ARTIFICIAL INTELLIGENCE (AI) AS SUSTAINABLE SOLUTION FOR THE AGRICULTURE SECTOR: FINDINGS FROM DEVELOPING ECONOMIES

Agricultural production plays an important role both in national and global economies. The efficient and safe methods of sustainable agricultural production is crucial, and the use of information technology is imperative to meet this end. Among the available information technology tools, this study highlights the IT based cognitive solutions supported with the artificial intelligence (AI) algorithm for sustainable solutions in the agriculture sector of developing economies. For this purpose, a systematic review of 87 papers has been conducted in the chosen last 20 years from 2000 to 2019 to identify the major trends, challenges, limitation related to the applicability of AI supported cognitive solutions in the agricultural industry of developing countries. The results derived from the systematic literature review represents some major flaws in the existing technological & cognitive solutions being used for agriculture means in the developing economies, with special emphasis on the lack of advanced AI techniques that are required for development of robust and precise farming methods. This is due the farmers’ inability to use sustainable technological solutions that are limited by the high cost of available technological tools. Moreover, contrary to other disciplines of science, a human expertise is scare and very costly in the agriculture industry. Hence, there is a need to actively introduce the concept of AI in the agriculture sector by making AI more viable and affordable for the farming community in the developing economies. Besides, there is also a need to create a centralized AI model for the agriculture industry which will integrate AI into a single central system for the entire economy that could be used in various enterprises of the agriculture industry.

ARTIFICIAL INTELLIGENCE (AI) AS SUSTAINABLE SOLUTION FOR THE AGRICULTURE SECTOR: FINDINGS FROM DEVELOPING ECONOMIES

Agricultural production plays an important role both in national and global economies. The efficient and safe methods of sustainable agricultural production is crucial, and the use of information technology is imperative to meet this end. Among the available information technology tools, this study highlights the IT based cognitive solutions supported with the artificial intelligence (AI) algorithm for sustainable solutions in the agriculture sector of developing economies. For this purpose, a systematic review of 87 papers has been conducted in the chosen last 20 years from 2000 to 2019 to identify the major trends, challenges, limitation related to the applicability of AI supported cognitive solutions in the agricultural industry of developing countries. The results derived from the systematic literature review represents some major flaws in the existing technological & cognitive solutions being used for agriculture means in the developing economies, with special emphasis on the lack of advanced AI techniques that are required for development of robust and precise farming methods. This is due the farmers’ inability to use sustainable technological solutions that are limited by the high cost of available technological tools. Moreover, contrary to other disciplines of science, a human expertise is scare and very costly in the agriculture industry. Hence, there is a need to actively introduce the concept of AI in the agriculture sector by making AI more viable and affordable for the farming community in the developing economies. Besides, there is also a need to create a centralized AI model for the agriculture industry which will integrate AI into a single central system for the entire economy that could be used in various enterprises of the agriculture industry.

Optimized Matrix Feature Analysis – Convolutional Neural Network (OMFA-CNN) Model for Potato Leaf Diseases Detection System

One of the most often grown crops is the potato. As a main food, potatoes are prioritised for cultivation worldwide. Because potatoes are such a rich source of vitamins and minerals, we can create a robust system for food security. However, a number of illnesses delay the growth of agriculture and harm potato output. Consequently, early disease identification can offer a better answer for effective crop production. In this research work aim is to classify and detect the potato leave (PL) diseases using OMFA-CNN deep learning model. An optimized matrix feature analysis-CNN deep learning model for PL disease detection is implemented. In the first phase, the PLs features are extracted from the potato leave images using K-means clustering image segmentation method. At the last phase, a new OMFA-CNN model are proposed using CNN to classify virus, and bacterial diseases of PLs, The PL disease dataset consists 2351 images gathered in real-time and from the Kaggle (PlantVillage) dataset. The implemented OMFA-CNN model attained 99.3 % precision and 99 % recall on potato disease detection. The implemented method is also compared with MASK RCNN,SVM and other models and attained significantly high precision and recall

