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The principal objective of agriculture is the production of a high yield of healthy crops. This yield may be improved by the automatic detection of diseases and the consequent reduction in the use of pesticides. A digital processing system for images was thus developed and used to identify lesions on the leaves of cotton plants. A collection of 60,659 images of sub-metric resolution showing samples of soil and both healthy and damaged leaves was obtained and processed with an algorithm for the extraction of texture from 102x102-pixel samples. Then they analyzed with a neuro-fuzzy classifier trained to discriminate the three types of regions (soil, healthy leaf, and lesioned leaf). The algorithm developed was able to recognize the three classes. It generated a great amount of information on recognition of background which was more consistent than leaf damage areas. Therefore, it surpassed the performance of areas of healthy leaves. A similar trend was found for sensitivity. The overall accuracy of the system was 71.2%, suggesting that the unbalanced data of the different classes had skewed the results of the algorithm, as the number of false positives for the less well represented classes was greater. The analysis of unbalance (F-Score) showed that, independent of the volume of data, the attributes of texture utilized yielded better results for the images containing areas of damage in relation to overall accuracy. Therefore, given the challenges involved in the automatic identification of lesions in agricultural crops, such as variations in illumination, color, and texture, as well as obstruction, overlapping, and complexity of the region of which the image was taken, the behavior of the model was deemed satisfactory. Given the hybrid nature of the model, it should contribute to the state of the art in the use of intelligent systems in agriculture. This algorithm is available at https://github.com/rafaeufg/Cotton-diseases

Chlorophyll content is a widely used parameter for nutritional status diagnosis in sugarcane. This study aimed to develop a predictive model of chlorophyll content in sugarcane seedlings using spectral imagery analysis within the electromagnetic spectrum visible range. The experiment was carried out in a split-plot design, with two fertilization rates and three sugarcane cultivars. For chlorophyll analysis, 144 leaves were collected from seedlings. Chlorophyll contents were extracted and measured by SPAD-502 meter. Spectral images within the range of 480 to 710 nm were analyzed using reflectance, absorbance (white source), and fluorescence (source at 405 and 470 nm) responses. Predictive models were developed using multivariate regression methods such as Principal Component Regression and Partial Least Squares Regression. We chose the best model through absorbance response using variable selection and the PLSR method (R2P = 0.718 and RMSEP = 7.665). The wavelengths of 480, 490, 500, 600, 630, and 640 nm were identified as the best for total chlorophyll content determination. The spectral image processing-based method can provide a chlorophyll measurement equivalent to SPAD, with the advantage of having a higher spatial coverage over the entire leaf area. Besides, it can also support automation of the chlorophyll measurement in greenhouses.

Background The San Francisco Valley region from Brazil is known worldwide for its fruit production and exportation, especially grapes and wines. The grapes have high quality not only due to the excellent morphological characteristics, but also to the pleasant taste of their fruits. Such features are obtained because of the climatic conditions present in the region. In addition to the favorable climate for grape cultivation, harvesting at the right time interferes with fruit properties. Results This work aims to define grape maturation stage of Syrah and Cabernet Sauvignon cultivars with the aid of deep-learning models. The idea of working with these algorithms came from the fact that the techniques commonly used to find the ideal harvesting point are invasive, expensive, and take a long time to get their results. In this work, convolutional neural networks were used in an image classification system, in which grape images were acquired, preprocessed, and classified based on their maturation stage. Images were acquired with varying illuminants that were considered as parameters of the classification models, as well as the different post-harvesting weeks. The best models achieved maturation classification accuracy of 93.41% and 72.66% for Syrah and Cabernet Sauvignon respectively. Conclusions It was possible to correctly classify wine grapes using computational intelligent algorithms with high accuracy, regarding the harvesting time, corroborating chemometric results. © 2020 Society of Chemical Industry.

