Diagnostic method and device for evaluating and forecasting the technical condition of farm machinery in operation

The paper discusses methods and ways to diagnose the technical condition of agricultural machines and harvesters, existing practices, and approaches to get reliable data on the current health of the machinery used. The device for assessing and predicting machines’ technical condition includes software and technical means developed with virtual technologies to measure diagnostic parameters of the machinery. The main device elements are digital sensors with physical modifiers (pressure, temperature, medium composition and motion sensors, a-d converters with signal amplifiers), software to configure data gathering, and output to conduct analyses and produce recommendations. The core of the present approach is the technology of virtual prediction of breakdowns by changes in the technical condition parameters. It is based on modular devices, software with an interface that collects and processes data and provides a complete set of failure diagnostics and forecasting. The given method based on a device operating in the information and communication network increases farm machinery’s performance. Furthermore, it reduces operating costs due to the prevention of expensive breakdowns, individual forecasting, and scheduled maintenance of machines in operation. The approach under consideration was applied in the laboratory of digital engineering technologies of the Bashkir State Agrarian University Republic of Bashkortostan of the Russian Federation. The given work is aimed to boost the efficiency of the farm machinery diagnostics and maintenance system by applying a virtual breakdown prediction technology to conduct an automated evaluation, registration, and analysis of a machine’s condition. It can be achieved by developing software and technical means to register data and their structure systematization.

Penetration of farm mechanization services through FPOs: rural dynamics-based business modelling

Farm mechanisation although one among the essential input to raise the agriculture productivity, but individual owning of agricultural machinery by resource constrained small and marginal farmers who constitute around 85% of operated land holdings in India is uneconomical. Hence, innovative arrangements such as custom hiring centres’ (CHCs) are being encouraged through farm aggregation models like cooperative farming, Joint Liability Groups (JLG), Farmer Producer Organizations (FPOs) to get access to farm machinery services at affordable prices and promote mechanization of operations on small farms. With this background, the present study is taken upto assess the feasibility for the establishment of FPO owned and operated model custom hiring centre (CHC) in Nimmanapalle mandal of chittoor district of Andhra Pradesh state and formulate suitable business strategies for ensuring viability of the unit. The sample size of the study was 120 farmers. The major crops grown in the study area are tomato, paddy and groundnut and the market potential for farm machinery is estimated at Rs. 269.73 lakhs. The SWOT analysis conducted indicated the opportunity for establishment of CHC due to inadequate farm machinery services, labour shortages and farmers habituated to hiring services. The financial assessment for the proposed unit over a five year period showed that the unit is worth investing as reflected by positive NPV of 6.56 lakhs at 12% discount rate, BCR of 1.05 and IRR of 17.27%. The debt service coverage ratios of greater than two from second year onwards and annual increase of positive cash accruals signifies the unit strength in meeting the debt obligations. The unit if established shall have long term social benefits that includes increase in input use efficiency of farm resources due to timeliness of operations, productivity, yields, income levels in addition to creation of employment in non-farm sector.

Finite automata model for leaf disease classification

In this modern era, the detection of plant disease plays a vital role in the sustainability of agricultural ecosystem. Today, India being second in farming, well-timed information related to crop is still questioning. Indian Government's farmer portal is available for pesticides, fertilisers, and farm machinery. To alleviate this problem, the paper describes a model to validate the leaf image, predicting leaf disease and notifying the farmer in an effective way on the harvest failure to stabilise farming income. For specific consideration on the validation, a data set library with predefined, uniformly scaled, regular image patterns of leaf disease, is maintained. The research suggests that farmers utilising the model can predict the breakout of leaf disease predominantly acquiring 100% yield.

laboratory test for different designs of feeding mechanism of subsoil manuring machine

"Laboratory experiment was carried out at the Department of farm Machinery / college of Agriculture, in order to study the effect of the types of feeding arms (straight arms, Lshaped arm, and arched arms) and tractor engine speed (600, 1200, 1800) rpm on some performance properties included manure discharge rate, manure quantity per hectare and machine productivity. All tests were conducted according to the complete randomized bolck design with three replicates. The results showed an increase in fertilizer discharge rate, manure quantity per hectare and productivity of the machine while increasing engine speed. The feeding mechanism with L-shaped arm was superior to other designs which gave the highest values for the manure discharge rate, the amount of manure per hectare and the productivity of the machine as compared to the feeding mechanism with straight and arched arms"

Comparison of tillage costs among eight paddy farm regions in East Kalimantan, Indonesia