스위트 바질 재배를 위한 가정용 퍼지 제어 수경재배 시스템 개발

학위논문 (석사) -- 서울대학교 대학원 : 농업생명과학대학 바이오시스템·소재학부(바이오시스템공학), 2021. 2. 조성인.토양에서 대기까지의 범위가 넓어지면서 환경 오염이 심화됨에 따라 실내 농업에 대한 수요가 증가하고 관련 연구도 증가하고 있다. 실외 기후 조건에 영향을 받지 않는 안정적인 실내 재배 시스템과 공간을 최대로 활용하는 효율적인 시스템에 대한 연구가 지속적으로 가속화되고 있다. 대부분의 연구는 산업 농업 또는 대규모 생산에 중점을 두는 경향이 있으나 가정을 위한 소규모 재배도 많이 개발되어야 한다. 이 연구는 Raspberry pi 4 및 python을 사용하여 퍼지 논리에 의해 자동으로 제어되는 가정용 수경 재배 시스템을 만들기 위해 설계되었다. 애매한 상황을 해결하고 챔버 내부의 환경 제어를 개선하기 위해 퍼지 로직 제어 (FLC)를 채택했다. FLC의 경우 3 개의 입력 변수와 7 개의 출력 변수가 사용되었다. 입력 변수는 온도, 습도 및 성장 단계 (기간)이고 출력 변수는 팬, 미스트, 히터 1, 히터 2, 그리고 3 개 (적색, 녹색, 청색) LED이며, 6 개의 FLC 퍼지 규칙이 적용되었다. 이 FLC는 각 성장 단계마다 다른 휘발성 화합물과 식물의 맛을 유발하는 다양한 광질과 밀도가 필요하여 세 가지 조명이 각각 세 단계의 재배 기간 동안 작동되도록 설계되었다. 그 결과 제어 시스템 내부 온도는 외부 온도 19.8 ℃에 비해 21 ~ 26 ℃ (평균 21.24 ℃)로 유지되었으며, 내부 습도의 평균값은 외부 습도 16.57 %에 비해 75.58 %로 유지되어 시스템을 통해 바질 재배에 적합한 환경이 조성되었다는 것이 확인되었다. 그러나, 습도가 60 ~ 65 % 수준으로 낮게 유지되면 더욱 적합한 환경이 조성되므로 이 문제를 해결하기 위해 Pearson의 상관 계수를 사용하여 규칙과 멤버십 함수를 분석했다. 팬과 히터, 습도, 미스트와의 상관 관계가 예상보다 낮았기 때문에 문제의 주요 원인은 팬과 관련된 것으로 추정된다. 게다가, 시뮬레이션과 실제 작동 사이의 비교가 수행되었으며 히터의 실제 작동이 시뮬레이션의 영역을 벗어나 부적절하게 이루어졌다는 것을 발견했다. 마지막으로, 광질은 지속 시간에 따른 세 가지 빛 파장 영역 (발아 및 잎의 성장을 위한 청색광, 개화를 위한 적색광, 잎의 캐노피를 통한 생육 저하를 방지하는 녹색광)을 기반으로 FLC에 의해 잘 제어되었다. 추후 RGB 카메라를 사용하는 머신 비전으로 성장 단계를 추정하면 조명 제어가 더 정확해질 것으로 기대된다.As environmental pollution gets more severe demands for indoor farming have been rising with a subsequent increase in studies related to it. Research on stable indoor cultivation systems that cannot be affected by outdoor climate conditions and efficient systems that can maximize production under space constraints is advancing rapidly. However, most studies focus on industrial farming or large-scale production. Small-scale cultivation for households requires equal attention. This study aimed to design household hydroponic systems automatically controlled by fuzzy logic with Raspberry Pi 4 and using the Python programming language. Fuzzy logic control (FLC) was adopted to resolve ambiguity and improve the environmental control of the inside chamber. For the FLC, three input and seven output variables were used. The input variables were temperature, humidity, and growth stage (duration) and the output variables were fan, mist, two heaters (heater1 and heater2), and three RGB LEDs. Six FLC rules were with these variables. The FLC ensured that the three lights operate for three different cultivating periods. Each growth stage required different light quality and density inducing different volatile compounds and flavors of plants. The results showed that inner temperature of the control uses airflow to maintain the temperature at approximately 21 – 26 ℃ (average of 21.24 ℃) compared to the outer temperature of 19.8 ℃. Furthermore, the mean value of inner humidity is 75.58 %, the outer humidity was 16.57 %. However, the