population in a large sequence of images.

Active optical sensors for tree stem detection and classification in nurseries.

Active optical sensing (LIDAR and light curtain transmission) devices mounted on a mobile platform can correctly detect, localize, and classify trees. To conduct an evaluation and comparison of the different sensors, an optical encoder wheel was used for vehicle odometry and provided a measurement of the linear displacement of the prototype vehicle along a row of tree seedlings as a reference for each recorded sensor measurement. The field trials were conducted in a juvenile tree nursery with one-year-old grafted almond trees at Sierra Gold Nurseries, Yuba City, CA, United States. Through these tests and subsequent data processing, each sensor was individually evaluated to characterize their reliability, as well as their advantages and disadvantages for the proposed task. Test results indicated that 95.7% and 99.48% of the trees were successfully detected with the LIDAR and light curtain sensors, respectively. LIDAR correctly classified, between alive or dead tree states at a 93.75% success rate compared to 94.16% for the light curtain sensor. These results can help system designers select the most reliable sensor for the accurate detection and localization of each tree in a nursery, which might allow labor-intensive tasks, such as weeding, to be automated without damaging crops.

Detection of the sour-rot pathogen Geotrichum candidum in tomato fruit and juice by using a highly specific monoclonal antibody-based ELISA.

Geotrichum candidum is a common soil-borne fungus that causes sour-rot of tomatoes, citrus fruits and vegetables, and is a major contaminant on tomato processing equipment. The aim of this work was to produce a monoclonal antibody and diagnostic assay for its detection in tomato fruit and juice. Using hybridoma technology, a cell line (FE10) was generated that produced a monoclonal antibody belonging to the immunoglobulin class M (IgM) that was specific to G. candidum and the closely related teleomorphic species Galactomyces geotrichum and anamorphic species Geotrichum europaeum and Geotrichum pseudocandidum in the G. geotrichum/G. candidum complex. The MAb did not cross-react with a wide range of unrelated fungi, including some likely to be encountered during crop production and processing. The MAb binds to an immunodominant high molecular mass (> 200 kDa) extracellular polysaccharide antigen that is present on the surface of arthroconidia and hyphae of G. candidum. The MAb was used in a highly specific enzyme-linked immunosorbent assay (ELISA) to accurately detect the fungus in infected tomato fruit and juice. Specificity of the ELISA was confirmed by sequencing of the internally transcribed spacer (ITS) 1-5.8S-ITS2 rRNA-encoding regions of fungi isolated from naturally-infected tomatoes.