Background: Tillage is done to prepare land for wetland paddy farming, and it is commonly done by hand tractor. The purposes of this study were to identify the levels of ownership of hand tractor by paddy farmers, to describe the rental of hand tractor in rural areas, to calculate and compare the tillage costs on eight paddy farm regions, and to understand the utilization of farm machinery for paddy farming in East Kalimantan, Indonesia. Methods: The study areas were Subcities/Subregencies of North Bontang, South Bontang, Muara Muntai, Loa Janan, Tenggarong Seberang, Waru, Penajam, and Babulu. Data collection was done by interviewing 380 respondents. Analysis of data used descriptive statistics, the Chi Square One Sample test, and One Way Anova. Results: The number of hand tractor renters (87.37%) in East Kalimantan 2014 was bigger than that of hand tractor owners (12.63%). The tillage costs in Tenggarong Seberang, Loa Janan, and Muara Muntai in 2014 were USD48.56 ha-1, USD52.03 ha-1, and USD48.56 ha-1, respectively. Tillage costs were the same in Babulu, Penajam, Waru, South Bontang, and North Bontang (USD69.37 ha-1 in each regency). Conclusions: There are very significant differences the number of hand tractor owners, the number of hand tractor renters, and the tillage costs among some regions in East Kalimantan, Indonesia. Farm machinery is needed in development of paddy farming.

Agricultural mechanization in Ethiopia: enabling policy, suppliers and inter-regional heterogeneity

Background: Agriculture in Ethiopia heavily relies on traditional farm power sources and is characterized by the lowest access to farm machinery in contrast to other places in SSA. The study analyzed the factors and gaps in the delivery of mechanization inputs through the qualitative survey of the supply chain actors. Furthermore, the study involved the crossectional survey of the producer households in the major crop production areas of Oromia, SNNPR, Amhara, and Tigray regions. Results: The recent policy amendment resulted in a reduction in the price of farm machinery while it excludes spare parts in isolation and raw materials for manufacturing. The counter influence of the simultaneous depreciation of the exchange rate has undermined the likely impacts of the tax benefits. The major deterring issues in the machinery supply system include the high shortage of foreign currency, the upsurging price of spare parts, and shortage of trained machinery operators. A formal survey tells that a significant proportion of the areas have no access to mechanization while in some parts of the country, utilization of the combine harvester reached full coverage and a large proportion of the farmers use tractor power.Conclusions: Maintaining a healthy business competition among imports and the manufacturing sector remained an important policy gap. The mechanization hiring costs, service transactions, and coordination have shown significant variability across the crop potential production areas. Resolving of the supply side encounters, development of the hiring service market, and reduction of transaction cost are the key areas of interventions in the mechanization supply and demand systems.

Towards the Development and Verification of a 3D-Based Advanced Optimized Farm Machinery Trajectory Algorithm

Efforts related to minimizing the environmental burden caused by agricultural activities and increasing economic efficiency are key contemporary drivers in the precision agriculture domain. Controlled Traffic Farming (CTF) techniques are being applied against soil compaction creation, using the on-line optimization of trajectory planning for soil-sensitive field operations. The research presented in this paper aims at a proof-of-concept solution with respect to optimizing farm machinery trajectories in order to minimize the environmental burden and increase economic efficiency. As such, it further advances existing CTF solutions by including (1) efficient plot divisions in 3D, (2) the optimization of entry and exit points of both plot and plot segments, (3) the employment of more machines in parallel and (4) obstacles in a farm machinery trajectory. The developed algorithm is expressed in terms of unified modeling language (UML) activity diagrams as well as pseudo-code. Results were visualized in 2D and 3D to demonstrate terrain impact. Verifications were conducted at a fully operational commercial farm (Rostěnice, the Czech Republic) against second-by-second sensor measurements of real farm machinery trajectories.

Classification of Agriculture Farm Machinery Using Machine Learning and Internet of Things

In this paper, we apply the multi-class supervised machine learning techniques for classifying the agriculture farm machinery. The classification of farm machinery is important when performing the automatic authentication of field activity in a remote setup. In the absence of a sound machine recognition system, there is every possibility of a fraudulent activity taking place. To address this need, we classify the machinery using five machine learning techniques—K-Nearest Neighbor (KNN), Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF) and Gradient Boosting (GB). For training of the model, we use the vibration and tilt of machinery. The vibration and tilt of machinery are recorded using the accelerometer and gyroscope sensors, respectively. The machinery included the leveler, rotavator and cultivator. The preliminary analysis on the collected data revealed that the farm machinery (when in operation) showed big variations in vibration and tilt, but observed similar means. Additionally, the accuracies of vibration-based and tilt-based classifications of farm machinery show good accuracy when used alone (with vibration showing slightly better numbers than the tilt). However, the accuracies improve further when both (the tilt and vibration) are used together. Furthermore, all five machine learning algorithms used for classification have an accuracy of more than 82%, but random forest was the best performing. The gradient boosting and random forest show slight over-fitting (about 9%), but both algorithms produce high testing accuracy. In terms of execution time, the decision tree takes the least time to train, while the gradient boosting takes the most time.