controlled humidity should have been maintained at an approximately lower temperature of the level of 60 - 65 %. To address this problem, the rules and membership functions were analyzed by Pearsons correlation coefficients. Because fans correlations with heaters, humidity and mist were lower than expected, it was assumed that the fan made a significant contribution to the problem. Besides, comparison between simulation and actual operation were carried out and it was noticed that heaters actual work was done inappropriately breaking the boundary of simulation. Finally, light quality was controlled by the FLC based on three light regimes upon days of duration; blue light for germination, red light for vegetative growth and green light for flowering stage. The light control will be more accurate if the growth stage is estimated by machine vision with an RGB camera.Abstract ⅲ Table of Contents ⅴ List of Tables ⅷ List of Figures ⅸ Chapter 1. Introduction 1 1.1. Study Background 1 1.2. Review of Literature 4 1.2.1. Urban farming and Urban agriculture 4 1.2.2. Household appliances in agriculture 7 1.2.3. Lighting for cultivation 9 1.2.4. Basil (Ocimum Basilicum L.) 13 1.2.5. Growth stages of Plants 15 1.2.6. Fuzzy Logic Control Systems in Agriculture 18 1.3. Research Purpose and Significance 20 1.3.1. Research Objectives 20 1.3.2. Significance 21 Chapter 2. Materials and Methods 22 2.1. Preliminary Performance Test of Conventional Control System 22 2.1.1. Purpose of Preliminary Test 22 2.1.2. Hardware Design 23 2.1.2.1. Wall-mount and standing type (two ways) 23 2.1.2.2. Modular type 25 2.1.3. Hardware Operating Test With Arduino 26 2.1.3.1. Materials 26 2.1.3.2. Overall system 28 2.1.4. Fuzzy Logic Simulation With MATLAB and Arduino 29 2.2. Experimental Hardware Setup and Configuration 32 2.2.1. Materials 32 2.2.2. Overall Circuit and System Setup 36 2.2.3. Fuzzy Logic Control System 39 Chapter 3. Results and Discussion 48 3.1. Preliminary Test Results 48 3.1.1. Hardware Work 48 3.1.2. Software Work 49 3.1.3. Limitations 50 3.2. Experiment Results 51 3.2.1. Simulation 51 3.2.1.1. Rule View 51 3.2.1.2. Surface View 54 3.2.2 Hardware operations 55 3.2.2.1. Fan 55 3.2.2.2. Mist 55 3.2.2.3. Heaters 56 3.2.2.4. LED 57 3.2.3. Results of Integrated system 58 3.2.3.1. Unprocessed data of temperature and humidity 58 3.2.3.2. Outliers removement 61 3.2.3.2.1. Statistics 61 3.2.3.2.2. Data Visualization 63 3.2.3.3. LED Changes 66 3.2.4. Correlation between Input and Output variables 70 3.2.4.1. Group1 (Temperature, Humidity) 69 3.2.4.2. Comparison between results of simulation and actual operation 75 Chapter 4. Conclusions 80 Bibliography 84 Abstract in Korean 88Maste

Predicción de la producción y rendimiento de frijol, con modelos de redes neuronales artificiales y datos climáticos