Weeds interfere in agricultural production, causing a reduction in crop yields and quality. The identification of weed species and the level of infestation is very important for the definition of appropriate management strategies. This is especially true for sugarcane, which is widely produced around the world. The present study has sought to develop and evaluate the performance of the Bag-of-Features (BoF) approach for use as a tool to aid decision-making in weed management in sugarcane production. The support vector machine to build a mathematical model of rank consisted of 30553 25x25-pixel images. Statistical analysis demonstrated the efficacy of the proposed method in the identification and classification of crops and weeds, with an accuracy of 71.6% and a Kappa index of 0.43. Moreover, even under conditions of high weed density and large numbers of overlapping and/or occluded leaves, weeds could be distinguished from crops This study clearly shows that the system can provide important subsidies for the formulation of strategies for weed management, especially in sugarcane, for which the timing of weed control is crucial.

This work presents a remote sensing solution for sugarcane precision agriculture based on a drone-borne differential interferometric synthetic aperture radar (DlnSAR) operating in the P-, L-, and C-bands. With one flight pass, the system can estimate the soil moisture, the plantation height, and the above-ground biomass map; and predict the harvest date and the respective productivity. With two flight passes, it assesses the crop growth via differential interferometry. A new methodology dedicated to sugarcane plantation was developed based on the existing methodologies for soil moisture and biomass measurement. The image information from the three bands, plus the C-band InSAR and P-band DInSAR information, show immense potential for efficient and low-cost monitoring. The results validated the methodology in a large sugarcane mill.

This paper presents a high-accuracy single-pass drone-borne Interferometric Synthetic Aperture Radar System in the P-band (P-InSAR) for forest inventory, where ground and canopy heights are accurately determined. Full penetration is proven for a eucalyptus forest with a tree spacing of 2.5 m by 3.0 m, and the measured digital terrain accuracy is compared with well-known statistical models. Combining a C-band single-pass InSAR with P-InSAR, forest height is estimated with 5 % accuracy for forest inventory. Both surface and digital ground models are presented and compared with ground truth measurements.

Accurate, high-resolution maps of for crop growth monitoring are strongly needed by precision agriculture. The information source for such maps has been supplied by satellite-borne radars and optical sensors, and airborne and drone-borne optical sensors. This article presents a novel methodology for obtaining growth deficit maps with an accuracy down to 5 cm and a spatial resolution of 1 m, using differential synthetic aperture radar interferometry (DInSAR). Results are presented with measurements of a drone-borne DInSAR operating in three bands—P, L and C. The decorrelation time of L-band for coffee, sugar cane and corn, and the feasibility for growth deficit maps generation are discussed. A model is presented for evaluating the growth deficit of a corn crop in L-band, starting with 50 cm height. This work shows that the drone-borne DInSAR has potential as a complementary tool for precision agriculture.

The use of deep learning models to identify lesions on cotton leaves on the basis of images of the crop in the field is proposed in this article. Cultivated in most of the world, cotton is one of the economically most important agricultural crops. Its cultivation in tropical regions has made it the target of a wide spectrum of agricultural pests and diseases, and efficient solutions are required. Moreover, the symptoms of the main pests and diseases cannot be differentiated in the initial stages, and the correct identification of a lesion can be difficult for the producer. To help resolve the problem, the present research provides a solution based on deep learning in the screening of cotton leaves which makes it possible to monitor the health of the cotton crop and make better decisions for its management. With the learning models GoogleNet and Resnet50 using convolutional neural networks, a precision of 86.6% and 89.2%, respectively, was obtained. Compared with traditional approaches for the processing of images such as support vector machines (SVM), Closest k-neighbors (KNN), artificial neural networks (ANN) and neuro-fuzzy (NFC), the convolutional neural networks proved to be up to 25% more precise, suggesting that this method can contribute to a more rapid and reliable inspection of the plants growing in the field.

Synthetic Aperture Radar (SAR) has become a powerful remote sensing tool during the last 25 years. Most relevant products are three dimensional and projected on a cartographic system: topographic, thematic and change-detection maps. Starting with the requirements of precision agriculture, subsurface tomography, subsidence and cartography a low weight drone-borne SAR was designed. It operates in P, L and C-bands with cross-track (InSAR) and differential Interferometry (DInSAR). The requirements of the foreseen applications, concept, design, results and validation from regular surveys and ground truths are presented.