The state of Zacatecas ranks first in the production of rainfed beans in Mexico. Due to the economic and food security repercussions, it is important to predict yields, production and harvested area, as well as to know the climatological variables that have the greatest effect on bean cultivation. The objectives of the present work were 1) to develop ANN models for the prediction of the harvested area, yields and production of rainfed beans in the state of Zacatecas, using data on maximum and minimum air temperature, precipitation and evaporation during the period 1988-2019. 2) to determine the input variables that have the greatest influence on bean production and yield through sensitivity analysis. Due to the limited availability of climatic data, the Climatol library of the R statistical package was used to fill in missing data. The results show that the RNA models capture the influence of climate on bean production, with an overall efficiency of 0.89 for Rto and 0.86 for SC. The production was estimated using the outputs, Rto and SC, from RNA models and an R2 =0.80 was obtained. According to the sensitivity analysis, Evaporation of the cycle is the most important variable in predicting yield, while precipitation in August (Pp_Ago) and minimum temperature (Tmin) had a greater influence on production.  El estado de Zacatecas ocupa el primer lugar en la producción de frijol de temporal en México. Debido a las repercusiones económicas y de seguridad alimentaria, es importante la predicción de los rendimientos, producción y superficie cosechada, igualmente, conocer las variables climatológicas que mayor efecto tienen en el cultivo de frijol. Los objetivos del presente trabajo fueron 1) desarrollar modelos de redes neuronales artificiales RNA para la predicción de la superficie cosechada (SC), los rendimientos (Rto) y la producción (P) de frijol de temporal en el estado de Zacatecas, empleando datos de temperatura máxima y mínima del aire, precipitación y evaporación durante el periodo 1988-2019. 2) realizar un análisis de sensibilidad para determinar las variables de entrada que tienen mayor influencia en la producción y rendimiento de frijol. Debido a la limitada disponibilidad de datos climáticos, se usó la librería Climatol del paquete estadístico R, para el llenado de datos faltantes. Los resultados muestran que los modelos de RNA son capaces de detectar la influencia del clima en la producción de frijol. La eficiencia global en los modelos RNA fue de 0.89 para Rto y 0.86 para SC.  La producción se estimó con los modelos de RNA para Rto y SC y se obtuvo un R2 =0.80. De acuerdo al análisis de sensibilidad, la evaporación del ciclo del cultivo (Eva) es la variable más importante en la predicción del rendimiento, mientras que la precipitación de agosto (Pp_Ago) y la temperatura mínima (Tmin) influyeron más en la producción

Efficient evolutionary algorithms for optimal control

If optimal control problems are solved by means of gradient based local search methods, convergence to local solutions is likely. Recently, there has been an increasing interest in the use of global optimisation algorithms to solve optimal control problems, which are expected to have local solutions. Evolutionary Algorithms (EAs) are global optimisation algorithms that have mainly been applied to solve static optimisation problems. Only rarely Evolutionary Algorithms have been used to solve optimal control problems. This may be due to the belief that their computational efficiency is insufficient to solve this type of problems. In addition, the application of Evolutionary Algorithms is a relatively young area of research. As demonstrated in this thesis, Evolutionary Algorithms exist which have significant advantages over other global optimisation methods for optimal control, while their efficiency is comparable.The purpose of this study was to investigate and search for efficient evolutionary algorithms to solve optimal control problems that are expected to have local solutions. These optimal control problems are called multi-modal. An important additional requirement for the practical application of these algorithms is that they preferably should not require any algorithm parameter tuning. Therefore algorithms with less algorithm parameters should be preferred. In addition guidelines for the choice of algorithm parameter values, and the possible development of automatic algorithm parameter adjustment strategies, are important issues.This study revealed that Differential Evolution (DE) algorithms are a class of evolutionary algorithms that do not share several theoretical and practical limitations that other Genetic Algorithms have. As a result they are significantly more efficient than other Genetic Algorithms, such as Breeder Genetic Algorithms (BGA), when applied to multi-modal optimal control problems. Their efficiency is comparable to the efficiency of Iterative Dynamic Programming (IDP), a global optimisation approach specifically designed for optimal control. Moreover the DE algorithms turned out to be significantly less sensitive to problems concerning the selection or tuning of algorithm parameters and the initialisation of the algorithm.Although it is not a DE algorithm, the GENOCOP algorithm is considered to be one of the most efficient genetic algorithms with real-valued individuals and specialized evolutionary operators. This algorithm was the starting point of our research. In Chapter 2 it was applied to some optimal control problems from chemical engineering. These problems were high dimensional, non-linear, multivariable, multi-modal and non-differentiable. Basically with GENOCOP the same solutions were obtained as with Iterative Dynamic Programming. Moreover GENOCOP is more successful in locating the global solution in comparison with other local optimisation algorithms. GENOCOP'S efficiency however is rather poor and the algorithm parameter tuning rather complicated. This motivated us to seek for more efficient evolutionary algorithms.Mathematical arguments found in the literature state that DE algorithms outperform other Evolutionary Algorithms in terms of computational efficiency. Therefore in Chapter 3, DE algorithms, generally used to solve continuous parameter optimisation problems, were used to solve two multi-modal (benchmark) optimal control problems. Also some Breeder Genetic Algorithms (BGA) were applied to solve these problems. The results obtained with these algorithms were compared to one another, and to the results obtained with IDP. The comparison confirmed that DE algorithms stand out in terms of efficiency as compared to the Breeder Genetic algorithms. Moreover, in contrast to the majority of Evolutionary Algorithms, which have many algorithm parameters that need to be selected or tuned, DE has only three algorithm parameters that have to be selected or tuned. These are the population size (µ), the crossover constant (CR) and the differential variation amplification (F). The population size plays a crucial role in solving multi-modal optimal control problems. Selecting a smaller population size enhances the computational efficiency but reduces the probability of finding the global solution. During our investigations we tried to find the best trade-off. One of the most efficient DE algorithms is denoted by DE/best/2/bin . All the investigated DE algorithms solved the two benchmark multi-modal optimal control problems properly and efficiently. The computational efficiency achieved by the DE algorithms in solving the first low multi-modal problem, was comparable to that of IDP. When applied to the second highly multi-modal problem, the computational efficiency of DE was