The challenges of modern agriculture have led to the development of localized management tools which allow the rationalization of the use and application of pesticides, a reduction in production costs and the optimization of agricultural processes. This study was carried out to develop an algorithm capable of orienting weed control in the management of a corn crop, using digital image analysis to identify the level of weed infestation in the field. The seeds of six species of weed were sown in an experimental plot of corn, and daily images were captured for 40 days for the evaluation of the level of weed infestation (low, intermediate or high). The algorithm developed was able to target information about the plants and soil accurately and discriminate the residual information as referring to either the culture or weeds. The proposed algorithm has achieved 90% accuracy in identifying the level of infestation from images already evaluated by experts. The results can thus be used as part of weed control strategy, with the incorporation of the geographic coordinates of the image making possible the construction of a map of the level of weed infestation in the different areas where the crop is growing.

Differential synthetic aperture radar interferometry (DInSAR) has been widely applied since the pioneering space-borne experiment in 1989, and subsequently with the launch of the ERS-1 program in 1992. The DInSAR technique is well assessed in the case of space-borne SAR data, whereas in the case of data acquired from aerial platforms, such as airplanes, helicopters, and drones, the effective application of this technique is still a challenging task, mainly due to the limited accuracy of the information provided by the navigation systems mounted onboard the platforms. The first airborne DInSAR results for measuring ground displacement appeared in 2003 using L- and X-bands. DInSAR displacement results with long correlation time in P-band were published in 2011. This letter presents a SAR system and, to the best of our knowledge, the first accuracy assessment of the DInSAR technique using a drone-borne SAR in L-band. A deformation map is shown, and the accuracy and resolution of the methodology are presented and discussed. In particular, we have obtained an accuracy better than 1 cm for the measurement of the observed ground displacement. It is in the same order as that achieved with space-borne systems in C- and X-bands and the airborne systems in X-band. However, compared to these systems, we use here a much longer wavelength. Moreover, compared to the satellite experiments available in the literature and aimed at assessing the accuracy of the DInSAR technique, we use only two flight tracks with low time decorrelation effects and not a big data stack, which helps in reducing the atmospheric effects.

The partial dehydration of grapes after harvest aimed at winemaking has been shown to be a process that brings increased concentration of sugar and phenolic compounds in the must, which affects the quality of the wines produced. However, the works developed so far have studied the process for temperatures up to a maximum of 25°C and air velocity lower than 1 m.s-1. This study aimed to analyze the physical-chemical changes concentration of total soluble solids (TSS) and phenolic compounds (PC) after partial dehydration of 'Syrah' grapes subjected to two treatments combining two temperatures and one air velocity (T1= 22.9°C/1.79 m.s-1 and T2 = 37.1°C/1.79 m.s-1) and relative humidity of 40%. The water loss of the grapes was approximately 14% and the drying process lasted between 34 and 68 h for treatments T2 and T1, respectively. We experimentally and statistically verified that the treatments promoted significant increase in TSS and PC; however, for PC at the temperature of 37.1°C, the increase accounted for approximately 27%, whereas, for the temperature of 22.9°C, it was only 12%. For TSS, the increase was on average 12.47±0.9% between both treatments. The results demonstrate that it is possible to moderately dry grapes, which consequently results in improvements in their chemical composition and can improve the quality of wine. Key words: Wine, polyphenols, soluble solids, drying, instrumentation.

The spectral images used as an alternative to the quality nondestructive evaluation, can contribute to improve monitoring and control of variables involved in partial dehydrated grapes for production of juice and / or wine, with the product quality as goal. This study aimed to differentiate Benitaka variety grapes with different concentrations of soluble solids using spectral image of fluorescence. The grape samples come from to Vale do Sao Francisco-State of Bahia, and were forwarded to the Laboratory of Atomic Interactions-Institute of Physics of Sao Carlos, University of Sao Paulo. The treatments consisted by two (T1 and T2) being T1-fresh grapes (12.54 +/- 0.09 °Brix), and T2-partial dehydrated grapes (in forced air oven at T=65°C and RH=50%) (13.32 +/- 0.08°Brix). The fluorescence image system used consists in a scientific CCD camera, lens system, a variable optical filter, and illumination system with LEDs (UV-405 nm). The wavelengths spectrum used in this study were between 480 to 750 nm. The spectral calibration occurred through a principal component analysis (PCA) in Matlab, wherein principal components which represent most the data were selected, separating T1 e T2. To select the best wavelength was applied a linear regression forward a PCA model to image data. After the PCA implementation was possible to distinguish the different soluble solids values in grapes to T1 and T2, setting the best wavelengths were to 480; 493; 552; 568; 605; 645; 666; 690 and 715 nm. The applied technique can contribute to technological advancement in the non-destructive evaluation field and real-time in high added value products such as derivatives Viticulture.