slightly inferior to the one of IDP, after tuning of the algorithm parameters. However, the selection or tuning of the algorithm parameters for IDP is more difficult and more involved.From our investigation the following guidelines were obtained for the selection of the DE algorithm parameters. Take the population size less than or equal to two times the number of variables to be optimised that result from the control parameterisation of the original optimal control problem ( µ ≤ 2n u ). Highly multi-modal optimal control problems require a large value of the differential variation amplification ( F ≥0.9) and a very small or zero value for the crossover constant (0≤ CR ≤0.2). Low multi-modal optimal control problems need a medium value for the differential variation amplification (0.4≤ CR ≤0.6) and a large or medium value for the crossover constant (0.2≤ CR ≤0.5). In contrast to IDP, finding near-optimal values for the algorithm parameters is very simple for DE algorithms.Generally, the DE algorithm parameters are kept constant during the optimization process. A more effective and efficient algorithm may be obtained if they are adjusted on-line. In Chapter 4, a strategy that on-line adjusts the differential variation amplification ( F ) and the crossover constant ( CR ) using a measure of the diversity of the individuals in the population, was proposed. Roughly, the proposed strategy takes large values for F and small values for CR at the beginning of the optimization in order to promote a global search. When the population approaches the solution, F is decreased in order to promote a local search, and the crossover parameter CR is enlarged to increase the speed of convergence. When implemented on the DE algorithm DE/rand/1/bin and applied to the two benchmark multi-modal optimal control problems, the computational efficiency significantly improved and also the probability of locating the global solution.To judge the opportunities and advantages of using Evolutionary Algorithms to solve problems related to optimal control, in Chapter 5 several engineering applications concerning optimal greenhouse cultivation control are considered. In Chapter 5.1 genetic algorithms with binary individuals (Simple Genetic Algorithm) and floating-point representation (GENOCOP) for the individuals are used to estimate some of the parameters of a two-state dynamic model of a lettuce crop, the so-called NICOLET model. This model is intended to predict dry weight and nitrate content of lettuce at harvest time. Parameter estimation problems usually suffer from local minima. This study showed that Evolutionary Algorithms are suitable to calibrate the parameters of a dynamic model. However the required computation time is significant. Partly this is due to the high computational load of a single objective function evaluation, which for parameter optimisation problems involves a system simulation. Even though parameter optimisation is very often performed off-line, thus making computation time perhaps less important, more efficient evolutionary algorithms like DE are to be preferred.In Chapter 5.2 an optimal control problem of nitrate concentration in a lettuce crop was solved by means of two different algorithms. The ACW (Adjustable Control-variation Weight) gradient algorithm, which searches for local solutions, and the DE algorithm DE/best/2/bin that searches for a global solution. The dynamic system is a modified two-state dynamic model of a lettuce crop (NICOLET B3) and the control problem has a fixed final time and control and terminal state constraints. The DE algorithm was extended in order to deal with this.The results showed that this problem probably does not have local solutions and that the control parameterisation required by the DE algorithm causes some difficulties in accurately approximating the continuous solution obtained by the ACW algorithm. On the other hand the computational efficiency of the evolutionary algorithm turned out to be impressive. An almost natural conclusion therefore is to combine a DE algorithm with a gradient algorithm.In Chapter 5.3 the combination of a DE algorithm and a first order gradient algorithm is used to solve an optimal control problem. The DE algorithm is used to approximate the global solution sufficiently close after which the gradient algorithm can converge to it efficiently. This approach was successfully tried on the optimal control of nitrate in lettuce, which unfortunately in this case, seems to have no local solutions. Still the feasibility of this approach, which is important for all types of optimal control problems of which it is unknown a-priori whether they have local solutions, was clearly demonstrated.Finally, in Chapter six this thesis ends with an overall discussion, conclusions and suggestions for future research

O impacte da inteligência artificial na sustentabilidade ambiental : uma agricultura sustentável

Mestrado em Gestão de Sistemas de InformaçãoPara lidar com o aumento da procura e de várias tendências disruptivas com sucesso, a indústria agrícola precisará superar os desafios de uma implementação de conectividade avançada. O presente estudo tem assim as seguintes questões de investigação: 1. Como pode a IA contribuir para uma gestão sustentável da agricultura? 2. Como introduzir o uso de IA na agricultura de uma forma continuada? E tem como objetivos de investigação: 1.Analisar as principais características das ferramentas baseadas em IA que permitem uma gestão sustentável da agricultura; 2.Apresentar os principais fatores limitadores para a adoção de IA na agricultura; 3.Compreender até que ponto o volume de dados é um desafio a nível do bom desempenho das ferramentas de IA; e 4.Relacionar o papel das universidades e empresas e a adoção de IA na agricultura. O método utilizado foi a realização de entrevistas semiestruturadas a peritos nas áreas da agricultura sustentável e das novas tecnologias emergentes de IA. Este estudo conclui que a IA contribui para a sustentabilidade da agricultura a três níveis: ambiental, económico e a nível dos dados. Porém alguns desafios dificultam esta adoção, nomeadamente: a dimensão territorial; a capacidade financeira dos agricultores; a idade mais avançada dos agricultores bem como a sua mentalidade cética e adversa a este tipo de tecnologias ou ainda o excesso e diversidade de dados existentes. Também, as empresas e universidades têm um peso importante na medida em que contribuem para que haja uma disseminação maior da informação através dos casos de estudo e da experimentação.To successfully cope with rising demand and several disruptive trends, the agricultural industry will need to overcome the challenges of an advanced connectivity implementation. The present study therefore has the following research questions: 1. How can AI contribute to sustainable management of agriculture? 2. How to introduce the use of AI in agriculture in a continuous way? And as research objectives: 1. Analyze the main characteristics of AI-based tools that allow sustainable management of agriculture; 2. Present the main limiting factors for the adoption of AI in agriculture; 3. Understand to what extent the volume of data is a challenge in terms of the good performance of AI tools; and 4. Relate the role of universities and companies and the adoption of AI in agriculture. The method used was the case study, using semi-structured interviews with experts in the areas of sustainable agriculture and the new emerging AI technologies. From the study, it was possible to conclude that AI contributes to the sustainability of agriculture at three levels: environmental, economic and at the data level. However, some challenges hinder this adoption, namely: the territorial dimension, the financial capacity of farmers; the older age of farmers as well as their skeptical and adverse mindset to this type of technologies; the excess and diversity of existing data, among others addressed in the study. Also, companies and universities have an important weight in that they contribute to a greater dissemination of information through case studies and experimentation.info:eu-repo/semantics/publishedVersio