The grapes dehydration to make wine, in controlled environment and with variance in temperature and humidity, began to be studied with greater emphasis recently for some grape varieties, due to the proven effects on the higher concentration of phenolic compounds and the positive change in aroma. This chapter focuses on process control in with particular emphasis in instrumentation, applied technologies and main quality results.

The sugarcane is one of the most important crops in Brazil, the world´s largest sugar producer and the second largest ethanol producer. The presence of weeds in the sugarcane plantation can cause losses up to 90% of the production, caused by the competition for light, water and nutrients, between the crop and the weeds. Usually sugarcane plantations occupy large fields, and due to this, the weeds control is mostly chemical, which is more practical and cheaper than mechanical control. In the chemical control, the dosage and type of herbicides has been calculated by sampling, which causes problems of waste and misapplication of herbicides, since the degree of infestation may be variant from one location to another, as well as the species presents in the plantation. In order to avoid unnecessary waste in the herbicides application, there are some studies about weed identification using images taken from satellites, solution that have proved to have the advantage of covering the whole plantation, solving the problems of sample surveying, nevertheless, this method its dependent of a high weed density to ensure a good pattern recognition and its affected by the influence of clouds in the imagery quality. This work proposes a system for weed identification based on pattern recognition in imagery taken from a Remotely Piloted Aircraft (RPA). The RPA is able to fly at low altitude, so it is possible to take images closer to the plants and make the weed identification even in low infestation levels. In an initial evaluation, the system reached an overall accuracy of 83.1% and kappa coefficient of 0.775, using k-Nearest Neighbors (kNN) classifier.

In slaughterhouses, the operations that occur are heavily dependent on electricity . This study aimed to diagnose, analyze and propose solutions that lead to the rational use of electricity, without interfering, or maintain, the quality of the final product on the production lines of a broiler slaughterhouse. The work was carried through in a slaughterhouse of broilers, located in Sao Paulo, in the period of 2004 to 2008 . It was found that the compressors responsible for maintaining cold the refrigerated environment showed the highest consumption of electricity in the fridge, representing 97% of total consumption . It was observed that in none of the evaluated electric motors the power factor reaches the value recommended by ANEEL, and that possibly the energy efficiency indicators can be optimized . It was concluded that there are failures in electric energy use, demonstrating the need to implement an action plan that aims the conservation and rational use of energy and consequent reduction in production cost of chicken meat. Keywords: Electrical Energy Consumption, Indicators, Rational Use, Slaughterhouse. DIAGNOSIS OF THE USE OF ELECTRIC ENERGY IN A CHICKEN SLAUGHTERHOUSE RESUMO: Nos matadouros , as operacoes que ocorrem sao altamente dependentes da eletricidade. Este estudo teve como objetivo diagnosticar, a nalisar e propor solucoes que levem ao uso racional de energia eletrica, sem interferir, e manter, a qualidade do produto final nas linhas de producao de frangos de corte matadouro. O trabalho foi realizado em um matadouro de frangos de corte , localizada em Sao Paulo, no periodo de 2004 a 2008. Verificou-se que os compressores responsaveis pela manutencao da frio do ambiente refrigerado apresentaram o maior consumo de electricidade no frigorifico , o que representa 97 % do consumo total. Foi observado que em nenhum dos motores eletricos avaliados o fator de potencia atinge o valor recomendado pela ANEEL, e que , possivelmente, os indicadores de eficiencia energetica pode ser otimizado. Concluiu-se que existem falhas no uso da energia eletrica , demonstrando a necessidade de implementar um plano de acao que visa a conservacao e uso racional de energia e consequente reducao do custo de producao de carne de frango . Palavras-chave: Consumo de Energia Eletrica , Indicadores, Uso Racional , Matadouro .