Predição para o uso da inteligência artificial no agronegócio na Caatinga

A ciência e a tecnologia, em diferentes formas, sempre exerceram um papel expressivo na solução de problemas, sendo usadas para o desenvolvimento de estratégias, produtos, métodos e ferramentas. Os avanços em ciência e tecnologia têm se mostrado promissores no intuito de aprimorar setores como o agronegócio. E essa visão tem sido justificada pelo constante avanço de dispositivos tecnológicos projetados para apresentar soluções aos problemas agrícolas. Sendo assim, este estudo tem por objetivo analisar o processo de inovação no contexto da Inteligência Artificial (IA), desde a produção do conhecimento científico até a fase de predição dessa tecnologia no agronegócio na Caatinga. Do ponto de vista dos aspectos metodológicos a pesquisa é classificada como exploratória, uma vez que essa investigação leva em consideração uma área na qual há pouco conhecimento acumulado e sistematizado. Em relação à técnica de pesquisa, é caracterizada como estudo de caso. Os resultados da aplicação dos métodos da IA no agronegócio no contexto geral apresentam diferentes abordagens como o uso de Visão de Máquina por meio de Sistema Agrícola Virtual, SVM e ELM na detecção precoce do patógeno de pragas e doenças; FIS e MLP para a exploração de culturas; propagação reversa para monitoramento dos limites da fazenda; ANN e MFNN para análise de estruturas de irrigação; e Árvore da Decisão e TDNN para a vigilância do rebanho. Com os dispositivos integrados no sistema de produção agrícola os sistemas das fazendas passam a oferecer recomendações e insights mais ricos para a tomada de decisão e melhoria da cadeia de suprimentos agrícola. Em relação ao levantamento das tecnologias atuais no agronegócio na Caatinga, o contexto local apresenta abordagens bem distintas, desde a utilização de técnicas de convivência com o semiárido como os métodos de manejo do solo, aproveitamento da água da chuva e preparo de ração animal. Já a análise do uso das tecnologias, o enfoco está na viabilidade da produção, diversificação e manejo da colheita em polos integrados de grande desenvolvimento tecnológico em polos de cultivo e manejo de culturas irrigadas. A perspectiva da adoção e o desenvolvimento de IA no agronegócio na Caatinga ainda se encontram em fase inicial, com os agentes buscando nas pesquisas, conhecer as oportunidades dessa tecnologia frente aos negócios no setor agrícola. Na Caatinga, os estudos ainda são reduzidos, mas já há exemplos como rastreabilidade de carne, predição da produtividade da palma forrageira, delineamento de zonas de manejo ou mesmo na estimativa da evapotranspiração de referência. Contudo, há etapas que devem ser superadas até a integração da IA como a habilidade de entender e manusear as ferramentas com IA e a integração dos sistemas dentro da cadeia de suprimentos. Já os resultados do levantamento sistemático apresentam ações como modelagem e previsão do fluxo de água; evapotranspiração; variabilidade, avaliação de terra; previsão de época ótima de semeadura e seleção de cultivares. De modo que, os achados apresentam os diferentes usos da IA, com iniciativas de sustentabilidade habilitadas por mudanças no sistema agrícola atual.Science and technology, in different forms, have always played an expressive role in problem solving, being used for the development of strategies, products, methods and tools. Advances in science and technology have shown promise in order to improve sectors such as agribusiness. And this vision has been justified by the constant advancement of technological devices designed to present solutions to agricultural problems. Therefore, this study aims to analyze the innovation process in the context of artificial intelligence, from the production of scientific knowledge to the prediction phase of this technology in agribusiness in the Caatinga. From the point of view of methodological aspects, the research is classified as exploratory, since this investigation takes into account an area in which there is little accumulated and systematized knowledge. Regarding the research technique, it is characterized as a case study. The results of the application of AI methods in agribusiness in the general context present different approaches such as the use of Machine Vision through Virtual Agricultural System, SVM and ELM in the early detection of the pathogen of pests and diseases; FIS and MLP for the exploitation of cultures; reverse propagation for monitoring farm boundaries; ANN and MFNN for analysis of irrigation structures; and Decision Tree and TDNN for herd surveillance. With the devices integrated into the agricultural production system. farm systems now offer richer recommendations and insights for decision making and agricultural supply chain improvement. Regarding the survey of current technologies in agribusiness in the Caatinga, the local context presents very different approaches, from the use of technologies of coexistence with the semi-arid region or social techniques such as methods of soil management, use of rainwater and preparation of feed animal. Even the use of technologies themselves aimed at the viability of production, diversification and management of the harvest in integrated poles of great technological development in poles of cultivation and management of irrigated cultures. The perspective of the adoption and development of AI in agribusiness in the Caatinga is still at an early stage, with agents seeking, in research, to know the opportunities of this technology in relation to business in the agricultural sector. In the Caatinga, studies are still very limited, but there are already examples such as meat traceability, prediction of forage cactus productivity, delineation of management zones or even in the estimation of reference evapotranspiration. However, there are steps that must be overcome until the integration of AI such as the ability to understand and handle the tools with AI and the integration of systems within the supply chain. On the other hand, the results of the systematic survey present actions such as modeling and forecasting the water flow; evapotranspiration; variability, land assessment; prediction of optimal sowing time and selection of cultivars. So, the findings present the different uses of AI, with sustainability initiatives enabled by changes in the current agricultural system

Ciência de dados na era da agricultura digital: anais.

Estes anais contêm o texto completo dos trabalhos apresentados no XI Congresso Brasileiro de Agroinformática (SBIAgro 2017), o qual foi promovido pela Embrapa Informática Agropecuária e pela Faculdade de Engenharia Agrícola, Instituto de Computação e pelo Centro de Pesquisas Meteorológicas e Climáticas Aplicadas à Agricultura da Universidade Estadual de Campinas (Unicamp). Esta edição do evento foi realizada no Centro de Convenções e na Casa do Lago da Unicamp, localizados na cidade de Campinas (SP). O propósito do evento foi o de reunir pesquisadores, professores, estudantes, empresários e funcionários de empresas para discutir o tema da informática aplicada à agricultura, além de promover um ambiente propício para o surgimento de novos relacionamentos, projetos e negócios.Organizadores: Jayme Garcia Arnal Barbedo, Maria Fernanda Moura, Luciana Alvim Santos Romani, Thiago Teixeira Santos, Débora Pignatari Drucker. SBIAgro 2017